A Comparitive study in detection of Abnormality of human Medical Thermographs using color image segmentation
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Abstract
Infrared thermography, non-contact, non-invasive technique is widely accepted as a medical diagnostic tool. An IR camera captures heat variations from the skin and maps into thermographs. Thermographs are acquired for the whole body or the region of interest. Thermographs either gray scale or pseudo color are processed for abnormality detection and quantification. However temperature variations are not normally visible to naked eye. Hence it is necessary to develop and analyze the feature extraction algorithms for abnormality detection. This paper gives a comparative study in the analysis of color image segmentation in detection of abnormality of human medical thermographs .Three feature extraction algorithms(Euclidean distance,Manhattan,Minkowski) are compared and the optimal one is predicted. Here, the cases considered are Stress fracture and Arthritis.
Keywords: Thermograph, Arthritis, and Stress fracture, Manhattan, minkowski.
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