NDLI: Improved mass detection in 3D automated breast ultrasound using region based features and multi-view information

Content Provider	IEEE Xplore Digital Library
Author	Chuyang Ye Vaidya, V. Fei Zhao
Copyright Year	2014
Description	Author affiliation: Dept. of Electr. & Comput. Eng., Johns Hopkins Univ., Baltimore, MD, USA (Chuyang Ye) \|\| GE Global Res., Niskayuna, NY, USA (Fei Zhao) \|\| GE Global Res., Bangalore, India (Vaidya, V.)
Abstract	Breast cancer is one of the leading causes of cancer death for women. Early detection of breast cancer is crucial for reducing mortality rates and improving prognosis of patients. Recently, 3D automated breast ultrasound (ABUS) has gained increasing attentions for reducing subjectivity, operator-dependence, and providing 3D context of the whole breast. In this work, we propose a breast mass detection algorithm improving voxel-based detection results by incorporating 3D region-based features and multi-view information in 3D ABUS images. Based on the candidate mass regions produced by voxel-based method, our proposed approach further improves the detection results with three major steps: 1) 3D mass segmentation in geodesic active contours framework with edge points obtained from directional searching; 2) region-based single-view and multi-view feature extraction; 3) support vector machine (SVM) classification to discriminate candidate regions as breast masses or normal background tissues. 22 patients including 51 3D ABUS volumes with 44 breast masses were used for evaluation. The proposed approach reached sensitivities of 95%, 90%, and 70% with averaged 4.3, 3.8, and 1.6 false positives per volume, respectively. The results also indicate that the multi-view information plays an important role in false positive reduction in 3D breast mass detection.
Sponsorship	IEEE Eng. Med. Biol. Soc.
Starting Page	2865
Ending Page	2868
File Size	1003637
Page Count	4
File Format	PDF
ISBN	9781424479290
ISSN	1557170X
DOI	10.1109/EMBC.2014.6944221
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2014-08-26
Publisher Place	USA
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Feature extraction Breast Three-dimensional displays Image segmentation Image edge detection Ultrasonic imaging Support vector machines SVM ABUS breast cancer multi-view geodesic active contours
Content Type	Text
Resource Type	Article
Subject	Signal Processing Biomedical Engineering Health Informatics Computer Vision and Pattern Recognition

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