Description:Complete Specification:

The following specification describes and ascertains the nature of this invention and the manner in which it is to be performed.
Field of the invention
[0001]This invention is related to a control unit for determining and representing Poikilocytes in a blood sample and a method thereof.

Background of the invention
[0002] In the medical procedures, analysis of blood samples provides clinically significant information. Currently blood samples are analyzed using cell counters followed by peripheral smear examination. Despite the vast technological advancement, peripheral blood smear examination plays a significant role because of the detailed clinical insights on cellular anomalies, which were not analyzed in-detail, by automated flow cytometers. Peripheral Smear examination is usually performed to study the morphological abnormalities of blood components and their distribution. On average, nearly 6 billion blood tests are performed every year requiring huge potential pathologists.

[0003] A US patent application 7838296 discloses a diagnostic device is able to perform measurements on blood flow using physiological salt solution, which is harmless to living objects, as a tracer. The device has a calculation process performed on the electric signal, which represents changes in concentration of hemoglobin generated by injection of the physiological salt solution. At least one of the blood flows, the absolute concentration of oxyhemoglobin or deoxyhemoglobin is displayed.
Brief description of the accompanying drawings
[0004] Figure 1 illustrates a control unit in a digital focusing device, in accordance with an embodiment of the invention; and
[0005] Figure 2 illustrates a flowchart of a method of determining of Poikilocytes of a blood sample in accordance with the present invention.

Detailed description of the embodiments
[0009] Figure 1 illustrates a control unit for determining and representing Poikilocytes of a blood sample, in accordance with an embodiment of the invention. The control unit captures multiple images of the blood sample using an automated digital image acquisition unit. The control unit calibrates the captured images of the blood sample to a micrometer scale. The control unit converts at least one captured image of the blood sample to a corresponding evenly contrasted image. The control unit detects multiple cells of interest in the evenly contrasted image and enhances each of the detected cell edges using at least one signal processing technique. The control unit extracts at least one feature of each cell and identifies an optimized feature set from extracted features comprising any one of morphological and geometrical feature for categorizing the detected cells. The control unit identifies poikilocytes from at least one of the extracted features using at least one intelligence network and determines a number of the poikilocytes in the detected cells based on a predefined feature value.

[0010] Further, the construction of the control unit 12 and the components of the control unit 12 is explained as follows. The Anisocytosis is a process of representing size variations in the red blood cells 18. The Anisocytosis represents unequal size of red blood cells in the blood sample 14. The control unit 12 is chosen from a group of control units comprising a microprocessor, a microcontroller, a digital circuit, an integrated chip and the like. The control unit 12 receives at least one image from the image processing unit. However, it is to be noted that, the control unit can receive a signal of said blood sample, not only in the form of image, but also can be in the form of a pulse or the like. However, it is to be understood that the control unit 12 receives the data related to the blood sample in any other form that is known to a person skilled in the art but is not restricted to the above-mentioned signal forms.

[0011] According to one embodiment of the invention, the image is received from the image processing unit 16 that uses any one of the image processing techniques known in the state of the art. The blood of a human being is smeared on the sample 14 for the analysis /diagnosis purpose. The sample 14 having the human body fluid/specimen like blood is placed in one of the analysis devices that is known in the state of the art.

[0012] According to one embodiment of the invention, the blood sample 14 is placed in a digital focusing device 10. The digital focusing device 10 is used to focus the content of the blood sample 14 and to analyze the content of the blood sample 14 for diagnosing purpose. According to one embodiment of the invention, the control unit 12 is made as an integral component of the digital focusing device 10. The device 10 captures the image 13 (which is two-dimensional) of the content of the blood sample 14 and processes it further to determine and represent the red blood cells 18 of the blood sample 14. In another embodiment, the control unit 12 is an external source connected to the device 10, wherein the control unit 12 is a cloud repository that is connected to the device through a communication means. The control unit 12 displays the severity of the identified at least one medical condition to the user directly using the communication means. In addition to that, the control unit 12 displays the number of identified types of red blood cells 18 in the given blood sample 14.

[0013] The artefacts in the content of the blood sample 14 is identified using at least one imaging processing technique or a learning model like an artificial intelligence (AI) model, deep learning technique (DL), a machine learning (ML) technique, a blurring technique or by using any one of the linear filters and non-linear filters. The basic information on the working methodology of the AI model or the DL/ML technique is not explained in detail, as it is known in the state of the art.

