Color Medical Image Edge Detection based on Higher Dimensional Fourier Transforms Applied in Diabetic Retinopathy Studies

Various edge detection techniques for color images that have been proposed in the last two decades showed that color images contain 10% additional edge information as compared to their gray scale counterparts. For color image edge detection, the traditional methods used for grayscale images are usually extended and applied to the three-color channels separately. This leads to lose the intrinsic inter-correlation information embedded in color image components in addition to computational complexity incurred. Efficient and accurate edge detection leads to increased performance of subsequent image processing and analysis techniques including image segmentation and quantification. In this thesis, an edge detection algorithm has been proposed that treats color value triplets as vectors based on higher dimensional algebra. A human perception-based color space has been used due to its importance in color image edge detection. The trinion based algorithm has provided an efficient method to represent the color information vectorally. Color edge features are extracted based on a second order statistical technique using the weibull distribution method. A suitable color space transformation and a way of extracting robust higher order features are included in the method. Performance of the proposed scheme is compared against classical and other vectorial approaches which have been proposed in the literature based on objective criteria. Results showed that the proposed approach wins the other techniques available in the literature. Application of the proposed method has been shown in edge detection applied on color images taken from patients treated for diabetic retinopathy acquired from publicly available databases and St. Paul's Hospital Millennium Medical College. The algorithm performs well in detecting exudates, hemorrhages, optical disc and blood vessels.