Performance evaluation of breast lesion detection systems with expert delineations: a comparative investigation on mammographic images

Performance of computerized diagnostic systems yearning to be approved by medical regulatory bodies must meet the expectations of human experts. Highly accurate lesion segmentation techniques have thus turned out to be an essential part for clinical acceptability of mammography based computer-aided diagnosis systems. The objective of this study is to evaluate the performance of six popular breast tumor detection techniques with manual delineations provided by two experienced radiologists on the mammographic images. In our study, 20 mammographic images from the mini-MIAS database are utilized. For the analysis, input mammographic images are first manually cropped to generate the region of interest (ROI). The ROI images are then pre-processed and segmentation is performed using different techniques, namely: expected maximization, K-means, Fuzzy c-Means (FCM), multilevel thresholding, region growing, and particle swarm optimization. The results were compared against the manual tracings. Among the other five segmentation techniques, FCM achieves the highest Jaccard Index (0.73 ± 0.06) and Dice Similarity Coefficient (0.82 ± 0.08) values. Statistical analysis (t-test, Mann Whitney U test, Wilcoxon test, Chi-Square test, and Kolmogorov–Smirnov test) and graphical analysis (Bland Altman and Regression plots) further prove the stability and reliability of the segmentation methods. Segmentation using FCM demonstrates the most accurate results and can be employed for the detection of breast cancer in the mammographic images. Further, it is concluded that computer-aided lesion detection systems can be used to assist Radiologists in routine clinical practice for the detection of breast tumors in mammographic images.