A super-Gaussian discrete cosine transform filter for the detection of pathologies in retinographies

1 Abstract— This paper presents a novel method using discrete transforms to segment exudates and blood vessels in retinographies. Illumination correction is previously done based on a homomorphic filter due to uneven illumination in retinographies. To distinguish foreground objects from the background, we propose a family of super-Gaussian filters in the discrete cosine transform domain and we analyze the difference between Butterworth and super-Gaussian band-pass filters. The filters are applied on the green channel since it has the relevant information to segment pathologies. To detect exudates in the filtered image, a gamma correction is first applied to enhance foreground object. Then, Otsu’s global thresholding method is used, after which, a masking operation over the effective area of retinographies is performed to obtain final segmentation. For blood vessels, the negative of the filtered image is first calculated, and then a median filter is applied to reduce noise and artifacts followed by gamma correction. Again, Otsu’s global thresholding method is applied for image binarization. Next a morphological closing operation is employed and a logical masking operation gives the resulting segmentation. Illustrative examples taken from a worldwide free clinical database are included to demonstrate the capability of the proposed segmentation method.