A vascular network matching in dynamic thermography for breast cancer detection

Because of limitation of human visual system, computer-aided diagnosis has been playing an important role in early breast cancer detection. Clinical interpretation of dynamic breast thermograms first depends on the similarity analysis between vascular pattern in thermal images before and after applying a cold challenge. To analyse the thermal images for cancer detection purposes in dynamic breast thermography, a new approach is proposed. First, the breast region was delineated from the background using a connected component method. Then, the blood vessels presented on the segmented breast region were extracted using image morphology. The branching points of the skeletonised vascular network are referred to as thermal minutia points (TMPs). Similar to fingerprints, these feature points are used as a geometric representation of the shape of vein patterns during the classification stage. Local and global structures of TMPs which are extracted from a cold-challenged image are matched with those of baseline image. The experimental results showed that the algorithm reaches 86% of the sensitivity and 61% specificity from 50 thermograms (25 normal and 25 cancerous). The mean of matching scores in malignant and benign cases are 0.78 and 0.3, respectively. According to the results the proposed approach is promising for detection of breast abnormality. More importantly, the results indicated the possibility of this framework in breast cancer detection so as to open a proper way for further methodological and experimental research in this area.