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Mesh‐free based variational level set evolution for breast region segmentation and abnormality detection using mammograms
Author(s) -
Kashyap Kanchan L.,
Bajpai Manish K.,
Khanna Pritee,
Giakos George
Publication year - 2018
Publication title -
international journal for numerical methods in biomedical engineering
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.741
H-Index - 63
eISSN - 2040-7947
pISSN - 2040-7939
DOI - 10.1002/cnm.2907
Subject(s) - artificial intelligence , pattern recognition (psychology) , radial basis function , level set (data structures) , computer science , unsharp masking , mammography , kernel (algebra) , radial basis function kernel , segmentation , image segmentation , mathematics , computer vision , support vector machine , image processing , image (mathematics) , kernel method , artificial neural network , medicine , cancer , combinatorics , breast cancer
Automatic segmentation of abnormal region is a crucial task in computer‐aided detection system using mammograms. In this work, an automatic abnormality detection algorithm using mammographic images is proposed. In the preprocessing step, partial differential equation–based variational level set method is used for breast region extraction. The evolution of the level set method is done by applying mesh‐free–based radial basis function (RBF). The limitation of mesh‐based approach is removed by using mesh‐free–based RBF method. The evolution of variational level set function is also done by mesh‐based finite difference method for comparison purpose. Unsharp masking and median filtering is used for mammogram enhancement. Suspicious abnormal regions are segmented by applying fuzzy c‐means clustering. Texture features are extracted from the segmented suspicious regions by computing local binary pattern and dominated rotated local binary pattern (DRLBP). Finally, suspicious regions are classified as normal or abnormal regions by means of support vector machine with linear, multilayer perceptron, radial basis, and polynomial kernel function. The algorithm is validated on 322 sample mammograms of mammographic image analysis society (MIAS) and 500 mammograms from digital database for screening mammography (DDSM) datasets. Proficiency of the algorithm is quantified by using sensitivity, specificity, and accuracy. The highest sensitivity, specificity, and accuracy of 93.96%, 95.01%, and 94.48%, respectively, are obtained on MIAS dataset using DRLBP feature with RBF kernel function. Whereas, the highest 92.31% sensitivity, 98.45% specificity, and 96.21% accuracy are achieved on DDSM dataset using DRLBP feature with RBF kernel function.