Performance Evaluation of Image Processing Algorithms for Automatic Detection and Quantification of Abnormality in Medical Thermograms

Infrared Imaging is a totally non-invasive, non-contact, medical imaging procedure for detecting and monitoring various diseases and physical injuries. It diagnoses abnormal areas in the body by measuring heat emitted from the skin surface and expressing the measurements into a thermal map called thermograms. Abnormalities manifest as hot spots in thermograms. Thermologist has to interpret thermograms, identify and quantify the abnormality. To overcome the subjectivity involved in human interpretation, it is desirable to develop an image-processing algorithm for automatic interpretation of thermograms. Two different algorithms namely conventional image processing algorithm and region growing algorithm are proposed. Conventional algorithm involves thermogram acquisition, enhancement, segmentation, morphological processing and quantitative characterization. Region growing algorithm involves selecting a seed pixel and appending the similar pixels. The paper also compares the performance of these algorithms in terms of parameter dependency, image specificity and time consumption.