Description:FIELD OF THE INVENTION
[0001] The present invention relates to an image processing method and system, and more particular related to a method and a device for predicting a skin disorder based on processing images of the skin.

BACKGROUND OF THE INVENTION
[0002] Vitiligo is a chronic dermatological disorder characterized by progressive skin depigmentation resulting from the loss of melanocytes. This autoimmune-mediated condition affects individuals of diverse demographic backgrounds, manifesting as distinct areas of depigmentation. The pathophysiology of vitiligo involves a complex interplay of genetic predisposition, immunological dysregulation, and environmental factors. The disorder also carries significant psychological implications; individuals often experience heightened self-consciousness due to the visibility of depigmented lesions, social stigma, and reduced self-esteem.
[0003] Timely identification and accurate characterization of vitiligo are essential for prompt therapeutic interventions and alleviating the psychological burden on patients. Tailored therapeutic strategies employing topical medications, advanced phototherapy, and surgical modalities can be offered with an early diagnosis. Vitiligo serves as a clinical indicator of possible underlying autoimmune causes, warranting a comprehensive medical examination. Given its outwardly conspicuous nature and its profound impact on patients' mental health, vitiligo care must be comprehensive and multidisciplinary. The development of dermatologic therapeutics and the advancement of compassionate care practices in vitiligo require an intimate understanding of the convergence of its physiological symptoms and psychological context.
[0004] The timely detection and precise characterization are key because they let clinicians prescribe the correct therapeutic interventions at the earliest stage, improving the potential for a successful outcome and reducing the psychological strain that can be associated with the condition. Indeed, early diagnosis can enable treatment strategies that are tailored to the patient with the selection of topical drugs, more advanced phototherapy, and even surgical interventions. In some, the clinical presentation of vitiligo may be a signal of underlying autoimmune reasons that are often ultimately diagnosed only with a complete medical assessment.
[0005] A precise and uniform vitiligo grading system is needed. Hence, the proposed method attempts to address this critical requirement. This is necessary to go beyond the subjectivity and errors associated with various existing techniques. The proposed method aims to substantially improve the accuracy of vitiligo lesion evaluation. It is hypothesized that its state-of-the-art methodology will yield a much more objective and reliable assessment of the extent, severity, and distribution of vitiligo, compared to current scoring schemes.

SUMMARY OF THE INVENTION
[0006] In one aspect of the present invention, a method for predicting a skin disorder is disclosed. The method includes obtaining a plurality of data associated with a skin disorder, and classifying the plurality of data into at least one skin type distribution category. Further, the method includes preprocessing the at least one skin type distribution category by performing a segmentation of at least one vitiligo lesion from a background analysis for the at least one skin type distribution category. Further, the method includes predicting a skin disorder by delineating at least one affected area for the at least one skin type distribution category after performing the segmentation of at least one vitiligo lesion from a background analysis.
[0007] In an embodiment, predicting the skin disorder includes applying an Otsu’s technique to an intensity histogram of a normal skin image to obtain an optimal threshold, separating a vitiligo lesion from the normal skin image using the optimal threshold, wherein the optimal threshold isolates at least one depigmented patch from the normal skin image; and categorizing pixel intensities into distinct classes, which represented different levels of a vitiligo lesion severity to predict the skin disorder.
[0008] In an embodiment, separating the vitiligo lesion from the normal skin image using the optimal threshold includes generating a binary mask for the image, computing a depigmentation area, performing contour analysis in the binary mask, computing a bounding rectangle for each contour, assigning an unique identifier to each lesion, computing a convex hull for each contour, analyzing a convexity defect, and generating output comprising the depigmentation area, the contour, the bounding rectangles, and the defect.
[0009] In an embodiment, the binary mask is used to compute a quantitative feature comprising a lesion area, a perimeter, and texture characteristics.
[0010] In an embodiment, the plurality of data comprises a skin type trends and age distribution among users.
[0011] In an embodiment, the plurality of data is classified into at least one skin type distribution category based on pigmentation, sensitivity and susceptibility to or protection against different skin condition.
[0012] In an embodiment, preprocessing the at least one skin type distribution category includes performing an image processing on the at least one skin type distribution category, dividing the image into non-overlapping tiles after performing the image processing, computing a cumulative distribution function (CDF) for each tile, clipping a histogram of each tile to limit a contrast enhancement on the image, normalizing the clipped histogram to obtain an equalized histogram, interpolating the equalized histogram to remove artificial discontinuities, and applying a Gaussian blur on the equalized histogram.
[0013] In another aspect of the present invention, a user device for predicting a skin disorder is disclosed. The user device includes a processor, a memory and a skin disorder prediction controller. The skin disorder prediction controller is coupled with the processor and the memory. The skin disorder prediction controller is configured to obtain a plurality of data. Further, the skin disorder prediction controller is configured to classify the plurality of data into at least one skin type distribution category. Further, the skin disorder prediction controller is configured to preprocess the at least one skin type distribution category. Further, the skin disorder prediction controller is configured to perform a segmentation of at least one vitiligo lesion from a background analysis for the at least one skin type distribution category. Further, the skin disorder prediction controller is configured to predict a skin disorder by delineating at least one affected area for the at least one skin type distribution category after performing the segmentation of at least one vitiligo lesion from a background analysis.
[0014] In another aspect of the present invention, non-transitory computer-readable medium having stored thereon computer-readable instructions that, when executed by a processor, cause the processor to: obtain a plurality of data; classify the plurality of data into at least one skin type distribution category; preprocess the at least one skin type distribution category; perform a segmentation of at least one vitiligo lesion from a background analysis for the at least one skin type distribution category; and predict a skin disorder by delineating at least one affected area for the at least one skin type distribution category after performing the segmentation of at least one vitiligo lesion from a background analysis.
[0015] Other features and aspects of this invention will be apparent from the following description and the accompanying drawings. The features and advantages described in this summary and in the following detailed description are not all-inclusive, and particularly, many additional features and advantages will be apparent to one of ordinary skill in the relevant art, in view of the drawings, specification, and claims hereof. Moreover, it should be noted that the language used in the specification has been principally selected for readability and instructional purposes and may not have been selected to delineate or circumscribe the inventive subject matter, resort to the claims being necessary to determine such inventive subject matter.

