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Hybrid Active Contour Mammographic Mass Segmentation and Classification

K. Yuvaraj*, U. S. Ragupathy

Department of Electronics and Instrumentation Engineering, Kongu Engineering College Perundurai, Erode, 638060, India

* Corresponding Authors: K. Yuvaraj. Email: email,email

Computer Systems Science and Engineering 2022, 40(3), 823-834.


This research implements a novel segmentation of mammographic mass. Three methods are proposed, namely, segmentation of mass based on iterative active contour, automatic region growing, and fully automatic mask selection-based active contour techniques. In the first method, iterative threshold is performed for manual cropped preprocessed image, and active contour is applied thereafter. To overcome manual cropping in the second method, an automatic seed selection followed by region growing is performed. Given that the result is only a few images owing to over segmentation, the third method uses a fully automatic active contour. Results of the segmentation techniques are compared with the manual markup by experts, specifically by taking the difference in their mean values. Accordingly, the difference in the mean value of the third method is 1.0853, which indicates the closeness of the segmentation. Moreover, the proposed method is compared with the existing fuzzy C means and level set methods. The automatic mass segmentation based on active contour technique results in segmentation with high accuracy. By using adaptive neuro fuzzy inference system, classification is done and results in a sensitivity of 94.73%, accuracy of 93.93%, and Mathew’s correlation coefficient (MCC) of 0.876.


Cite This Article

K. Yuvaraj and U. S. Ragupathy, "Hybrid active contour mammographic mass segmentation and classification," Computer Systems Science and Engineering, vol. 40, no.3, pp. 823–834, 2022.

cc This work is licensed under a Creative Commons Attribution 4.0 International License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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