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 Aniso-chromasity 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 Aniso-chromasity of a blood sample in accordance with the present invention.

 
Detailed description of the embodiments
[0006] Figure 1 illustrates a control unit for determining Aniso-chromasity of a blood sample, in accordance with an embodiment of the invention. The control unit 12 captures multiple images 15 of the blood sample 14 using an automated digital image acquisition unit 16 and identifies an ideal region in the captured images 15. The control unit 12 calibrates the capture images 15 of the blood sample 14 to a micrometer scale and converts the at least one captured image 15 of to a corresponding evenly contrasted image. The control unit 12 detects multiple cells 18 of interest in the evenly contrasted image 15 and enhances each of the detected cells 18 using at least one signal processing technique. the control unit 12 extracts at least one feature of each cell 18 and identifies an optimized feature set from extracted features for categorizing the detected cells 18 the optimized feature set is formed based on size and chromasity/hemoglobin content of the at least one blood cell 18. The control unit 18 identifies anisocytosis from at least one of the extracted features of the optimized feature set using at least one intelligence network 20 and determine a number of the
Aniso-chromasity cells in the detected cells 18 based on a predefined feature value.

[0007] Further, the construction of the control unit 12 and the components of the control unit 12 is explained as follows. The Aniso-chromasity conditions involves any one of the following characteristics namely cell size smaller or larger than the normal cellular size, consisting of either normal or low hemoglobinized cells, combination of varied cellular size & hemoglobin content. 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 related to the blood sample 14, 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 image form.

[0008] The automated digital image acquisition unit 16 comprises an image processing unit which 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.

[0009] 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, the control unit 12 is made as an integral component of the digital focusing device 10. The device 10 captures the at least one 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.

[0010] 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 final result of the Aniso-chromasity /cellular size variation and chromasity variation values or the severity of the identified at least one medical condition to the user directly using the communication means.

[0011] 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.

[0012]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).

[0013] Figure 2 illustrates a flowchart of a method of determining Aniso-chromasity from a signal 13 related to a blood sample 14 in accordance with the present invention. In step S1, - multiple images 15 of the blood sample 14 are captured using an automated digital image acquisition unit 16 and an ideal region is identified in the captured images 15 by a control unit 12. In step S2, the capture images 15 is calibrated of the blood sample 14 to a micrometer scale. In step S3, at least one captured image 15 of the blood sample 14 is converted to a corresponding evenly contrasted image. In step S4, multiple cells 18 of interest in the evenly contrasted image 15 and enhance each of the detected cell 18 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 is identified from extracted features for categorizing the detected cells 18. The optimized feature set is formed based on size and chromasity/hemoglobin content of the at least one blood cell 18. In step S6, anisocytosis cells are identified from at least one the extracted feature of the optimized feature set using at least one intelligence network 20 and a number of the anisocytosis in the detected cells (18) are determined based on a predefined feature value. Based on the above disclosed information, the control unit 12 detects a severity of at least one medical condition.

[0014] The above method is explained in detail. For better understanding of the invention, the device 10 is considered as the digital focusing device 10, the content of the sample 14 is a blood sample and the control unit captures multiple images of the blood sample 14 for further analysis. In the present invention, the control unit 12 is determining aniso-chromasity content in each of the cell in the given blood sample 14 of the patient using the above digital focusing device 10. Hemoglobin, a protein molecule, is responsible for transporting oxygen molecules from the lungs to different parts of the body for cellular metabolism and Carbon-di-oxide molecules (By-product of metabolism) from various cells to the lungs for purification. Hemoglobin estimation helps to calculate the amount of Hemoglobin molecules present in the sample analyzed which in turn helps the clinician to provide a differential diagnosis.

[0015] This method involves characterization of minute cellular traits at micron level requires crisp segmentation of cellular boundary & hemoglobinized zone extraction from a two-dimensional space followed by unique feature engineering. The method also explains identification of the different types of the red blood cells 18 in the blood sample 14 based on the cellular size and chromasity content of each red blood cell 18 in the given blood sample 14. It is to be noted that, the cellular size and chromasity content is obtained from the at least one feature value of the optimized feature set.

[0016] The blood on the sample 14 is smeared and is placed in the digital focusing device 10. The control unit 12 captures multiple images 15 of the blood sample 14 using the automated digital image acquisition unit 16 (e.g.: a camera) of the device 10. At least one image 15 comprises different cells 18 of the blood and the artefacts. The control unit 12 removes artefacts from the image 15 of the blood sample 14 using the image processing unit. The control unit 12 detects multiple cells of interest in the at least one image 15 and enhances each of the detected cell 18 using at least one image processing technique. The control unit 12 labels each of the detected cells 18 after enhancing each of the detected cell 18.
 

