On the Use of Active Contour Models for Breast Cancer Lesion Segmentation

Early detection and classification of mass lesion in mammograms constitute an essential step to decrease patient mortality caused by breast cancer, because it is possible to analyze the initial stages of cancer before it appears clinically. A well-performed segmentation task allows the lesion to be separated from the background to improve its shape-based classification. However, it is a challenging task because of its similarity to surrounding tissue. Therefore, we propose exploring two active contour models, Geodesic and Chan-Vese, to maximize the performance of mass segmentation in mammography images. Both models were optimized in terms of initialization radius and number of iterations used and validated on an experimental dataset containing 115 images with mass lesions. The best-selected Chan-Vese model, with a radius of 50 pixels and 436 iterations, outperformed the best Geodesic model, attaining a mean Dice score of 0.812 versus 0.558. This result highlighted the successful performance of the Chan-Vese model in segmenting mass lesions from different images. It also demonstrated the Geodesic model's tendency to get stuck in local minimums. The Median and CLAHE filters were crucial to improving the boundary quality of the mass lesion prior to the segmentation step. Also, the proposed method was able to successfully segment complex and irregular mass shapes, which is considered an essential result for cancer classification with respect to the degree of malignancy.