BRIEF DESCRIPTION OF THE DRAWINGS
[0016] The accompanying drawings, which are incorporated herein, and constitute a part of this disclosure, illustrate exemplary embodiments of the disclosed methods and systems in which like reference numerals refer to the same parts throughout the different drawings. Components in the drawings are not necessarily to scale, emphasis instead being placed upon clearly illustrating the principles of the present disclosure. Some drawings may indicate the components using block diagrams and may not represent the internal circuitry of each component. It will be appreciated by those skilled in the art that disclosure of such drawings includes disclosure of electrical components, electronic components or circuitry commonly used to implement such components.
[0017] FIG. 1 is an exemplary block diagram of a user device for predicting a skin disorder, according to various embodiments of the present disclosure.
[0018] FIG. 2 is an example flow diagram illustrating a method for predicting the skin disorder, according to various embodiments of the present disclosure.
[0019] FIG. 3a to FIG. 3c are example illustrations of an original image versus grayscale image versus CLAHE enhanced image.
[0020] FIG. 4a to FIG. 4c are example illustrations of the original image versus RGB image versus Binary masked image.
[0021] FIG. 5 is an example illustration of a bounding rectangle generation.
[0022] FIG. 6 is example illustration of a component numbering for a depigmented area.
[0023] FIG. 7a to FIG. 7c and FIG. 8a to FIG. 8c are example illustrations of segmented outputs of a VEIDI multifactorial scoring framework.
[0024] FIG. 9a to FIG. 9d, FIG. 10a to FIG. 10d, and FIG. 11a to FIG. 11d are example illustrations of segmented outputs of the VEIDI multifactorial scoring framework.
[0025] FIG. 12 is an exemplary block diagram of computing system for predicting the skin disorder, according to various embodiments of the present disclosure.
[0026] Further, skilled artisans will appreciate that elements in the drawings are illustrated for simplicity and may not have necessarily been drawn to scale. For example, the flow charts illustrate the method in terms of the most prominent steps involved to help to improve understanding of aspects of the present invention. Furthermore, in terms of the construction of the device, one or more components of the device may have been represented in the drawings by conventional symbols, and the drawings may show only those specific details that are pertinent to understanding the embodiments of the present invention so as not to obscure the drawings with details that will be readily apparent to those of ordinary skill in the art having benefit of the description herein.
[0027] The foregoing shall be more apparent from the following detailed description of the invention.

