Classification of severity of pathological condition using hybrid image representation

1. A method comprising:

obtaining, at a digital image processor, at least one image from which severity of a given pathological condition presented in the at least one image is to be classified; and

generating, at the digital image processor, a hybrid image representation of the at least one obtained image, wherein the hybrid image representation comprises a concatenation of a discriminative pathology histogram, a generative pathology histogram, and a fully connected representation of a trained baseline convolutional neural network, and further wherein the discriminative pathology histogram and the generative pathology histogram are each computed using a word occurrence count vector modeling process;

training a classifier using the hybrid image representation;

classifying the severity of the given pathological condition presented in the at least one image based on the trained classifier; and

wherein the steps of the method are performed by a computing device comprising a processor coupled to a memory.

2. The method of claim 1 , wherein the given pathological condition presented in the at least one obtained image comprises diabetic retinopathy.

3. The method of claim 2 , wherein the classifier classifies the diabetic retinopathy in the at least one obtained image into one of a plurality of diabetic retinopathy severity classifications.

4. The method of claim 1 , wherein the classifier comprises a random forest classifier.

5. The method of claim 1 wherein the discriminative pathology histogram is computed utilizing a random forest dictionary and a linear support vector machine classifier and the generative pathology histogram is computed utilizing a Gaussian Mixture Model dictionary and at least one Fisher vector.

6. The method of claim 1 , wherein generating the hybrid image representation further comprises extracting a set of patches from the at least one obtained image.

7. The method of claim 6 , wherein generating the hybrid image representation further comprises passing the extracted set of patches through the trained baseline convolutional neural network to obtain a fully connected representation of the extracted set of patches in the form of a set of feature descriptors for each patch.

8. The method of claim 7 , wherein generating the hybrid image representation further comprises encoding the set of feature descriptors for each patch using the classifier.

9. The method of claim 8 , wherein the set of feature descriptors for each patch is encoded using the word occurrence count vector modeling process.

10. The method of claim 8 , wherein generating the hybrid image representation further comprises clustering the encoded feature descriptors in each patch.

11. The method of claim 10 , wherein generating the hybrid image representation further comprises computing the discriminative pathology histogram from the clustered feature descriptors.

12. An apparatus, comprising:

at least one processor; and

a memory operatively coupled to the processor and configured to:

obtain, at a digital image processor, at least one image from which severity of a given pathological condition presented in the at least one image is to be classified;

generate, at the digital image processor, a hybrid image representation of the at least one obtained image, wherein the hybrid image representation comprises a concatenation of a discriminative pathology histogram, a generative pathology histogram, and a fully connected representation of a trained baseline convolutional neural network, and further wherein the discriminative pathology histogram and the generative pathology histogram are each computed using a word occurrence count vector modeling process

train a classifier using the hybrid image representation; and

classify the severity of the given pathological condition presented in the at least one image based on the trained classifier.

13. A computer program product comprising a processor-readable storage medium having encoded therein executable code of one or more software programs, wherein the one or more software programs when executed by the one or more processors implement steps of:

obtaining, at a digital image processor, at least one image from which severity of a given pathological condition presented in the at least one image is to be classified;

generating, at the digital image processor, a hybrid image representation of the at least one obtained image, wherein the hybrid image representation comprises a concatenation of a discriminative pathology histogram, a generative pathology histogram, and a fully connected representation of a trained baseline convolutional neural network, and further wherein the discriminative pathology histogram and the generative pathology histogram are each computed using a word occurrence count vector modeling process;

training a classifier using the hybrid image representation; and

classifying the severity of the given pathological condition presented in the at least one image based on the trained classifier.

14. The computer program product of claim 13 , wherein the given pathological condition presented in the at least one obtained image comprises diabetic retinopathy, and wherein the classifier classifies the diabetic retinopathy in the at least one obtained image into one of a plurality of diabetic retinopathy severity classifications.

15. The computer program product of claim 13 , wherein the classifier comprises a random forest classifier.

16. The computer program product of claim 13 , wherein the discriminative pathology histogram is computed utilizing a random forest dictionary and a linear support vector machine classifier and the generative pathology histogram is computed utilizing a Gaussian Mixture Model dictionary and at least one Fisher vector.

17. The computer program product of claim 13 , wherein generating the hybrid image representation further comprises:

extracting a set of patches from the at least one obtained image;

passing the extracted set of patches through the trained baseline convolutional neural network to obtain a fully connected representation of the extracted set of patches in the form of a set of feature descriptors for each patch; and

encoding the set of feature descriptors for each patch using the classifier.

18. The computer program product of claim 17 , wherein the set of feature descriptors for each patch is encoded using the word occurrence count vector modeling process.

19. The computer program product of claim 17 , wherein generating the hybrid image representation further comprises clustering the encoded feature descriptors in each patch.

20. The computer program product of claim 19 , wherein generating the hybrid image representation further comprises computing the discriminative pathology histogram from the clustered feature descriptors.