System and method of classification of biological particles
A method and system for classification of cells and particles in a biological sample using an automated image-based feature extraction and classification architecture. A method operates by applying a mask or series of masks to an image, extracting features from the unmasked portions of the image based on the content and location of colored pixels, selecting a subset of the extracted features, and mapping the subset of the extracted features into a classifier architecture. In a majority of cases, the first level model architecture provides an accurate identification of the cell or particle. In a minority of cases, the classification of the cell or particle requires a second level step requiring the use of numerical or categorical values from the first level in combination with a second level model.
1 . A method of determining a classification of a particle in a biological sample comprising:
acquiring an image of the particle with a digital camera;
receiving the image of the particle at a processor system; and
utilizing the processor system to execute 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:
input a first set of features into a first classifier, the first classifier configured to provide a first label among a set of labels and a first probability value associated with the first label, and
input a second set of features into a second classifier if the first probability value is below a first threshold, the second classifier configured to provide a second label among the set of labels.
2 . The method of claim 1 , wherein the second label is utilized if the second label is at or above a second threshold.
3 . The method of claim 2 , wherein the first and second thresholds are different numerical amounts.
4 . The method of claim 1 , wherein inputting the first set of features into a first classifier comprises applying a first mask to the image.
5 . The method of claim 4 , wherein inputting the first set of features into a second classifier comprises applying a second mask to the image.
6 . The method of claim 5 , wherein the first mask and the second mask are circular or ring-shaped.
7 . The method of claim 5 , wherein an application of each mask reveals different pixels concentrically outside a center of the image.
8 . The method of claim 7 , further comprising clustering a 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.
9 . The method of claim 4 , further comprising normalizing the image to a size of the first mask.
10 . The method of claim 1 , wherein the first set of features and the second set of features comprise different sets of features.
11 . A method of determining a classification of a particle in a biological sample comprising:
acquiring an image of the particle with a digital camera;
receiving the image of the particle at a processor system;
utilizing the processor system to execute 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:
input a first set of features into a first classifier, the first classifier configured to provide a first label among a set of labels and a first probability value associated with the first label, and
input a second set of features into a second classifier, the second classifier configured to provide a second label, and
apply the first label if the first probability value is at or above a first threshold, and apply the second label if the second label is at or above a second threshold among the set of labels.
12 . The method of claim 11 , wherein the first and second thresholds are different numerical amounts.
13 . The method of claim 11 , wherein inputting the first set of features into a first classifier comprises applying a first mask to the image.
14 . The method of claim 13 , further comprising 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.
15 . The method of claim 13 , wherein inputting the first set of features into a second classifier comprises applying a second mask to the image.
16 . The method of claim 15 , wherein the first mask and the second mask are circular or ring-shaped.
17 . The method of claim 15 , wherein an application of each mask reveals different pixels concentrically outside a center of the image.
18 . The method of claim 13 , further comprising normalizing the image to a size of the first mask.
19 . The method of claim 18 , further comprising clustering a 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.
20 . The method of claim 11 , wherein the first set of features and the second set of features comprise different sets of features.