Comparison of boundary detection techniques to improve image analysis in medical thermography

In digital imaging, poor contrast between the target and its background can affect the extraction of the object of interest and increase the time used in its analysis. Medical thermal imaging requires the correct interpretation of the thermal values obtained from the region of interest. In this investigation, a subjective and objective comparison of currently available outlining techniques is performed to determine the optimal method. Results indicate that probability-based operators produce the best outcome especially after pre-processing with a noise removal filter. The findings of this study suggest that probability-based edge detection techniques in combination with homomorphic filtering and limited post-processing provide initial estimate delineations of areas. These delineations are of sufficient quality for subsequent automatic or semiautomatic post-processing so that a maximum of the original information inside the regions is preserved without loss or distortion of data.