Parallel medical image analysis for diabetic diagnosis

This paper aims to investigate the characteristics of medical images. A novel diagnostic scheme to develop quantitative indexes of diabetes is introduced in this paper. To achieve a better image-processing result, an appropriate image-processing algorithm is adopted in this work. The computation time increases as the image size grows. Fortunately, the computation can be partitioned and performed in parallel in a high performance system and a grid computing system can be a good infrastructure for it. An important fractal feature introduced in this paper is the measure of lacunarity, which describes the characteristics of fractals that have the same fractal dimension but different appearances. In this study, the measure of lacunarity and the moment of inertia have shown their significance in the classification of diabetes and are adequate for use as quantitative indexes.