[0017] Using at least one image processing technique, the control unit 12 removes all the artefacts present in the image 15. 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 AI model, Machine learning technique, Deep learning technique and the like. The size of the blood cells is measured in micrometer scale. In order to match the standard measurement scale, the captured images 15 are calibrated to a micrometer scale by the control unit 12. The Image processing unit 16 modifies 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.

[0018] The cells of interest 18 with the enhanced edges are used for further processing in determining the cellular size and chromasity content of the red blood cells in the blood sample. Two levels of segmentation are performed to extract the cellular information and hemoglobinized zone/chromasity from the input signal. According to one embodiment of the invention, instead of addressing a specific problem like artefacts removal, uneven illumination, uneven contrast, out of focus or blurredness in at least one captured image, the image processing unit of the control unit 12 preserves and enhance the edges of the detected cells while suppressing the noises in the background. The image processing unit 16 converts 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 a background of the image 15. The Image processing unit 16 is made generic to adapt any magnification value with a configurable parameter. Two levels of segmentation is performed to extract the cellular information and hemoglobinized zone from the input signal.

[0019] The control unit 12 isolates the enhanced cells 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.

[0020] 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.

[0021] The control unit 12 upon categorizing the labelled cells 18 based on the extracted features, determines the count of the categorized red blood 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] The control unit 12 extracts multiple features from each of the identified labelled cell. For instance, the control unit 12 extracts more than 164 features related to each of the labelled cell from the captured image. It is to be noted that the labelled cell and the detected cell holds the same meaning. The control unit 12 identifies an optimized feature set from the extracted features. The classification accuracy depends on the efficiency of the features in differentiating the anomaly types. The selection of appropriate feature highly depends on the unique characterization of each anomaly from the others.

[0022] The optimized feature set is selected based on the morphological, geometrical features, morphology, cellular arrangement, color, texture and Inclusions was employed to build a cascaded intelligence (e.g.: AI) Architecture. Based on the targeted cellular anomalies, the control unit 10 divides the cells into microcytic normochromic, microcytic hypochromic, macrocytic normochromic, macrocytic hypochromic, normocytic normochromic, and normocytic hypochromic consist of varied cellular size and hemoglobin content. Hypochromic condition is further categorized into 1+, 2+, 3+.

[0023]The optimized feature set comprises the features like a cellular size/area, a pallor size/area, a kurtosis value, an energy value, difference pallor, rectangular difference pallor, axis percentage difference pallor, feret major difference pallor, feret minor difference pallor, Chroma decider pallor, circular area difference pallor, red center mean, blue center mean, green center mean, target blob, compactness pallor, form factor pallor, area, area pallor, red mean pallor, green mean pallor, blue mean pallor, compactness pallor, rectangularity pallor, elongation pallor, circularity pallor, aspect ratio pallor 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.

[0024] The control unit 12 identifies different types of red blood cells 18 and cellular size of the red blood cells 18 from at least one of the above-disclosed extracted features of the optimized set. The control unit 12 determines the number of each of the identified type of red blood cell 18 based on a predefined feature value. 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 a cellular size, and the predefined feature value is the cellular size of an ideal red blood cell.

[0025] It is to be noted that, at least one feature is not restricted to only cellular size and chromasity content, but can be of any other feature comprising Feret, Surface area, area and the like. The control unit 12 identifies aniso-chromasity from at least one of the extracted features using at least one intelligence network and determine a number of the aniso-chromasity cells in the detected cells 18 based on a predefined feature value. In the extracted features, highly significant latent features are selected while forming the optimized feature set. The blood sample to be analyzed is digitally converted into 2-D image data. Image processing techniques are applied on the acquired images to segment the area of interest (AOI) inclusive of blood cells, and other artefacts. The extracted cellular boundary is fed into a feature analyzer module of intelligence module 16.

[0026] The intelligence module 16 comprises features extracted by a statistical assessment of cellular arrangement, shape, size, color, and inclusions. Around 168 features are conceived and optimized for accurate classification of blood components at cellular level. The optimized feature set is formed based on Cellular Geometry, Morphology, Cellular Arrangement, Color, Texture and Inclusions was employed to build a Machine-learning hypothesis/intelligence neural network built/developed in an intelligence network (for instance an AI model, A DL model or a ML model). The control unit 12 will efficiently characterize the blood components into RBC, WBC, and Platelets. Characterized red cells are considered for aniso-chromaticity analysis. Based on the clinical knowledge on red cell, features were engineered equivalent to visual interpretation provided by pathologists & volumetric space analysis from 2-Dimensional space.

