Contour extraction of skin tumors accurately is an important task for further feature generation of their borders and sur-faces to early diagnose melanomas. An integrated approach, combining visual attention model and...Contour extraction of skin tumors accurately is an important task for further feature generation of their borders and sur-faces to early diagnose melanomas. An integrated approach, combining visual attention model and GVF-snake, is pro-posed in the paper to provide a general framework for locating tumor boundaries in case of noise and boundaries with large concavity. For any skin image, the visual attention model is implemented to locate the Region of Interests (ROIs) based on saliency maps. Then an algorithm called GVF-snake is utilized to iteratively drive an initial contour, deriving from the extracted ROIs, towards real boundary of skin tumors by minimizing an energy function. It is shown from ex-periments that the proposed approach exceeds in two aspects compared with other contour-deforming methods: 1) ini-tial contours generated from saliency maps are definitely located at neighboring regions of real boundaries of skin tu-mors to speed up converges of contour deformation and achieve higher accuracy;2) the method is not sensitive to nois-es on skins and initial contours extracted.展开更多
Image segmentation is an important research area in Computer Vision and the GVF-snake is an effective segmentation algorithm presented in recent years. Traditional GVF-snake algorithm has a large capture range and can...Image segmentation is an important research area in Computer Vision and the GVF-snake is an effective segmentation algorithm presented in recent years. Traditional GVF-snake algorithm has a large capture range and can deal with boundary concavities. However, when interesting object has deep concavities, traditional GVF-snake algorithm can’t converge to true boundaries exactly. In this paper, a novel improved scheme was proposed based on the GVF-snake. The central idea is introduce dynamic balloon force and tangential force to strengthen the static GVF force. Experimental results of synthetic image and real image all demonstrated that the improved algorithm can capture boundary concavities better and detect complex edges more accurately.展开更多
文摘Contour extraction of skin tumors accurately is an important task for further feature generation of their borders and sur-faces to early diagnose melanomas. An integrated approach, combining visual attention model and GVF-snake, is pro-posed in the paper to provide a general framework for locating tumor boundaries in case of noise and boundaries with large concavity. For any skin image, the visual attention model is implemented to locate the Region of Interests (ROIs) based on saliency maps. Then an algorithm called GVF-snake is utilized to iteratively drive an initial contour, deriving from the extracted ROIs, towards real boundary of skin tumors by minimizing an energy function. It is shown from ex-periments that the proposed approach exceeds in two aspects compared with other contour-deforming methods: 1) ini-tial contours generated from saliency maps are definitely located at neighboring regions of real boundaries of skin tu-mors to speed up converges of contour deformation and achieve higher accuracy;2) the method is not sensitive to nois-es on skins and initial contours extracted.
文摘Image segmentation is an important research area in Computer Vision and the GVF-snake is an effective segmentation algorithm presented in recent years. Traditional GVF-snake algorithm has a large capture range and can deal with boundary concavities. However, when interesting object has deep concavities, traditional GVF-snake algorithm can’t converge to true boundaries exactly. In this paper, a novel improved scheme was proposed based on the GVF-snake. The central idea is introduce dynamic balloon force and tangential force to strengthen the static GVF force. Experimental results of synthetic image and real image all demonstrated that the improved algorithm can capture boundary concavities better and detect complex edges more accurately.