DETAILED DESCRIPTION OF THE INVENTION
[0028] Some embodiments of the present disclosure, illustrating all its features, will now be discussed in detail. It must also be noted that as used herein and in the appended claims, the singular forms "a", "an" and "the" include plural references unless the context clearly dictates otherwise.
[0029] Various modifications to the embodiment will be readily apparent to those skilled in the art and the generic principles herein may be applied to other embodiments. However, one of ordinary skill in the art will readily recognize that the present disclosure including the definitions listed here below are not intended to be limited to the embodiments illustrated but is to be accorded the widest scope consistent with the principles and features described herein.
[0030] A person of ordinary skill in the art will readily ascertain that the illustrated steps detailed in the figures and here below are set out to explain the exemplary embodiments shown, and it should be anticipated that ongoing technological development will change the manner in which particular functions are performed. These examples are presented herein for purposes of illustration, and not limitation. Further, the boundaries of the functional building blocks have been arbitrarily defined herein for the convenience of the description. Alternative boundaries can be defined so long as the specified functions and relationships thereof are appropriately performed. Alternatives (including equivalents, extensions, variations, deviations, etc., of those described herein) will be apparent to persons skilled in the relevant art(s) based on the teachings contained herein. Such alternatives fall within the scope and spirit of the disclosed embodiments.
[0031] Before discussing example, embodiments in more detail, it is to be noted that the drawings are to be regarded as being schematic representations and elements that are not necessarily shown to scale. Rather, the various elements are represented such that their function and general purpose becomes apparent to a person skilled in the art. Any connection or coupling between functional blocks, devices, components, or other physical or functional units shown in the drawings or described herein may also be implemented by an indirect connection or coupling. A coupling between components may also be established over a wireless connection. Functional blocks may be implemented in hardware, firmware, software or a combination thereof.
[0032] Further, the flowcharts provided herein, describe the operations as sequential processes. Many of the operations may be performed in parallel, concurrently or simultaneously. In addition, the order of operations maybe re-arranged. The processes may be terminated when their operations are completed but may also have additional steps not included in the figured. It should be noted, that in some alternative implementations, the functions/acts/ steps noted may occur out of the order noted in the figured. For example, two figures shown in succession may, in fact, be executed substantially concurrently, or may sometimes be executed in the reverse order, depending upon the functionality/acts involved.
[0033] Further, the terms first, second etc… may be used herein to describe various elements, components, regions, layers and/or sections, it should be understood that these elements, components, regions, layers and/or sections should not be limited by these terms. These terms are used only to distinguish one element, component, region, layer or section from another region, layer, or a section. Thus, a first element, component, region layer, or section discussed below could be termed a second element, component, region, layer, or section without departing form the scope of the example embodiments.
[0034] Spatial and functional relationships between elements (for example, between modules) are described using various terms, including “connected,” “engaged,” “interfaced,” and “coupled.” Unless explicitly described as being “direct,” when a relationship between first and second elements is described in the description below, that relationship encompasses a direct relationship where no other intervening elements are present between the first and second elements, and also an indirect relationship where one or more intervening elements are present (either spatially or functionally) between the first and second elements. In contrast, when an element is referred to as being "directly” connected, engaged, interfaced, or coupled to another element, there are no intervening elements present. Other words used to describe the relationship between elements should be interpreted in a like fashion (e.g., "between," versus "directly between," "adjacent," versus "directly adjacent," etc.).
[0035] The terminology used herein is for the purpose of describing particular example embodiments only and is not intended to be limiting. Unless otherwise defined, all terms (including technical and scientific terms) used herein have the same meaning as commonly understood by one of ordinary skill in the art to which example embodiments belong. It will be further understood that terms, e.g., those defined in commonly used dictionaries, should be interpreted as having a meaning that is consistent with their meaning in the context of the relevant art and will not be interpreted in an idealized or overly formal sense unless expressly so defined herein.
[0036] As used herein, the singular forms “a,” “an,” and “the,” are intended to include the plural forms as well, unless the context clearly indicates otherwise. As used herein, the terms “and/or” and “at least one of” include any and all combinations of one or more of the associated listed items. It will be further understood that the terms “comprises,” “comprising,” “includes,” and/or “including,” when used herein, specify the presence of stated features, integers, steps, operations, elements, and/or components, but do not preclude the presence or addition of one or more other features, integers, steps, operations, elements, components, and/or groups thereof.
[0037] Unless specifically stated otherwise, or as is apparent from the description, terms such as “processing” or “computing” or “calculating” or “determining” of “displaying” or the like, refer to the action and processes of a computer system, or similar electronic computing device/hardware, that manipulates and transforms data represented as physical, electronic quantities within the computer system's registers and memories into other data similarly represented as physical quantities within the computer system memories or registers or other such information storage, transmission or display devices.
[0038] Various embodiments of the invention provide a method for predicting a skin disorder. The method includes obtaining a plurality of data. Further, the method includes classifying the plurality of data into at least one skin type distribution category. Further, the method includes preprocessing the at least one skin type distribution category. Further, the method includes performing a segmentation of at least one vitiligo lesion from a background analysis for the at least one skin type distribution category. Further, the method includes predicting a skin disorder by delineating at least one affected area for the at least one skin type distribution category after performing the segmentation of at least one vitiligo lesion from a background analysis.
[0039] FIG. 1 illustrates an exemplary block diagram of a user device (100) predicting a skin disorder, according to various embodiments of the present disclosure. The user device (100) may include, but are not limited to, a handheld wireless communication device (e.g., a mobile phone, a smart phone, a phablet device, and so on), a wearable computer device (e.g., a head-mounted display computer device, a head-mounted camera device, a wristwatch computer device, and so on), a laptop computer, a tablet computer, or another type of portable computer, a and/or any other type of computer device with wireless communication or VoIP capabilities. In an embodiment, the user device (100) may include, but are not limited to, any electrical, electronic, electro-mechanical or an equipment or a combination of one or more of the above devices such as virtual reality (VR) devices, augmented reality (AR) devices, laptop, a general-purpose computer, desktop, personal digital assistant, tablet computer, mainframe computer, or any other computing device, wherein the computing device may include one or more in-built or externally coupled accessories including, but not limited to, a visual aid device such as camera, audio aid, a microphone, a keyboard, input devices for receiving input from a user such as touch pad, touch enabled screen, electronic pen and the like. It may be appreciated that the user device (100) may not be restricted to the mentioned devices and various other devices may be used.
[0040] In an embodiment, the user device (100) includes a processor (102), a memory (104), a display (106), and a skin disorder prediction controller (108). The processor (102) may be implemented as one or more microprocessors, microcomputers, microcontrollers, digital signal processors, central processing units, state machines, logic circuitries, single board computers, and/or any devices that manipulate signals based on operational instructions. As per the illustrated embodiment, the user device (100) includes one processor. However, it is to be noted that the user device (100) may include multiple processors as per the requirement and without deviating from the scope of the present disclosure. The processor (102) is coupled with the memory (104), the display (106), and the skin disorder prediction controller (108).
[0041] Information related to a request may be provided or stored in the memory (104). Among other capabilities, the processor (102) is configured to fetch and execute computer-readable instructions stored in the memory (104). The memory (104) may be configured to store one or more computer-readable instructions or routines in a non-transitory computer-readable storage medium, which may be fetched and executed to create or share data packets over a network service. The memory (104) may include any non-transitory storage device including, for example, volatile memory such as RAM, or non-volatile memory such as disk memory, EPROMs, FLASH memory, unalterable memory, and the like. The memory (104) may comprise any non-transitory storage device including, for example, volatile memory such as Random-Access Memory (RAM), or non-volatile memory such as Electrically Erasable Programmable Read-only Memory (EPROM), flash memory, and the like.
[0042] The display (208) is implemented using Liquid Crystal Display (LCD) display technology, Organic Light-Emitting Diode (OLED) display technology, and/or other types of conventional display technology. The display (208) may be integrated within the user device (100) or connected externally.
[0043] The skin disorder prediction controller (108) obtains a plurality of data and classifies the plurality of data into at least one skin type distribution category. Further, the skin disorder prediction controller (108) preprocesses the at least one skin type distribution category. Further, the skin disorder prediction controller (108) performs the segmentation of at least one vitiligo lesion from a background analysis for the at least one skin type distribution category. Further, the skin disorder prediction controller (108) predicts a skin disorder by delineating at least one affected area for the at least one skin type distribution category after performing the segmentation of at least one vitiligo lesion from a background analysis.
[0044] In an embodiment, the skin disorder prediction controller (108) applies an Otsu’s technique to an intensity histogram of a normal skin image to obtain an optimal threshold. Further, the skin disorder prediction controller (108) separates a vitiligo lesion from the normal skin image using the optimal threshold, wherein the optimal threshold isolates at least one depigmented patch from the normal skin image. In an embodiment, the vitiligo lesion is separated from the normal skin image by generating a binary mask for the image, computing a depigmentation area, performing a contour analysis in the binary mask, computing the bounding rectangle for each contour, assigning a unique identifier to each lesion, computing a convex hull for each contour, analyzing a convexity defect, and generating output comprising the depigmentation area, the contour, the bounding rectangles, and the defect.
[0045] Further, the skin disorder prediction controller (108) categorizes pixel intensities into distinct classes, which represented different levels of a vitiligo lesion severity to predict the skin disorder.
[0046] By using the skin disorder prediction controller (108), the plurality of data is collected in real-time to provide snapshots of trends in skin types and age distribution among participants. The age distribution data for each session offers a means to track changes over time and identify patterns in the onset and progression of symptoms or skin conditions. The data is classified across six categories of skin types (e.g., Type 1 - Type 6), reflecting established criteria used to classify different skin types based on pigmentation, sensitivity, and susceptibility to or protection against various skin conditions.
[0047] Further, by using the skin disorder prediction controller (108), a Contrast Limited Adaptive Histogram Equalization (CLAHE) is employed in the data preprocessing stage to enhance image quality for analysis. The CLAHE improves local contrast by dividing the image into smaller regions, called tiles, and applying histogram equalization to each. This adaptive procedure ensures uniform enhancement of contrast across these segments, regardless of specific regional characteristics, which are highly dependent on both the particular body area and individual's skin. Consequently, it preserves local details, which is particularly advantageous in medical imaging, such as dermatology, where subtle features need to be identified. As depicted in FIG. 3a to FIG. 3c, subtle features are visualized without adding unnecessary noise. FIG. 3a to FIG. 3c are example illustrations of an original image (300a) versus grayscale image (300b) versus CLAHE enhanced image (300c). The CLAHE is particularly beneficial in cases like vitiligo, where it aids in visualizing vitiligo lesions and plays a crucial role in improving the accuracy of our computer vision methodologies for segmentation, focusing solely on relevant areas for analysis.
a. Divide the image into non-overlapping tiles:

b. Calculate the cumulative distribution function (CDF) for each tile:

c. Clip the histogram of each tile to limit the contrast enhancement:

d. Normalize the clipped histogram to obtain the equalized histogram

e. Interpolate the equalized histograms to remove artificial discontinuities:

[0048] Further, by using the skin disorder prediction controller (108), Gaussian blur a crucial subsequent step to the CLAHE is applied, that smoothens the image by averaging the value of each pixel with its neighbors using a sliding kernel. Following the CLAHE operations, which enhances contrast and makes details in the image more visible, Gaussian blur further smooths the image for easier analysis. It evens out lighting and contrast, resulting in reduced noise and fine details, and enhancing clarity of the remaining feature.

