TY - JOUR ID - 51045 TI - Tumor segmentation in mammogram images using Chan-Vese active contour and texture local feature information JO - Journal of Machine Vision and Image Processing JA - JMVIP LA - en SN - AU - Shirazi, Fatemeh AU - Rashedi, Esmat AU - Nezamabadi-pour, Hossein AD - M.S. Student Department of Electrical Engineering, Graduate University of Advanced Technology AD - Department of Electrical Engineering, Graduate University of Advanced Technology AD - Department of electrical engineering, Shahid Bahonar University of Kerman Y1 - 2018 PY - 2018 VL - 5 IS - 2 SP - 13 EP - 25 KW - breast cancer KW - Computer aided detection KW - mammography KW - Cancerous tumor segmentation KW - Texture feature KW - Local feature information KW - Chan-Vese active contour model DO - N2 - Cancerous tumor segmentation in mammogram images is an important stage and a challenging problemin computer aided detection (CAD) systems. In this paper, local feature information and Chan-Vese(LFI-CV)active contour modelare used for tumor segmentation. First, the texture feature mapsof mammograms are extracted. The utilized texture feature information includes gray level co-occurrence matrix (GLCM) and Gabor features. Using this information,the force values ofChan-Vese model are set and active contour model’s energy is minimized.As a result, the contour accurately segments the tumor. The results show that tumor segmentation using the proposed active contour modelandGabor texture feature at orientationis efficient in regard to the number of iterations, accuracy, and sensitivity. The mini-MIAS database is used for evaluation. UR - https://jmvip.sinaweb.net/article_51045.html L1 - https://jmvip.sinaweb.net/article_51045_64c153aee55cda516076a66130e565e0.pdf ER -