[0014]The at least one extracted feature is chosen from a group of characteristics comprising a surface area, a feret, a total area of the signal, Mean, Centroid, Perimeter, Angle, Circularity, Integrated Density, Skewness, Kurtosis, Aspect ratio, Roundness, Solidity, Rectangularity, Correlation, Entropy, Elongation, Solidarity, Convexity, Axis Percentage Difference, Variance, Homogeneity, Prominence, Shade, Energy, a cellular geometry , a morphology, color, texture, a cellular arrangement and the like. However, the extracted features are not restricted to the above disclosed features but can be any other features that provides the information of at least one cell of the human body fluid (which is a blood sample according to one embodiment of the present invention).

[0015] Figure 2 illustrates a flowchart of a method method of determining poikilocytes in red blood cells present in a blood sample. In step S1, multiple images are captured of the blood sample using an automated digital image acquisition unit of a control unit. In step S2, the capture images of the blood sample are calibrated to a micrometer scale by the control unit. In step S3, the at least one captured image of blood sample is converted to a corresponding evenly contrasted image. In step S4, multiple cells of interest are detected in the evenly contrasted image and each of the detected cell edge is enhanced using at least one signal processing technique. In step S5, at least one feature of each cell 18 is extracted and an optimized feature set from extracted features is identified comprising any one of morphological and geometrical feature for categorizing the detected cells. In step S6, poikilocytes from at least one said extracted features is identified using at least one intelligence network and a number of the poikilocytes is identified in said detected cells 18 based on a predefined feature value.

[0016]The above method is explained in detail. For better understanding of the invention, the device 10 is considered as the digital focusing device, the content of the sample 14 is a blood sample and the image of the sample 14 is taken. With the above-mentioned method, the control unit 12 detects multiple characteristics of the blood. In the present invention, the control unit 12 is detecting different types of red blood cells based on at least one feature value of the blood sample 14 of the patient using the above digital focusing device 10. The at least one medical condition is chosen from a group of medical conditions comprising Iron deficiency anemia, Elliptocytosis, Ovalocytic Anemia, Macro Ovalocytic anemia and the like. However, the medical condition can be of any other medical condition that is known to a person skilled in the art. The blood on the sample 14 is smeared and is placed in the digital focusing device 10. The control unit 12 captures the image 13 of the blood sample 14 using a camera of the device 10. The image 13 comprises different cells 18 of the blood and the artefacts.

[0017] Using at least one image processing technique, the control unit 12 removes all the artefacts present in the image 13. In other words, the control unit 12 differentiates between the cells 18 of interest and the artefacts using any one of the following processing techniques like artificial intelligence (AI) model, Machine learning technique, Deep learning technique and the like. The control unit 18 uses an image processing technique on the raw captured images that are acquired from the peripheral blood smear using an automated Image Acquisition unit. The size of the blood cells is usually measured in micrometer scale. In order to match the standard measurement scale, the acquired images are calibrated to micrometer scale by the control unit 18 using any one of the image processing techniques. The Image processing unit is made generic to adapt any magnification value with a configurable parameter. The control unit 12 upon detecting the multiple cells 18 of interest enhances the cells 18 by enhancing the edges of the cells 18. The cells of interest 18 with the enhanced edges are used for further processing in determining the shape deformities in red blood cells/ Poikilocytes of the red blood cells in the blood sample.

[0018] The control unit 12 isolates the enhanced cells 18 of interest from the background of the image 13 and labels each cell 18. For instance, the control unit 12 labels each cell 18 as L1, L2, and L3…Ln. The control unit 12 upon labelling each cell 18 extracts at least one feature (as mentioned above) of each cell 18. The control unit 12 extracts the features from each of the labelled cells 18.

[0019] The control unit is fed with an artificial intelligence model (AI), a machine-learning module (ML), a deep learning model (DL), which is built based on the extracted feature sets. The real-time features are extracted and then fed to the model for further analysis. The explanation on the learning module like artificial intelligence model (AI), machine learning module (ML), deep learning models (DL) and the like is not provided much in detail, as it is known in the state of the art. The features as mentioned above are used to differentiate the cells 18 into different categories. According to one embodiment of the invention, the control unit 12 categorizes the labelled cells 18 into at least two categories comprising red blood cells (RBC) 18 and the non-red blood cells (non-RBC). The Non-RBC comprises white blood cells, the blood platelets and the others.

