Feature Extraction Techniques for Mass Detection in Digital Mammogram (Review)

A mammogram is an x-ray that allows a qualified specialist to examine the breast tissue for any suspicious areas. Mammogram helps for early diagnosis before showing symptoms of cancer. The aim of this paper is to extract the various features of pre-processed mammogram images to improve the performance of the diagnosis, which helps the radiologists in reducing the false positive predictions. Mammogram images are pre-processed using hybrid filter MAX_AVM. Shape, Intensity, Gray Level Co-occurrence Matrix and Gray Level Run-Length Matrix features that help to represent the various classes of objects are extracted and used as inputs to the classifier. The classifier helps to classify the mammogram images into a normal or abnormal pattern. Experiments were conducted on MIAS database. The result shows that the combination of GLCM and GLRLM features are efficient and achieved the maximum classification accuracy rate when compared to other features.

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Breast Cancer Detection via Mammographic Images : A Survey

Mwadulo

Angulu

Mutua

2020

IJSRCSEIT

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Breast cancer is a top killer disease for women globally. The long term survival rate of women can be improved through early and effective screening of breast cancer cells. Currently, a mammogram is the recommended tool for breast cancer screening since it can identify breast cancer cells several years before physical signs appear and it is cost effective. This paper analyzes mammographic detection of breast cancer by providing an explanation on development and classification of Breast Cancer, Image representation models for breast tumor, mammography technologies, a discussion on various mammographic signs of breast cancer, breast cancer feature extraction techniques, popular breast cancer classification techniques, comparative analysis of existing mammogram breast cancer databases, and a review of mammographic breast cancer detection studies are presented. Finally, a highlight on future work is given.

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Feature Extraction Techniques for Mass Detection in Digital Mammogram (Review)

Cited by 3 publications

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Optimal Feature Selection for Computer-Aided Characterization of Tissues: Case Study of Mammograms

Optimal Feature Selection for Computer-Aided Characterization of Tissues: Case Study of Mammograms

GLCM and GLRLM based Feature Extraction Technique in Mammogram Images

Breast Cancer Detection via Mammographic Images : A Survey

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