Mesh‐free based variational level set evolution for breast region segmentation and abnormality detection using mammograms

• Published in: International journal for numerical methods in biomedical engineering Vol. 34; no. 1
• Main Authors: Kashyap, Kanchan L., Bajpai, Manish K., Khanna, Pritee, Giakos, George
• Format: Journal Article
• Language: English
• Published: England Wiley Subscription Services, Inc 01.01.2018
• Subjects: Accuracy; Algorithms; Basis functions; Breast; Breast - anatomy & histology; Breast - diagnostic imaging; Breast Neoplasms - diagnosis; Clustering; Computation; Digital imaging; Evolution; Feature extraction; Female; Filtration; Finite difference method; fuzzy c‐means; Humans; Image analysis; Image detection; Image Interpretation, Computer-Assisted; Image processing; Image segmentation; Kernel functions; LBP; Mammography; Masking; median filtering; Meshless methods; mesh‐free; Multilayer perceptrons; Partial differential equations; Preprocessing; Radial basis function; Sensitivity; Support vector machines; Texture; unsharp masking; median filtering; LBP; mammography; mesh-free; fuzzy c-means; unsharp masking
• ISSN: 2040-7939; 2040-7947; 2040-7947
• DOI: 10.1002/cnm.2907

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. An automatic abnormality detection algorithm using mammographic images is proposed. Variational level set method is used for breast region extraction. The evolution of the level set method is done by applying radial basis function. Unsharp masking and median filtering is used for mammogram enhancement. Suspicious abnormal regions are segmented by applying FCM clustering. Texture features are extracted from the suspicious regions by computing LBP and DRLBP. Finally, suspicious regions are classified as normal or abnormal regions by means of SVM classifier.