title: A new computer-aided detection scheme based on assessment of local bilateral mammographic feature asymmetry - a preliminary evaluation.
abstract: Accurate segmentation of breast lesions depicting on two-dimensional projection mammograms has been proven very difficult and unreliable. In this study we investigated a new approach of a computer-aided detection (CAD) scheme of mammograms without lesion segmentation. Our scheme was developed based on the detection and analysis of region-of-interest (ROI)-based bilateral mammographic tissue or feature asymmetry. A bilateral image registration, image feature selection process, and naïve Bayes linear classifier were implemented in CAD scheme. CAD performance predicting the likelihood of either an ROI or a subject (case) being abnormal was evaluated using 161 subjects from the mini-MIAS database and a leave-one-out testing method. The results showed that areas under receiver operating characteristic (ROC) curves were 0.87 and 0.72 on the ROI-based and case-based evaluation, respectively. The study demonstrated that using ROI-based bilateral mammographic tissue asymmetry can provide supplementary information with high discriminatory power in order to improve CAD performance.
authors.forename: Adam
authors.initials: A
authors.lastname: Kelder
authors.forename: Yaniv
authors.initials: Y
authors.lastname: Zigel
authors.forename: Dror
authors.initials: D
authors.lastname: Lederman
authors.forename: Bin
authors.initials: B
authors.lastname: Zheng
mesh_terms: D000465:Algorithms; D001499:Bayes Theorem; D001943:Breast Neoplasms / Q000000981:diagnostic imaging*; D005260:Female; D006801:Humans; D008327:Mammography / Q000379:methods; D012372:ROC Curve; D011857:Radiographic Image Interpretation, Computer-Assisted