报告地点： 励学楼B 219
报告摘要: Image segmentation is a complex and core technique for disease diagnosis or image-guided surgery in the medical image domain. However, low-quality images, such as images with weak edges and intensity inhomogeneities, may bring considerable challenges for radiologists. In this paper, we propose an adaptive weighted curvature-based active contour model by coupling heat kernel convolution and adaptively weighted high-order total variation for medical image segmentation to improve diagnosis effectiveness. To reduce the computational complexity, the heat kernel convolution operation is applied to approximate the perimeter of a segmentation curve. In addition, the weighted parameter included in the high-order total variation term can be automatically evaluated based on an adaptive input image to emphasize local structures and increase segmentation accuracy. Since the proposed method is a smoothing optimization model, the alternating direction method of multipliers is introduced to translate the original problems into several easily solvable subproblems. The numerical experimental results on ultrasonic and MRI datasets demonstrate that the proposed model is quite efficient and robust compared with several traditional segmentation methods.
报告人简介: 庞志峰, 河南大学教授, 博士生导师, 河南省应用数学中心(河南大学)副主任, 河南省数字图形图像学会副理事长, 南洋理工大学/香港城市大学博士后, 利物浦大学访问学者. 主持国家自科和省部级项目共计3项, 参与973项目1项, 省重大和重点项目各1项, 校企合作项目2项. 目前兼任《CT理论与应用研究》编委会委员,《中国体视学与图像分析》编委会委员, 授权国家发明专利2项, 发表学术论文40余篇。