[0027] Parameters like cellular area, pallor area and ratio of cell/pallor metrics were selected to efficiently isolate the regions containing hemoglobin molecules in an erythrocyte. From the extracted regions, analysis on the cellular & pallor characteristics, target cellular anomalies are attained. Area, Roundness, Solidity, Aspect Ratio, Bounding Rectangle width, Bounding Rectangle Height, Rectangularity, Feret, Minimum Feret, Difference Pallor, Axis Percentage Difference Pallor, Area Pallor, Aspect Ratio Pallor etc., The optimized feature set comprises features extracted specific to characterization of aniso-chromasity which includes Area, Major Axis Length, Minor Axis length, Roundness, Solidity, Aspect Ratio, Bounding Rect width, Bounding Rect Height, Rectangularity, Feret, Minimum Feret and the like. The control unit 12 detects the number of anito-chromasity cells based on the total number of microcytic normochromic, microcytic hypochromic, macrocytic normochromic, macrocytic hypochromic, normocytic normochromic, and normocytic hypochromic in the detected cells 18.

[0028] The predefined feature value is stored in the control unit 12 of the device 10 during the calibration process. The control unit 12 compares the cellular size and chromasity content of the each of the red blood cell 18 present in the blood sample 14 with the predefined cellular size value. Based on the comparison result the control unit 12 identifies the types of red blood cells 18 as microcytic normochromic, microcytic hypochromic, macrocytic normochromic, macrocytic hypochromic, normocytic normochromic, and normocytic hypochromic

[0029] 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.

[0028] The present invention helps in avoiding the reagent cost used in cell counters, which are currently used for volumetric analysis of blood. With the above disclosed method, estimation of both volumetric indices/ count metrics and cellular morphological findings 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.

[0030] With the present invention disclosing a method of determining the at least one feature value and cellular variation values 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 (e.g.: 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 cellular variation from the two-dimensional vector.

[0031] 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. The above-disclosed optimized methodology manifests the two-dimensional digital space eligible for employing the clinically accepted red cell indices formula. 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 and cellular variation values in the form of a graphical representation.

[0032] 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
, C , Claims:We Claim:

1. A control unit (12) for determining Aniso-chromasity 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) and identify an ideal region in said captured images (15);
- calibrate said capture images (15) of said blood sample (14) to a micrometer scale;
- convert said at least one captured image (15) of 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) using at least one signal processing technique;
- characterized in that:
- extract at least one feature of said each cell (18) and identify an optimized feature set from extracted features for categorizing said detected cells (18), said optimized feature set is formed based on size and chromasity/hemoglobin content of said at least one blood cell (18);
- identify Aniso-chromasity cells from at least one said extracted feature of said optimized feature set using at least one intelligence network (20) and determine a number of said Aniso-chromasity cells 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 and a chromasity of each of said detected cells (18).

3. The control unit (12) as claimed in claim 1, wherein a two-level segmentation is performed on said detected multiple cells (18) to extract a cellular data and a hemoglobinized zone from said captured image (15).

4. 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.

5. The control unit (12) as claimed in claim 1, wherein said detected cells (18) are categorized into multiple groups comprising Microcytic Normochromic, Microcytic hypochromic, Macrocytic normochromic, Macrocytic Hypochromic, Normocytic Normochromic, and Normocytic hypochromic based on varied cellular size and hemoglobin content/chromasity.

6. The control unit (12) as claimed in claim 1, wherein an optimized feature set is formed based on cellular Geometry, morphology, cellular arrangement, color, texture to build said intelligence network in said intelligent module.

7. The control unit (12) as claimed in claim 6, wherein said optimized feature set if formed based on said at least one extracted feature comprising Area, Roundness, Solidity, Aspect Ratio, Bounding Rectangle width, Bounding Rectangle Height, Rectangularity, Feret, Minimum Feret, Difference Pallor, Axis Percentage Difference Pallor, Area Pallor, Aspect Ratio Pallor

8. 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).

9. A method of determining anisocytosis of 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) and identify an ideal region in said captured images (15) by a control unit (12);
- calibrating said capture images (15) of said blood sample (14) to a micrometer scale;
- 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 enhance each of said detected cell (18) using at least one signal processing technique;
characterized in that:
- extracting at least one feature of said each cell (18) and identify an optimized feature set from extracted features for categorizing said detected cells (18), said optimized feature set is formed based on size and chromasity/hemoglobin content of said at least one blood cell (18);
- identifying anisocytosis from at least one said extracted feature of said optimized feature set using at least one intelligence network (20) and determine a number of said aniso-chromasity cells in said detected cells (18) based on a predefined feature value.