Title :
A new automatic method for mass detection in mammography with false positives reduction by supported vector machine
Author :
Liu, Xiaoming ; Xu, Xin ; Liu, Jun ; Feng, Zhilin
Author_Institution :
Coll. of Comput. Sci. & Technol., Wuhan Univ. of Sci. & Technol., Wuhan, China
Abstract :
Mass localization is important in computer-aided detection (CAD) system for the diagnosis of suspicious regions in mammograms. In this paper, we present an automated classification system for the detection of masses in mammographic images. Suspicious regions are located with an adaptive region growing firstly. Then, the initial regions are further refined with narrow band based active contour, which can improve the segmentation accuracy of masses. CLBP (Complete Local Binary Pattern) texture features are extracted from the ROIs (regions of interest) containing the segmented suspicious regions. Finally, the ROIs are classified by means of support vector machine (SVM), with supervision provided by the radiologist´s diagnosis. The method was evaluated on a dataset with 231 images, containing 245 masses. Among them, 125 images containing 133 masses are used to optimize the parameters and are used to train SVM. The remaining 106 images are used to test the performance. It obtained 1.36 FPsI at the sensitivity 76.8%. It shows that the proposed method is a promising approach to achieve low FPsI while maintain a high sensitivity.
Keywords :
diagnostic radiography; feature extraction; image classification; image segmentation; image texture; mammography; medical image processing; support vector machines; CAD system; CLBP texture features; SVM; active contour; adaptive region growing; automated classification system; automatic mass detection method; breast mass localization; complete local binary pattern; computer aided detection system; false positive reduction; image segmentation accuracy; mammographic images; mammography; support vector machine; texture feature extraction; Active contours; Biomedical imaging; Feature extraction; Image segmentation; Level set; Sensitivity; Support vector machines; Mass detection; active contour; mammography; support vector machine;
Conference_Titel :
Biomedical Engineering and Informatics (BMEI), 2011 4th International Conference on
Conference_Location :
Shanghai
Print_ISBN :
978-1-4244-9351-7
DOI :
10.1109/BMEI.2011.6098328