[0049] The Gaussian blur is the result of performing convolution of an image with a Gaussian kernel that is two=dimensional (2D) domain along the normal distribution curve:

where i represents the input image, s is the standard deviation of the Gaussian kernel, and Gs denotes the Gaussian kernel.
[0050] Further, a thresholding is a fundamental image processing operation where objects of interest are separated from the background by binarizing the image using a predetermined threshold value. This process effectively separates pigmented and depigmented areas in vitiligo lesions, facilitating segmentation. Subsequent erosion and dilation operations are applied consecutively to enhance segmentation results and improve lesion boundary delineation. Erosion reduces the boundaries of segmented regions by removing edge pixels, while dilation expands these boundaries by adding pixels to the edges. Repeating this sequence of operations eliminated noise and small irregularities from the segmented regions, as these elements typically have lower intensity around their periphery compared to their surroundings in the images. As a result, the representation of vitiligo lesions became smoother and more accurate in these areas.

where, I represent the input image, B denotes the structuring element, which defines the neighborhood used for the erosion or dilation operation, (x, y) are the coordinates of the pixel being processed, and (i, j) are the coordinates within the structuring element.
[0051] By using the skin disorder prediction controller (108), the proposed method adopts several sophisticated image processing and analysis techniques for the quantification of vitiligo lesions, aiming to provide comprehensive and accurate assessment. It begins by applying Otsu’s technique for optimal thresholding to effectively segment vitiligo lesions from the background, and subsequently delineate the affected areas with precision.
[0052] A binary mask is then computed based on the threshold obtained. The binary mask forms the basis for further analysis. Depigmented lesion extent is measured by quantifying the number of foreground pixels in the binary mask. This provides a quantitative measure of lesion extent which is useful in further characterizing lesions. Contour analysis is performed to accurately demarcate the boundaries of individual lesions.
[0053] Bounding rectangles enclosing each lesion are computed, providing essential spatial information needed for subsequent lesion characterization. To assign unique identifiers to lesion components for tracking purposes, convex hulls are also computed to capture the overall shape and spatial distribution of depigmented regions. This processing step is followed by an analysis of convexity defects within the convex hulls, revealing irregularities in the lesion contours that indicate the structural complexities defining lesion morphology.
[0054] All these sophisticated operations are integrated by leveraging advanced image processing techniques along with advanced spatial and morphological analysis to uncover the intricacies of vitiligo lesions. This approach aims to deliver insights that can significantly enhance the precision of vitiligo assessment and guide personalized therapeutic strategies. Together, these techniques aim to overcome the limitations of existing grading systems, providing dermatologists with a robust foundation to make definitive diagnoses, monitor therapy efficacy, and prognosticate vitiligo management.
[0055] The design for vitiligo lesions detection centered predominantly on segmentation methods grounded in intensity. These techniques encapsulate the nuances of pixel intensities within an image. As we are discerning vitiligo lesion detection, the necessity for intensity-based segmentation techniques arises from their ability to exploit the differences in depigmented patches to the surrounding unaffected skin. The segmentation process begins with the division of the intensity range of the image into several classes, absolutely essential for identifying vitiligo lesions based on their characteristic pixel intensities.
[0056] The Otsu’s method is applied to the intensity histogram of the image to obtain an optimal threshold that separates vitiligo lesion from surrounding healthy skin. The threshold effectively isolates the depigmented patches (lesions) from the normal skin in the image, which effectively crisp- gates the lesion delineation. The mathematical formula for calculating intra-class variance (s2) in Otsu’s method is as follows

where s2 (T) is the intra-class variance for threshold T, w0(T), and w1(T) are the probabilities of the background and foreground classes, respectively, for threshold T, s2(T) and s2(T) are the variances of the background and foreground classes, respectively, for threshold.
[0057] The optimal threshold Topt is the one that minimizes the intra- class variance:

[0058] The intensity ranges corresponding to different lesion scores are defined, aligned with our vitiligo scoring system. These in- tensity ranges are critical for categorizing pixel intensities into distinct classes, which represent different levels of vitiligo severity. Pixels falling within each intensity range are assigned the corresponding lesion score, allowing us to quantify vitiligo lesions based on its intensity characteristics. The proposed approach provides a systematic way of evaluation and classification of vitiligo lesions according to their salvageable levels, which will allow for comprehensive analysis and diagnosis.
[0059] Binary Mask Creation: The next step after determining the optimal threshold for each intensity category with Otsu’s method, is to generate binary masks, which is pivotal for highlighting regions of the image corresponding to vitiligo lesions. This enables further analysis and quantification of the extent and distribution of depigmented patches.
[0060] For the segmentation process a binary mask is created for each intensity category. These masks are used to isolate the regions from the images that correspond to the vitiligo lesions. The method includes, thresholding of intensity values of a plurality of image pixels. A value determined via Otsu’s method is shown in FIG. 4a to FIG. 4c. If an image pixel’s intensity is above a threshold, it is given a value of one in the binary mask, otherwise it is given a value of zero. In an embodiment, a value of zero represents healthy skin. Let I (x, y) be the intensity of the pixel at position (x, y) in the image. The binary mask B (x, y) is defined as:

where T is the threshold value determined using Otsu’s method.
[0061] Binary masks are invaluable for lesion’s further analysis and quantification in vitiligo due to the fact that they highlight regions of interest corresponding to depigmented patches. This is important for the measurement of shape, size and distribution of lesions. They also facilitate automated analysis techniques where quantitative features, such as lesion area, perimeter, and texture characteristics, are extracted.
[0062] Depigmentation Area Calculation: The number of vitiligo lesions within each intensity category is quantified using percentage calculation. A percentage calculation quantified the number of vitiligo lesions in number of pixels as compared to the whole image(s). This is important to this study as it not only gave some quantitation of the number of pixels within each intensity category, giving some inclination about the severity of vitiligo, but also because it described the dispersion of vitiligo. dispersion of which is important in the analysis of disease severity and progression. Let Ntotal denote a total number of pixels in the image, and Nlesion represent a number of pixels classified as vitiligo lesions within a specific intensity category. The percentage of pixels within the intensity category can be calculated using the following formula:

