System and method of classification of biological particles

1. A method of determining a classification of a particle in a biological sample comprising:

acquiring an image of the particle;

receiving, at a processor system, the image of the particle;

executing, using the processor system, computer executable code stored on a non-transitory computer readable medium, the computer executable code comprising instructions that when executed on the processor system, cause the processor system to:

perform an extraction routine that comprises extracting a plurality of features from the image based on content and location of pixels of the image, the extracting comprising:

defining a center of the image;

normalizing the image to a size of a first mask;

applying the first mask substantially to the center of the normalized image;

acquiring a first set of pixels from the image based on applying the first mask;

applying a second mask to the image, wherein the application of each mask reveals different pixels concentrically outside of the center of the image;

acquiring a second set of pixels based on applying the second mask; and

determining the plurality of features from the first and second set of pixels;

selecting a subset of the extracted features;

performing a mapping routine that comprises mapping the subset of the extracted features into a classifier architecture, the mapping comprising:

using a first level model to compare the subset of the extracted features to a previously stored data set;

identifying a preliminary classification based on the comparison of the subset of the extracted features to the previously stored data set;

calculating a probability value that the preliminary classification is correct using the first level model; and

determining the classification based on the preliminary classification when the probability value is at or above a threshold value;

wherein the method uses a digital microscope camera.

2. The method of claim 1 , wherein the first mask and the second mask are circular or ring-shaped.

3. The method of claim 1 , wherein the first mask and the second mask are applied in a predetermined order, or a combination thereof.

4. The method of claim 1 , wherein the extracting comprises clustering the first set of pixels into a group, creating a color palette from the clustered group of pixels, determining a label for the image based in part on the color palette, or any combination thereof.

5. The method of claim 1 , wherein the extracting comprises normalizing the first mask to a unit magnitude, using a chosen color space, including red-green-blue (RGB) hue-saturation-value (HSV), hue-saturation-lightness (HSL), or hue-saturation-brightness (HSB), or any combination thereof.

6. The method of claim 1 , wherein the subset of the extracted features comprises training features, validation features, or testing features.

7. The method of claim 1 , wherein the subset of the extracted features is mapped into a cascade classifier architecture.

8. The method of claim 1 , wherein the first level model is a machine learning model.

9. The method of claim 1 , wherein the mapping further comprises:

using a second level model to determine the particle classification when the probability value is below the threshold value.

10. The method of claim 9 , wherein the second level model is a machine learning model.

11. The method of claim 9 , wherein using the second level model further comprises:

receiving the probability value at the second level model;

creating a sorted list of values according to a classification performance in relation to a particle category;

combining the probability value and the sorted list to create a second level probability value;

using the first level probability value and the second level probability value to determine the particle classification.

12. The method of claim 1 , wherein the particle comprises a member selected from the group consisting of a neutrophil, a lymphocyte, a monocyte, an eosinophil, a basophil, an immature white blood cell, a reticulocyte, a nucleated red blood cell, an erythrocyte, an epithelial cell, a bacterium, a yeast, or a parasite.

13. A system of determining a classification of a particle in a biological sample comprising, the system comprising:

a processor and a computer-readable storage medium coupled to the processor, the computer readable storage medium comprising code executable by the processor for implementing the method of claim 1 .

14. The system of claim 13 , wherein the system uses a digital microscope camera.

15. A non-transitory computer-readable storage medium including program instructions executable by one or more processors that, when executed, cause the one or more processors to perform operations, the operations comprising a method of determining a classification of a particle in a biological sample, the method comprising:

acquiring an image of the particle;

receiving, at a processor system, the image of the particle;

executing, using the processor system, computer executable code stored on a non-transitory computer readable medium, the computer executable code comprising instructions that when executed on the processor system, cause the processor system to:

perform an extraction routine that comprises extracting a plurality of features from the image based on content and location of pixels of the image, the extracting comprising:

defining a center of the image;

normalizing the image to a size of a first mask;

applying the first mask substantially to the center of the normalized image;

acquiring a first set of pixels from the image based on applying the first mask;

applying a second mask to the image, wherein the application of each mask reveals different pixels concentrically outside of the center of the image;

acquiring a second set of pixels based on applying the second mask; and

determining the plurality of features from the first and second set of pixels;

selecting a subset of the extracted features;

performing a mapping routine that comprises mapping the subset of the extracted features into a classifier architecture, the mapping comprising:

using a first level model to compare the subset of the extracted features to a previously stored data set;

identifying a preliminary classification based on the comparison of the subset of the extracted features to the previously stored data set;

calculating a probability value that the preliminary classification is correct using the first level model; and

determining the classification based on the preliminary classification when the probability value is at or above a threshold value.