Application of texture analysis method for mammogram density classification. (6th July 2017)

• Record Type: Journal Article
• Authors: Nithya, R.; Santhi, B.
• Is Part Of: Journal of instrumentation. Volume 12:Number 7(2017:Jul.)
• Page Start: P07009
• Page End: P07009
• Publication Date: 2017-07-06
• Subjects: Medical-image reconstruction methods and algorithms, computer-aided diagnosis -- X-ray mammography and scinto- and MRI-mammography; Scientific apparatus and instruments -- Periodicals; 502.84
• DOI: 10.1088/1748-0221/12/07/P07009
• Languages: English
• ISSNs: 1748-0221

Abstract

Abstract: Mammographic density is considered a major risk factor for developing breast cancer. This paper proposes an automated approach to classify breast tissue types in digital mammogram. The main objective of the proposed Computer-Aided Diagnosis (CAD) system is to investigate various feature extraction methods and classifiers to improve the diagnostic accuracy in mammogram density classification. Texture analysis methods are used to extract the features from the mammogram. Texture features are extracted by using histogram, Gray Level Co-Occurrence Matrix (GLCM), Gray Level Run Length Matrix (GLRLM), Gray Level Difference Matrix (GLDM), Local Binary Pattern (LBP), Entropy, Discrete Wavelet Transform (DWT), Wavelet Packet Transform (WPT), Gabor transform and trace transform. These extracted features are selected using Analysis of Variance (ANOVA). The features selected by ANOVA are fed into the classifiers to characterize the mammogram into two-class (fatty/dense) and three-class (fatty/glandular/dense) breast density classification. This work has been carried out by using the mini-Mammographic Image Analysis Society (MIAS) database. Five classifiers are employed namely, Artificial Neural Network (ANN), Linear Discriminant Analysis (LDA), Naive Bayes (NB), K-Nearest Neighbor (KNN), and Support Vector Machine (SVM). Experimental results show that ANN provides better performance than LDA, NB, KNN and SVM classifiers. The proposed methodology has achieved 97.5% accuracy for three-class and 99.37% for two-class density classification.