[0020] The control unit 12 upon categorizing the labelled cells 18 based on the extracted features, determines the count of the categorized red b132alood cells 18 and the count of the non-red blood cells. The count of red blood cells 18 is considered as “n” and the count of non- red blood cells is considered as “n1”.

[0021] Instead of addressing any particular problem such as artifacts removal, uneven illumination, uneven contrast, out of focus or blurredness in an image, the control unit enhance the edges of the cells while suppressing the noises in the background. This is performed to convert an input image of any varied image quality to an evenly contrasted image where there is clear distinction between the cells in the foreground and the image background. The control unit 12 identifies an optimized feature set from the extracted features. Identification of poikilocytes involves any one of the following characteristics namely cellular boundary consisting of multiple projections which are uniform and blunt, extension of membrane in one area and rounded margins otherwise, egg-shaped cellular boundary, Pencil rod cigar shaped with Hemoglobin concentrated on both the ends, crescent shaped with pointed projections, broader central area, 3-12 spicules, sharp, irregularly spaced, no central pallor, one or more semi-circular portions removed from the cell margin. The control unit identifies the types of poikilocytes based on any one of the features and the identified cell will be from a group of poikilocytes comprising Dacrocyte, Sickle cell, Elliptocyte, Ovalocyte, Echinocyte, Acanthocyte, and Schistocyte.

[0022]The optimized feature set comprises the features like List of features extracted specific to characterization of Poikilocytes include – Centre of mass, Bounding Rectangular Width & Height, Feret, Minimum Feret, Aspect Ratio, Solidity, Rectangularity, Convex Hull Area, Convex Hull Perimeter, Solidarity, Convexity, Difference Percentage, Rectangular Difference, Axis Percentage Difference, Feret Major Difference, Feret Minor Difference and the like. However, it is to be noted that, the optimized feature set is not restricted to the above features, but can be of any other features as known to a person skilled in the art.

[0023] According to one embodiment, the control unit 12 determines the number of the identified poikilocytes. The predefined feature value is fed to the device 10 using at least one learning techniques comprising an artificial intelligence (AL) model, a machine-learning module (ML), a deep learning model (DL) or the like. For instance, at least one feature is an area value, and the predefined feature value is the area value of an ideal red blood cell. The predefined feature value is stored in the control unit 12 of the device 10 during the calibration process. For instance, the control unit 12 compares the area of the each of the red blood cell 18 present in the blood sample with the predefined area value. Based on the comparison result the control unit 12 identifies the types of red blood cells 18 as any one of the following cells Dacrocyte, Sickle cell, Elliptocyte, Ovalocyte, Echinocyte, Acanthocyte, and Schistocyte. From the extracted features, the control unit 18 using any one of the intelligence networks (eg: Artificial intelligence (AI) network) based supervised learning model is being built to classify the targeted cellular anomaly. The built-in hypothesis has been continuously optimized based on the neural network present in the AI performance to efficiently classify the Poikilocyte.

[0024] The above-disclosed different types of red blood cells 18 are determined an image of the blood sample 14. The control unit 12 determines at least one feature values from the image 13 (which is a two-dimensional signal) of the blood sample 14, which is a simple and cost –effective solution when compared to the conventional methods. It also represents the identified type of red blood cells based on the determined at least one feature values and the severity of at least one medical condition. With the above method and control unit 12 in the device 10, many blood characteristics are effectively determined. The present invention is highly compliant to handle myriad of raw image data variations including image intensity fluctuations, different staining conditions, myriad of smearing quality.

[0025] Even in the cases of custom-made smear of the content of the sample 14 or in the conventional methods, the present invention provides a robust solution in determining at least one characteristic of a human body fluid in detecting at least one abnormality in the human body. The above-disclosed method evades the necessity of high-rate 360-degree rotational movement of the content of the sample 14, at approximately 3200 –3500 rpm for 15 –20 minutes. This centrifugation of the blood sample 14 will have an impact on the clinical reports being generated. The present invention does not require a controlled environment to be established.

[0026] The present invention helps in avoiding the reagent cost used in cell coulters, which are currently used for analysis of blood sample 14. With the above disclosed method, estimation of both volumetric indices/ count metrics and cellular morphological findings along with additional new graphical representations (provided for clinical insights) from single system is achieved, thereby overcoming the necessity of two independent devices (as in the conventional systems) and reducing the cost & system maintenance. The method disclosed in the present invention eliminates the lysing of cells, thereby overcoming the necessity of chemicals in the volumetric analysis and avoids the chemicals used for retaining the cellular morphology.