[0063] In addition, the percentage of pixels constituting the lesions in each intensity category can also provide a quantitative measure of severity of vitiligo, allowing clinicians an objective means by which to assess disease extent and to plan therapeutic interventions accordingly. Higher percentages at a given intensity reflect a larger area of the vitiligo lesions being occupied within that intensity. This might indicate a severe disease or a greater likelihood of disease progression. Based on response to a given therapy, it would then be decided whether or not to consider an alternative modality or agent. Ultimately, with time, it could lead to a tailoring treatment regimens and interventions, with the goal of optimizing patient and health related quality of life.
[0064] Intensity Distribution Analysis: The intensity distribution analysis refers to processing of the pixel intensity values of the segmented depigmented regions. Specifically, it is a critical analysis of the brightness and diversity of the areas to consider information that would be significant in deducing the depigmentation depth. To describe the intensity properties of the vitiligo patches, several statistics of the out are calculated, such as the mean intensity, standard deviation, and the histogram of the intensity values.
[0065] Mean Intensity (µ): The mean intensity is the average intensity of brightness of pixels found in the segmented patches. Mathematically, mean intensity is derived by adding up all the pixels and then dividing them by the number of total pixels:
…. (14)
where Ii represents the intensity value of pixel I, and N is the total number of pixels within the segmented region.
[0066] Standard Deviation (s): Standard Deviation is the variation in intensity of values generally within the patches. It shows how much the pixel intensity varies from the mean intensity. Mathematically, it is computed as

where Ii and µ have the same meanings as before. v denotes the square root function.
[0067] Lower average intensity values would mean decreased concentration of pigment, which corresponds to deeper depigmentation. As depigmentation continues, the melanin content decreases, which makes the skin dimmer which can be reflected by decreased average intensity values. Higher deviation values would mean that the intensity for the patch would vary more across it. The variation might result from disparities in melanin amounts or variations in the pigmentation areas of the skin among them. Increased variation is seen as a result of a deeper depigmentation process. Deeper depigmentation is associated with greater variability because melanin distribution pattern becomes more diversified.
[0068] Contour Detection: Contour detection is the essential step in the analysis of vitiligo lesions, which allows to locate the boundaries of lesions by delineating them. By representing the spatial distribution of depigmented areas as outlines (contours) within binary masks, contours contain critical information on the size, spatial configuration and morphology of lesions. Let C denote the set of contours detected within the binary mask, and A(C) represents the area enclosed by each contour C. The total number of contours detected within the binary mask is denoted as Ncontours.
[0069] The area of each contour A(C) can be calculated using the formula for polygon area. For a contour represented by a sequence of n vertices (xi, yi), the area A(C) is given by:

[0070] Lesion analysis relies on contour detection to elicit the spatial distribution and morphology of vitiligo lesions [24]. Contour detection algorithms determine lesion boundaries allowing for lesion size and shape complexity to be quantified along with their spatial arrangement. These metrics are essential for characterizing vitiligo severity, monitoring disease progression and assessing treatment efficacy.
[0071] Bounding Rectangle Generation: Bounding rectangles are geometric representations drawn around each contour detected within the binary mask and encapsulating the corresponding vitiligo lesions as shown in Figure 3. Spatially, bounding rectangle generation is critical in characterizing vitiligo lesions, as it provides vital spatial information, such as location, size and orientation. These rectangles are invaluable for lesion analysis, as they facilitate detailed examination, comparison and quantitative assessment of lesion characteristics. An example illustration of the bounding rectangle generation (500) is shown in FIG. 5.
[0072] Let Ri denote the bounding rectangle generated around contour Ci, where i = 1, 2, . . ., Ncontours and Ncontours is the total number of contours detected within the binary mask. Each bounding rectangle Ri is defined by four parameters: the coordinates of the top-left corner (xi, yi), the width wi, and the height hi. The coordinates of the top-left corner (xi, yi) are determined by the minimum (xmin, ymin) coordinates of the contour Ci. The width wi and height hi of bounding rectangle Ri are computed as the differences between the maximum (xmax, ymax) and minimum (xmin, ymin) coordinates of contour Ci along the x and y directions, respectively

[0073] The bounding rectangles created around vitiligo lesions have the potential to operationally integrate seamlessly with clinical practice. Clinically, bounding rectangle-derived metrics provide an operational definition of disease extent that can be used to quantify the size and distribution of lesions, track changes in lesion size and distribution over time, monitor the effects of treatment on the size and distribution of individual or combined lesions, or assess response or progression in clinical studies or practice, and objectively define target areas for treatment interventions, whether through the use of laser therapy or the defined application of topical treatments.
[0074] Component numbering: The component numbering (illustrated in FIG. 6) assigns a unique component number to each segmented lesion as shown in FIG. 6, which allows the correlation of lesion characteristics across body sites and with clinical data and treatment history. An example illustration of the component numbering (600) for a depigmented area is shown in FIG. 6. Let L denote the binary mask representing segmented vitiligo lesions, where Lij = 1 if pixel (i, j) belongs to a lesion and Lij = 0 otherwise. Component numbering involves assigning a unique component number Cij to each connected component (lesion) in the binary mask L. Component numbering is defined by

