Analysis of 2D singularities for mammographic mass classification Journal Article


Authors: Rabidas, R.; Chakraborty, J.; Midya, A.
Article Title: Analysis of 2D singularities for mammographic mass classification
Abstract: Masses are one of the prevalent early signs of breast cancer, visible in mammogram. However, its variation in shape, size, and appearance often creates hazards in proper diagnosis of mammographic masses. This study analyses the 2D singularities of masses and their surrounding regions with Ripplet-II transform to classify them as benign and malignant. Since benign and malignant masses may change the orientation patterns of normal breast tissues differently, several textural features including Ripplet-II coefficients and statistical co-variates, derived from the Ripplet-II transformed images, are extracted to quantify the texture information of mammographic regions. The important features are then selected using stepwise logistic regression technique and evaluated using linear discriminant analysis and support vector machine with a ten-fold crossvalidation. The best performance in terms of the area under the receiver operating characteristic curve of 0.91 ± 0.01 and 0.83 ± 0.01 and accuracy of 87.28 ± 0.02 and 75.60 ± 0.01 are obtained with the proposed method while experimenting with 58 images from the mini-MIAS and 200 images from the Digital Database for Screening Mammography database, respectively. © 2016, The Institution of Engineering and Technology.
Keywords: mammography; medical imaging; discriminant analysis; receiver operating characteristic curves; logistic regressions; image retrieval; digital database for screening mammographies; important features; linear discriminant analysis; mammographic mass; surrounding regions; texture information
Journal Title: IET Computer Vision
Volume: 11
Issue: 1
ISSN: 1751-9632
Publisher: INST Engineering Technology-IET  
Date Published: 2017-02-01
Start Page: 22
End Page: 32
Language: English
DOI: 10.1049/iet-cvi.2016.0163
PROVIDER: scopus
DOI/URL:
Notes: Article -- Export Date: 2 May 2017 -- Source: Scopus
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