[0027] With the present invention disclosing a method of determining the at least one feature value of the blood sample 14 and representing the same in the device 10 in the form of a graphical representation, at least one medical condition can be easily identified. The medical condition can be detected from a two-dimensional image signal through at least one image processing technique and through at least one learning module (egg: artificial Intelligence (AI) model). A mathematical machine-learning hypothesis is being built combining both the statistical hypothesis and clinical hypothesis in order to detect the feature values from the two-dimensional vector.

[0028] The above-disclosed method provides a solution in reducing the cost function by adapting several function approximation techniques and by assigning high weights to the significant feature vectors thus eliminating the feature vectors resulting in null hypothesis. In addition to this, the proposed methodology employs techniques to calculate at least one feature value and representing the severity of the medical conditions based on the determined feature value in the form of a graphical representation.

[0029] It should be understood that embodiments explained in the description above are only illustrative and do not limit the scope of this invention. Many such embodiments and other modifications and changes in the embodiment explained in the description are envisaged. The scope of the invention is only limited by the scope of the claims.
, Claims:We Claim:

1. A control unit (12) for determining poikilocytes in red blood cells (18) present in a blood sample (14), said control unit (12) adapted to:
- capture multiple images (15) of said blood sample (14) using an automated digital image acquisition unit (16);

characterized in that:
- calibrate said capture images (15) of said blood sample (14) to a micrometer scale;
- convert said at least one captured image (15) of said blood sample (14) to a corresponding evenly contrasted image;
- detect multiple cells (18) of interest in said evenly contrasted image (15) and enhance each of said detected cell (18) edges using at least one signal processing technique;
- extract at least one feature of said each cell (18) and identify an optimized feature set from extracted features comprising any one of morphological and geometrical feature for categorizing said detected cells (18);
- identify poikilocytes from at least one said extracted features using at least one intelligence network (20) and determine a number of said poikilocytes in said detected cells (18) based on a predefined feature value.

2. The control unit (12) as claimed in claim 1, wherein said control unit (12) adapted to calibrate said at least one captured image (15) to said micrometer scale for detecting a size of each of said detected cells (18).

3. The control unit (12) as claimed in claim 1, wherein said detected cells (18) are categorized into multiple groups based on morphological and geometrical features, said multiple groups comprises Dacrocyte, Sickle cell, Elliptocyte, Ovalocyte, Echinocyte, Acanthocyte, Schistocyte.

4. The control unit (12) as claimed in claim 1, wherein an optimized feature set is chosen from said extracted features, said optimized featured set comprises features like Centre of mass, Bounding Rectangular Width & Height, Feret, Minimum Feret, Aspect Ratio, Solidity, Rectangularity, Convex Hull Area, Convex Hull Perimeter, Solidarity, Convexity, Difference Percentage, Rectangular Difference, Axis Percentage Difference, Feret Major Difference, Feret Minor Difference.

5. The control unit (12) as claimed in claim 4, wherein determines a volume of each of said at least one categorized cell from said at least one extracted feature of said optimized set, for determining at least one medical condition and at least one volume characteristic of said blood sample (14).

6. The control unit (12) as claimed in claim 1, wherein said control unit (12) adapted to distinguish cells (18) present in a foreground of said at least one captured image (15) and a background of said at least one captured image (15) using said at least one processing technique.

7. The control unit (12) as claimed in claim 1, wherein said intelligence networks (20) are chosen from a group of intelligence networks comprising an artificial intelligence (AI) network, a machine learning network (ML), a deep learning network (DL).

8. A method of determining poikilocytes in red blood cells (18) present in a blood sample (14), said method comprising:
- capturing multiple images (15) of said blood sample (14) using an automated digital image acquisition unit (16) of a control unit (12);

characterized in that:
- calibrating said capture images (15) of said blood sample (14) to a micrometer scale by said control unit (12);
- converting said at least one captured image (15) of to a corresponding evenly contrasted image;
- detecting multiple cells (18) of interest in said evenly contrasted image (15) and enhancing each of said detected cell (18) using at least one signal processing technique;
- extracting at least one feature of said each cell (18) and identify an optimized feature set from extracted features comprising any one of morphological and geometrical feature for categorizing said detected cells (18);
- identifying poikilocytes from at least one said extracted features using at least one intelligence network (20) and determine a number of said poikilocytes in said detected cells (18) based on a predefined feature value.