where Label (Lij) denotes the label assigned to pixel (i, j) in the connected component analysis
[0075] Convex Hull Calculation: The convex hull is utilized with the aim to characterize the overall shape and convexity of vitiligo lesions. The convex hull of a set of points is the smallest convex polygon that contains all the points [26][27], and thus computing the convex hull for each vitiligo lesion is used to provide a tool to quantify the irregularities and complexity of lesion boundaries and further characterize vitiligo morphology
[0076] Let Hi denote the convex hull computed for vitiligo lesion i, where i = 1, 2, . . ., Nlesions and Nlesions is the total number of vitiligo lesions detected within the binary mask. The convex hull Hi is represented as a set of vertices {(xi1, yi1), (xi2, yi2), . . ., (xik, yik)}, where k is the number of vertices in the convex hull.
[0077] Convexity Defects Analysis: Convexity defects analysis is performed as a means of assessing the morphology and irregularities of vitiligo lesions. Along the contours of lesions, convexity defects are identified, or areas of concavity/irregularity in the lesion boundary. Our goal in analysing these is to detect subtle changes in lesion morphology as shown in FIG. 7a to FIG. 11d and moreover, to evaluate progression of the disease over time. FIG. 7a to FIG. 7c and FIG. 8a to FIG. 8c are example illustrations (700a-700c, 800a-800c) of segmented outputs of a VEIDI multifactorial scoring framework. FIG. 9a to FIG. 9d, FIG. 10a to FIG. 10d, and FIG. 11a to FIG. 11d are example illustrations (900a-900d, 1000a-1000d, and 1100a-1100d) of segmented outputs of the VEIDI multifactorial scoring framework.
[0078] Let Di denote the set of convexity defects detected along the contour Ci of vitiligo lesion i, where i = 1, 2, . . ., Nlesions and Nlesions is the total number of vitiligo lesions detected within the binary mask. Each convexity defect is represented by a tuple (xj, yj, depthj), where (xj, yj) denotes the coordinates of the defect and depthj indicates the distance between the defect and the convex hull of the lesion. The detection of convexity defects involves analyzing the contour Ci of each vitiligo lesion and identifying regions of concavity or irregularity along the boundary.
[0079] FIG. 2 is a flow chart (200) illustrating a method for predicting a skin disorder, according to various embodiments of the present system.
[0080] At 202, the method includes obtaining the plurality of data. At 204, the method includes classifying the plurality of data into the at least one skin type distribution category. At 206, the method includes preprocessing the at least one skin type distribution category. At 208, the method includes performing a segmentation of at least one vitiligo lesion from a background analysis for the at least one skin type distribution category. At 210, the method includes predicting a skin disorder by delineating at least one affected area for the at least one skin type distribution category after performing the segmentation of at least one vitiligo lesion from a background analysis.
[0081] The modules of the system (1200) for predicting the skin disorder, described herein are implemented in computing devices. One example of the user device (100) is described below in FIG.12. The system (1200) includes one or more processor(s) (1202), one or more computer-readable RAMs (1204) and one or more computer-readable ROMs (1206) on one or more buses (1208). Further, the computing system (1200) includes a tangible storage device (1210) that may be used to execute operating systems (1220) and the system (1200). The various modules of the system (1200) may be stored in the tangible storage device (1210). Both the operating systems (1220) and the system (1200) are executed by the one or more processor(s) (1202) via one or more respective RAMs (1204) (which typically include cache memory). The execution of the operating systems (1220) and/or the system (1200) by the one or more processor(s) (1202), configures the one or more processor(s) (1202) as a special purpose processor configured to carry out the functionalities of the operation systems (1220) and/or the system (1200) as described above.
[0082] Examples of the tangible storage device (1210) include semiconductor storage devices such as ROM, EPROM, flash memory or any other computer-readable tangible storage device that may store a computer program and digital information.
[0083] Computing device (1900) also includes a R/W drive or interface (1214) to read from and write to one or more portable computer-readable tangible storage devices (1228) such as a CD-ROM, DVD, memory stick or semiconductor storage device. Further, network adapters or interfaces (1212) such as a TCP/IP adapter cards, wireless Wi-Fi interface cards, 3G or 4G wireless interface cards or other wired or wireless communication links are also included in computing devices.
[0084] In one example embodiment, the system (1200) may be stored in the tangible storage device (1210) and may be downloaded from an external computer via a network (for example, the Internet, a local area network or other, wide area network) and network adapter or interface (1212).
[0085] The system (1200) further includes device drivers (1216) to interface with input and output devices. The input and output devices may include a computer display monitor (1218), a keyboard (1222), a keypad, a touch screen, a computer mouse (1224), and/or some other suitable input device.
[0086] In this description, including the definitions mentioned earlier, the term ‘module’ may be replaced with the term ‘circuit.’ The term ‘module’ may refer to, be part of, or include processor hardware (shared, dedicated, or group) that executes code and memory hardware (shared, dedicated, or group) that stores code executed by the processor hardware. The term code, as used above, may include software, firmware, and/or microcode, and may refer to programs, routines, functions, classes, data structures, and/or objects.
[0087] Shared processor hardware encompasses a single microprocessor that executes some or all code from multiple modules. Group processor hardware encompasses a microprocessor that, in combination with additional microprocessors, executes some or all code from one or more modules. References to multiple microprocessors encompass multiple microprocessors on discrete dies, multiple microprocessors on a single die, multiple cores of a single microprocessor, multiple threads of a single microprocessor, or a combination of the above. Shared memory hardware encompasses a single memory device that stores some or all code from multiple modules. Group memory hardware encompasses a memory device that, in combination with other memory devices, stores some or all code from one or more modules.
[0088] In some embodiments, the module may include one or more interface circuits. In some examples, the interface circuits may include wired or wireless interfaces that are connected to a local area network (LAN), the Internet, a wide area network (WAN), or combinations thereof. The functionality of any given module of the present description may be distributed among multiple modules that are connected via interface circuits. For example, multiple modules may allow load balancing. In a further example, a server (also known as remote, or cloud) module may accomplish some functionality on behalf of a client module.
[0089] It will be understood by those within the art that, in general, terms used herein, are generally intended as “open” terms (e.g., the term “including” should be interpreted as “including but not limited to,” the term “having” should be interpreted as “having at least,” the term “includes” should be interpreted as “includes but is not limited to,” etc.). It will be further understood by those within the art that if a specific number of an introduced claim recitation is intended, such an intent will be explicitly recited in the claim, and in the absence of such recitation no such intent is present.
[0090] For example, as an aid to understanding, the following appended claims may contain usage of the introductory phrases "at least one" and "one or more" to introduce claim recitations. However, the use of such phrases should not be construed to imply that the introduction of a claim recitation by the indefinite articles "a" or "an" limits any particular claim containing such introduced claim recitation to embodiments containing only one such recitation, even when the same claim includes the introductory phrases "one or more" or "at least one" and indefinite articles such as "a" or "an" (e.g., “a” and/or “an” should be interpreted to mean “at least one” or “one or more”); the same holds true for the use of definite articles used to introduce claim recitations. In addition, even if a specific number of an introduced claim recitation is explicitly recited, those skilled in the art will recognize that such recitation should be interpreted to mean at least the recited number (e.g., the bare recitation of "two recitations," without other modifiers, means at least two recitations, or two or more recitations).
[0091] While only certain features of several embodiments have been illustrated, and described herein, many modifications and changes will occur to those skilled in the art. It is, therefore, to be understood that the appended claims are intended to cover all such modifications and changes as fall within the true spirit of inventive concepts.
[0092] The aforementioned description is merely illustrative in nature and is in no way intended to limit the disclosure, its application, or uses. The broad teachings of the disclosure may be implemented in a variety of forms. Therefore, while this disclosure includes particular examples, the true scope of the disclosure should not be so limited since other modifications will become apparent upon a study of the drawings, the specification. It should be understood that one or more steps within a method may be executed in different order (or concurrently) without altering the principles of the present disclosure. Further, although each of the example embodiments is described above as having certain features, any one or more of those features described with respect to any example embodiment of the disclosure may be implemented in and/or combined with features of any of the other embodiments, even if that combination is not explicitly described. In other words, the described example embodiments are not mutually exclusive, and permutations of one or more example embodiments with one another remain within the scope of this disclosure.
