Abnormality Detection from Medical Thermographs in Human Using Euclidean Distance Based Color Image Segmentation

Infrared thermography is recently widely accepted as a medical diagnostic tool. Thermographs are acquired for the whole body or the region of interest. Thermographs are processed for abnormality detection and quantification. As temperature variations are not normally visible to naked eye it is necessary to develop and analyze the feature extraction algorithms for abnormality detection. This paper proposes Euclidean distance based color image segmentation algorithm for abnormality extraction. Arthritis and stress fracture thermographs are considered.