Echocardiogram view classification using edge filtered scale-invariant motion features

1. A computer program product for echocardiogram view classification, the computer program product comprising a non-transitory computer readable storage medium having computer readable program code executable by a computer device to perform a method comprising:

obtaining a plurality of video images of a subject;

aligning the plurality of images;

using the aligned images to generate a motion magnitude image;

filtering the motion magnitude image using an edge map;

detecting features on the motion magnitude image, discarding those features which do not lie in the neighborhood of edges;

encoding the remaining features by:

generating, x, y image coordinates for a feature point;

generating a motion magnitude histogram in a window around the feature point; and

generating a histogram of edge intensity values near the feature point; and

using the encoded features to classify the video images of the subject into a predetermined classification.

2. The computer program product of claim 1 wherein the classifying the video images comprises using a vocabulary-based Pyramid Matching Kernel based Support Vector Machine.

3. The computer program product of claim 1 wherein the aligning comprises using affine transformation.

4. The computer program product of claim 1 wherein motion magnitude image is generated using Demons algorithm.

5. The computer program product of claim 1 wherein the video images are echocardiograms.

6. A computer program product for echocardiogram view classification, the computer program product comprising a non-transitory computer readable storage medium having computer readable program code executable by a computer device to perform a method comprising:

representing each image from the echocardiogram video by a set of salient features;

modifying the image to produce an edge filtered motion magnitude image;

filtering the motion magnitude image using an edge map;

detecting features on the motion magnitude image, discarding those features which do not lie in the neighborhood of edges;

locating the features at scale invariant points in the edge filtered motion magnitude image; and

encoding the edge filtered motion magnitude image by:

generating, x, y image coordinates of a feature point;

generating a motion magnitude histogram in a window around the feature point; and

generating a histogram of edge intensity values near the feature point.

7. The computer program product of claim 6 wherein the encoding comprises encoding the edge filtered motion magnitude image by using spatial information about the image.

8. The computer program product of claim 6 wherein the encoding comprises encoding the edge filtered motion magnitude image by using textual information about the image.

9. The computer program product of claim 6 wherein the encoding comprises encoding the edge filtered motion magnitude image by using kinetic information about the image.

10. The computer program product of claim 6 wherein the locating comprises identifying the scale invariant interest points in motion magnitude that are also close to edges in the edge filtered motion magnitude image.

11. The computer program product of claim 6 wherein the representing comprises representing the image by at least one position (x, y).

12. The computer program product of claim 6 wherein the classifying comprises using a vocabulary-based Pyramid Matching Kernel based Support Vector Machine.

13. A system for processing a plurality of video images of a subject comprising:

a processor configured to:

align the plurality of images;

use the aligned images to generate a motion magnitude image;

filter the motion magnitude image using an edge map;

detect features on the motion magnitude image, discard those features which do not lie in the neighborhood of edges;

encode the remaining features by:

generating, x, y image coordinates of a feature point;

generating a motion magnitude histogram in a window around the feature point; and

generating a histogram of edge intensity values near the feature point; and

use the encoded features to classify the video images of the subject into a predetermined classification.

14. The system of claim 13 wherein the processor is further configured to classify using a vocabulary-based Pyramid Matching Kernel based Support Vector Machine.

15. The system of claim 13 wherein the processor is further configured to align using affine transformation.

16. The system of claim 13 wherein the processor is further configured to generate the motion magnitude image using Demons algorithm.