[0093] The example embodiment or each example embodiment should not be understood as a limiting/restrictive of inventive concepts. Rather, numerous variations and modifications are possible in the context of the present disclosure, in particular those variants and combinations which may be inferred by the person skilled in the art with regard to achieving the object for example by combination or modification of individual features or elements or method steps that are described in connection with the general or specific part of the description and/or the drawings, and, by way of combinable features, lead to a new subject matter or to new method steps or sequences of method steps, including insofar as they concern production, testing and operating methods. Further, elements and/or features of different example embodiments may be combined with each other and/or substituted for each other within the scope of this disclosure.
[0094] Still further, any one of the above-described and other example features of example embodiments may be embodied in the form of an apparatus, method, system, computer program, tangible computer readable medium and tangible computer program product. For example, the aforementioned methods may be embodied in the form of a system or device, including, but not limited to, any of the structure for performing the methodology illustrated in the drawings.
[0095] In this application, including the definitions below, the term ‘module’ or the term ‘controller’ may be replaced with the term ‘circuit.’ The term ‘module’ may refer to, be part of, or include processor hardware (shared, dedicated, or group) that executes code and memory hardware (shared, dedicated, or group) that stores code executed by the processor hardware.
[0096] The module may include one or more interface circuits. In some examples, the interface circuits may include wired or wireless interfaces that are connected to a local area network (LAN), the Internet, a wide area network (WAN), or combinations thereof. The functionality of any given module of the present disclosure may be distributed among multiple modules that are connected via interface circuits. For example, multiple modules may allow load balancing. In a further example, a server (also known as remote, or cloud) module may accomplish some functionality on behalf of a client module.
[0097] Further, at least one example embodiment relates to a non-transitory computer-readable storage medium comprising electronically readable control information (e.g., computer-readable instructions) stored thereon, configured such that when the storage medium is used in a controller of a magnetic resonance device, at least one example embodiment of the method is carried out.
[0098] Even further, any of the aforementioned methods may be embodied in the form of a program. The program may be stored on a non-transitory computer readable medium, such that when run on a computer device (e.g., a processor), cause the computer-device to perform any one of the aforementioned methods. Thus, the non-transitory, tangible computer readable medium is adapted to store information and is adapted to interact with a data processing facility or computer device to execute the program of any of the above-mentioned embodiments and/or to perform the method of any of the above-mentioned embodiments.
[0099] The computer readable medium or storage medium may be a built-in medium installed inside a computer device’s main body or a removable medium arranged so that it may be separated from the computer device’s main body. The term computer-readable medium, as used herein, does not encompass transitory electrical or electromagnetic signals propagating through a medium (such as on a carrier wave), the term computer-readable medium is therefore considered tangible and non-transitory. Non-limiting examples of the non-transitory computer-readable medium include, but are not limited to, rewriteable non-volatile memory devices (including, for example flash memory devices, erasable programmable read-only memory devices, or a mask read-only memory devices), volatile memory devices (including, for example static random access memory devices or a dynamic random access memory devices), magnetic storage media (including, for example an analog or digital magnetic tape or a hard disk drive), and optical storage media (including, for example a CD, a DVD, or a Blu-ray Disc). Examples of the media with a built-in rewriteable non-volatile memory, include but are not limited to memory cards, and media with a built-in ROM, including but not limited to ROM cassettes, etc. Furthermore, various information regarding stored images, for example, property information, may be stored in any other form, or it may be provided in other ways.
[00100] \The term code, as used above, may include software, firmware, and/or microcode, and may refer to programs, routines, functions, classes, data structures, and/or objects. Shared processor hardware encompasses a single microprocessor that executes some or all code from multiple modules. Group processor hardware encompasses a microprocessor that, in combination with additional microprocessors, executes some or all code from one or more modules. References to multiple microprocessors encompass multiple microprocessors on discrete dies, multiple microprocessors on a single die, multiple cores of a single microprocessor, multiple threads of a single microprocessor, or a combination of the above.
[00101] Shared memory hardware encompasses a single memory device that stores some or all code from multiple modules. Group memory hardware encompasses a memory device that, in combination with other memory devices, stores some or all code from one or more modules.
[00102] The term memory hardware is a subset of the term computer-readable medium. The term computer-readable medium, as used herein, does not encompass transitory electrical or electromagnetic signals propagating through a medium (such as on a carrier wave), the term computer-readable medium is therefore considered tangible and non-transitory. Non-limiting examples of the non-transitory computer-readable medium include, but are not limited to, rewriteable non-volatile memory devices (including, for example flash memory devices, erasable programmable read-only memory devices, or a mask read-only memory devices), volatile memory devices (including, for example static random access memory devices or a dynamic random access memory devices), magnetic storage media (including, for example an analog or digital magnetic tape or a hard disk drive), and optical storage media (including, for example a CD, a DVD, or a Blu-ray Disc). Examples of the media with a built-in rewriteable non-volatile memory, include but are not limited to memory cards, and media with a built-in ROM, including but not limited to ROM cassettes, etc. Furthermore, various information regarding stored images, for example, property information, may be stored in any other form, or it may be provided in other ways.
[00103] The apparatuses and methods described in this application may be partially or fully implemented by a special purpose computer created by configuring a general-purpose computer to execute one or more particular functions embodied in computer programs. The functional blocks and flowchart elements described above serve as software specifications, which may be translated into the computer programs by the routine work of a skilled technician or programmer.
[00104] The computer programs include processor-executable instructions that are stored on at least one non-transitory computer-readable medium. The computer programs may also include or rely on stored data. The computer programs may encompass a basic input/output system (BIOS) that interacts with hardware of the special purpose computer, device drivers that interact with particular devices of the special purpose computer, one or more operating systems, user applications, background services, background applications, etc.
[00105] The computer programs may include: (i) descriptive text to be parsed, such as HTML (hypertext markup language) or XML (extensible markup language), (ii) assembly code, (iii) object code generated from source code by a compiler, (iv) source code for execution by an interpreter, (v) source code for compilation and execution by a just-in-time compiler, etc. As examples only, source code may be written using syntax from languages including C, C++, C#, Objective C, Haskell, Go, SQL, R, Lisp, Java®, Fortran, Perl, Pascal, Curl, OCaml, Javascript®, HTML5, Ada, ASP (active server pages), PHP, Scala, Eiffel, Smalltalk, Erlang, Ruby, Flash®, Visual Basic®, Lua, and Python®.
[00106] A person of ordinary skill in the art will readily ascertain that the illustrated embodiments and steps in description and drawings (FIGS. 1-12) are set out to explain the exemplary embodiments shown, and it should be anticipated that ongoing technological development will change the manner in which particular functions are performed. These examples are presented herein for purposes of illustration, and not limitation. Further, the boundaries of the functional building blocks have been arbitrarily defined herein for the convenience of the description. Alternative boundaries can be defined so long as the specified functions and relationships thereof are appropriately performed. Alternatives (including equivalents, extensions, variations, deviations, etc., of those described herein) will be apparent to persons skilled in the relevant art(s) based on the teachings contained herein. Such alternatives fall within the scope and spirit of the disclosed embodiments.
[00107] Method steps: A person of ordinary skill in the art will readily ascertain that the illustrated steps are set out to explain the exemplary embodiments shown, and it should be anticipated that ongoing technological development will change the manner in which particular functions are performed. These examples are presented herein for purposes of illustration, and not limitation. Further, the boundaries of the functional building blocks have been arbitrarily defined herein for the convenience of the description. Alternative boundaries can be defined so long as the specified functions and relationships thereof are appropriately performed. Alternatives (including equivalents, extensions, variations, deviations, etc., of those described herein) will be apparent to persons skilled in the relevant art(s) based on the teachings contained herein. Such alternatives fall within the scope and spirit of the disclosed embodiments.
[00108] The present invention offers multiple advantages over the prior art and the above listed are a few examples to emphasize on some of the advantageous features. The listed advantages are to be read in a non-limiting manner.

