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A major concern for women’s health in today’s age is breast cancer. Thermography is an upcoming technology that is painless, private and relatively cheap to screen breast health. The presence of asymmetric hot blood vessel patterns in the breast thermogram portrays an abnormality. Proper extraction of these hotspots from the breast can help build a reliable breast cancer detection system and play a critical role in knowing the extent of spread of the cancer. In this work, segmentation of mammary thermograms is performed to extract the hottest blood vessel patterns using five state-of-the-art image segmentation methods. The proposed work is tested on the benchmark breast thermogram public dataset available at the Visual Lab. The most vascularised areas of each breast are extracted, and their areas are matched with the patches in the ground truth images. Based on metrics like DICE similarity coefficient and Jaccard index, it is concluded that particle swarm optimisation (PSO) algorithm and multi-seed region-growing technique provide the best segmentation results that are closer to the ground truth images. This indicates that infrared imaging is a promising tool that can act as a catalyst in predicting breast anomalies.

Extraction of hottest blood vessels from breast thermograms using state-of-the-art image segmentation methods