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%.