REFERENCE NUMERALS
[00109] User device - 100
[00110] Processor – 102
[00111] Memory – 104
[00112] Display – 106
[00113] Skin disorder prediction controller - 108
, Claims:We Claim:
1. A method for predicting a skin disorder, the method comprising the steps of: - obtaining, by a user device (100), a plurality of data;
- classifying, by the user device (100), the plurality of data into at least one skin type distribution category;
- preprocessing, by the user device (100), the at least one skin type distribution category;
- performing, by the user device (100), a segmentation of at least one vitiligo lesion from a background analysis for the at least one skin type distribution category; and
- predicting, by the user device (100), a skin disorder by delineating at least one affected area for the at least one skin type distribution category after performing the segmentation of at least one vitiligo lesion from a background analysis.

2. The method as claimed in claim 1, wherein the predicting the skin disorder further comprises:
- applying an Otsu’s technique to an intensity histogram of a normal skin image to obtain an optimal threshold;
- separating a vitiligo lesion from the normal skin image using the optimal threshold, wherein the optimal threshold isolates at least one depigmented patch from the normal skin image; and
- categorizing pixel intensities into distinct classes, which represented different levels of a vitiligo lesion severity to predict the skin disorder.

3. The method as claimed in claim 2, wherein the separating the vitiligo lesion from the normal skin image using the optimal threshold, further comprises:
- generating a binary mask for the image;
- computing a depigmentation area;
- performing contour analysis in the binary mask;
- computing a bounding rectangle for each contour;
- assigning a unique identifier to each lesion;
- computing a convex hull for each contour;
- analyzing a convexity defect; and
- generating output comprising the depigmentation area, the contour, the bounding rectangles, and the defect.

4. The method as claimed in claim 3, wherein the binary mask is used to compute a quantitative feature comprising a lesion area, a perimeter, and texture characteristics.

5. The method as claimed in claim 1, wherein a plurality of data comprises a skin type trends and age distribution among users.

6. The method as claimed in claim 1, wherein the plurality of data is classified into at least one skin type distribution category based on pigmentation, sensitivity and susceptibility to or protection against different skin condition.

7. The method as claimed in claim 1, wherein preprocessing the at least one skin type distribution category comprises:
- performing an image processing on the at least one skin type distribution category;
- dividing the image into non-overlapping tiles after performing the image processing;
- computing a cumulative distribution function (CDF) for each tile;
- clipping a histogram of each tile to limit a contrast enhancement on the image;
- normalizing the clipped histogram to obtain an equalized histogram;
- interpolating the equalized histogram to remove artificial discontinuities; and
- applying a Gaussian blur on the equalized histogram.

8. A user device (100) for predicting a skin disorder, comprising:
- a processor (102);
- a memory (104); and
- a skin disorder prediction controller (108), coupled with the processor (102) and the memory (104), configured to:
obtain a plurality of data;
classify the plurality of data into at least one skin type distribution category;
preprocess the at least one skin type distribution category;
perform a segmentation of at least one vitiligo lesion from a background analysis for the at least one skin type distribution category; and
predict a skin disorder by delineating at least one affected area for the at least one skin type distribution category after performing the segmentation of at least one vitiligo lesion based on a background analysis.

9. The user device (100) as claimed in claim 8, wherein to predict the skin disorder, the user device (100) is further configured to:
- apply an Otsu’s technique to an intensity histogram of a normal skin image to obtain an optimal threshold;
- separate a vitiligo lesion from the normal skin image using the optimal threshold, wherein the optimal threshold isolates at least one depigmented patch from the normal skin image; and
- categorize pixel intensities into distinct classes, which represent different levels of a vitiligo lesion severity to predict the skin disorder.

10. The user device (100) as claimed in claim 9, wherein to separate the vitiligo lesion from the normal skin image using the optimal threshold, the user device (100) is further configured to:
- generate a binary mask for the image;
- compute a depigmentation area;
- perform contour analysis in the binary mask;
- compute a bounding rectangle for each contour;
- assign a unique identifier to each lesion;
- compute a convex hull for each contour;
- analyze a convexity defect; and
- generate an output comprising the depigmentation area, the each contour, the each bounding rectangle, and the convexity defect.

11. The user device (100) as claimed in claim 10, wherein the binary mask is used to compute a quantitative feature comprising a lesion area, a perimeter, and a plurality of texture characteristics.

12. The user device (100) as claimed in claim 8, wherein a plurality of data comprises one or more skin type trends and an age distribution among users.

13. The user device (100) as claimed in claim 8, wherein the plurality of data is classified into at least one skin type distribution category based on a pigmentation, a sensitivity and one or more of a susceptibility to or a protection against a skin condition.

14. The user device (100) as claimed in claim 8, wherein to preprocess the at least one skin type distribution category, the user device (100) is further configured to:

- perform an image processing on the at least one skin type distribution category;
- divide the image into non-overlapping tiles after performing the image processing;
- compute a cumulative distribution function (CDF) for each tile;
- clip a histogram of each tile to limit a contrast enhancement on the image;
normalize the clipped histogram to obtain an equalized histogram;
interpolate the equalized histogram to remove artificial discontinuities; and
- apply a Gaussian blur on the equalized histogram.

15. A non-transitory computer-readable medium having stored thereon computer-readable instructions that, when executed by a processor (102), cause the processor (102) to:
- obtain a plurality of data;
- classify the plurality of data into at least one skin type distribution category;
- preprocess the at least one skin type distribution category;
- perform a segmentation of at least one vitiligo lesion from a background analysis for the at least one skin type distribution category; and
- predict a skin disorder by delineating at least one affected area for the at least one skin type distribution category after performing the segmentation of at least one vitiligo lesion from a background analysis.