Automatic Breast Boundary Segmentation of Mammograms
R. R. Nanayakkara1, Y. P. R. D. Yapa2, P. B. Hevawithana3, P. Wijekoon4
1R. R. Nanayakkara, Postgraduate Institute of Science, University of Peradeniya, Sri Lanka.
2Y. P. R. D. Yapa, Department of Statistics & Computer Science, Faculty of Science, University of Peradeniya, Sri Lanka.
3P. B. Hevawithana, Department of Radiology, Faculty of Medicine, University of Peradeniya, Sri Lanka.
4P. Wijekoon, Department of Statistics & Computer Science, Faculty of Science, University of Peradeniya, Sri Lanka.
Manuscript received on February 19, 2015. | Revised Manuscript received on February 29, 2015. | Manuscript published on March 05, 2015. | PP: 97-101 | Volume-5 Issue-1, March 2015. | Retrieval Number: A2540035115/2015©BEIESP
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©The Authors. Published By: Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/
Abstract: Accurate breast boundary estimation and segmentation of breast tissue region from the background of the mammogram image is an important pre-processing task in computer-aided diagnosis of breast cancer. This paper presents an automated system to estimate skin-line and breast segmentation. The proposed method is based on automatic seed region selection, modified fast marching algorithm to propagate the seed region and automatic boundary point selection with intensity gradient information to initial boundary estimation and morphological operators to final boundary estimation and breast tissue region segmentation. Performance of the proposed method was tested by using 136 mammogram images with all types of breast tissues taken from mini-MIAS database. The results obtained from the experimental evaluation indicate that the sensitivity of this algorithm is 99.2% of the ground truth breast region and accuracy of the segmentation is 99.0%. By analyzing the results we can conclude that this system is capable of estimate the breast boundary and segment the breast area from background for all three types of breast tissues with high accuracy level.
Keywords: Breast Cancer, Mathematical Morphology, Modified Fast Marching Algorithm.