Early breast cancer detection with digital mammograms using Haar-like features and AdaBoost algorithm

Yufeng Zheng; Clifford Yang; Alex Merkulov; Malavika Bandari

doi:10.1117/12.2227342

19 May 2016 Early breast cancer detection with digital mammograms using Haar-like features and AdaBoost algorithm

Yufeng Zheng, Clifford Yang, Alex Merkulov, Malavika Bandari

Proceedings Volume 9871, Sensing and Analysis Technologies for Biomedical and Cognitive Applications 2016; 98710D (2016) https://doi.org/10.1117/12.2227342
Event: SPIE Commercial + Scientific Sensing and Imaging, 2016, Baltimore, MD, United States

Abstract

The current computer-aided detection (CAD) methods are not sufficiently accurate in detecting masses, especially in dense breasts and/or small masses (typically at their early stages). A small mass may not be perceived when it is small and/or homogeneous with surrounding tissues. Possible reasons for the limited performance of existing CAD methods are lack of multiscale analysis and unification of variant masses. The speed of CAD analysis is important for field applications. We propose a new CAD model for mass detection, which extracts simple Haar-like features for fast detection, uses AdaBoost approach for feature selection and classifier training, applies cascading classifiers for reduction of false positives, and utilizes multiscale detection for variant sizes of masses. In addition to Haar features, local binary pattern (LBP) and histograms of oriented gradient (HOG) are extracted and applied to mass detection. The performance of a CAD system can be measured with true positive rate (TPR) and false positives per image (FPI). We are collecting our own digital mammograms for the proposed research. The proposed CAD model will be initially demonstrated with mass detection including architecture distortion.

Citation Download Citation

Yufeng Zheng, Clifford Yang, Alex Merkulov, and Malavika Bandari "Early breast cancer detection with digital mammograms using Haar-like features and AdaBoost algorithm", Proc. SPIE 9871, Sensing and Analysis Technologies for Biomedical and Cognitive Applications 2016, 98710D (19 May 2016); https://doi.org/10.1117/12.2227342

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