Extraction and quantification techniques for abnormality detection from medical thermographs in human

Nowadays Infrared thermography is 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 analyzes the performance of three feature extraction algorithms used for abnormality detection. The developed techniques are applied on Arthritis and stress fracture thermographs.