Classification of mammogram using two-dimensional discrete orthonormal S-transform for breast cancer detection

Healthcare Technology Letters

article

Shradhananda Beura¹  Banshidhar Majhi¹  Ratnakar Dash¹  Susnata Roy¹ 

[1] Department of Computer Science and Engineering, Pattern Recognition Laboratory, National Institute of Technology

关键词: mammography;    medical image processing;    image classification;    learning (artificial intelligence);    feature selection;    feature extraction;    cancer;    statistical testing;    mammogram classification;    two-dimensional discrete orthonormal S-transform;    breast cancer detection;    feature selection;    null-hypothesis test;    statistical two-sample t-test method;    AdaBoost algorithm;    Mammographic Image Analysis Society database;    Digital Database for Screening Mammography database;    image classification;   

DOI  :  10.1049/htl.2014.0108

学科分类：肠胃与肝脏病学

来源: Wiley

【 摘 要 】

An efficient approach for classification of mammograms for detection of breast cancer is presented. The approach utilises the two-dimensional discrete orthonormal S-transform (DOST) to extract the coefficients from the digital mammograms. A feature selection algorithm based the on null-hypothesis test with statistical ‘two-sample t -test’ method has been suggested to select most significant coefficients from a large number of DOST coefficients. The selected coefficients are used as features in the classification of mammographic images as benign or malignant. This scheme utilises an AdaBoost algorithm with random forest as its base classifier. Two standard databases Mammographic Image Analysis Society (MIAS) and Digital Database for Screening Mammography (DDSM) are used for the validation of the proposed scheme. Simulation results show an optimal classification performance with respect to accuracies of 98.3 and 98.8% and AUC (receiver operating characteristic) values of 0.9985 and 0.9992 for MIAS and DDSM, respectively. Comparative analysis shows that the proposed scheme outperforms its competent schemes.

【 授权许可】

CC BY|CC BY-ND|CC BY-NC|CC BY-NC-ND