An automatic mass detection system in mammograms based on complex texture features

Shen Chuan Tai, Zih Siou Chen, Wei Ting Tsai

Research output: Contribution to journalArticlepeer-review

84 Citations (Scopus)


It is difficult for radiologists to identify the masses on a mammogram because they are surrounded by complicated tissues. In current breast cancer screening, radiologists often miss approximately 10-30% of tumors because of the ambiguous margins of lesions and visual fatigue resulting from long-time diagnosis. For these reasons, many computer-aided detection (CADe) systems have been developed to aid radiologists in detecting mammographic lesions which may indicate the presence of breast cancer. This study presents an automatic CADe system that uses local and discrete texture features for mammographic mass detection. This system segments some adaptive square regions of interest (ROIs) for suspicious areas. This study also proposes two complex feature extraction methods based on cooccurrence matrix and optical density transformation to describe local texture characteristics and the discrete photometric distribution of each ROI. Finally, this study uses stepwise linear discriminant analysis to classify abnormal regions by selecting and rating the individual performance of each feature. Results show that the proposed system achieves satisfactory detection performance.

Original languageEnglish
Article number6589132
Pages (from-to)618-627
Number of pages10
JournalIEEE Journal of Biomedical and Health Informatics
Issue number2
Publication statusPublished - 2014 Mar

All Science Journal Classification (ASJC) codes

  • Biotechnology
  • Computer Science Applications
  • Electrical and Electronic Engineering
  • Health Information Management


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