Detection and phenotyping of retinal disease using AM-FM processing for feature extraction

We present the application of an Amplitude-Modulation Frequency-Modulation (AM-FM) method for extracting potentially relevant features towards the classification of diseased retinas from healthy retinas. In terms of AM-FM features, we use histograms of the instantaneous amplitude, the angle of the instantaneous frequency and the magnitude of the instantaneous frequency extracted over different frequency scales. To classify the AM-FM features, we use a combination of a clustering method and Partial Least Squares (PLS). Using 18 images from each of the four risk levels, three experiments were performed to test the algorithm's ability to differentiate the controls (Risk 0) from each of the three levels of pathology, i.e. Risk 1, Risk 2, and Risk 3. For Risk 0 versus Risk 3 an area under the receiver operating system (AROC) of 0.99 was achieved with a best sensitivity of 100% and a specificity of 95%. For Risk 0 versus Risk 2, the AROC was 0.96 with 94% sensitivity and 85% specificity. For Risk 0 versus Risk 1, the AROC was 0.93 and a sensitivity/specificity of 94%/67%.