【摘要】 临床上医生分割淋巴结主要依靠手动,针对手动分割淋巴结的缺点和局限,本文提出一种基于Hessian矩阵和区域扩展拟合水平集模型(Region-Scalable Fitting,RSF)的淋巴结自动分割算法。该算法首先利用Hessian矩阵对CT图像中的淋巴结进行增强,并得到淋巴结粗略轮廓,然后把该粗略轮廓作为RSF模型的初始轮廓,并利用RSF模型对初始轮廓进行演化以实现淋巴结的有效分割。将该方法应用于6个病例的CT淋巴结图像中,初步实验结果与医生手动分割结果对比,平均重叠率93.3%,平均Hausdorff距离为3.8 mm。更多还原

【Abstract】 In current clinical practice, the segmentation of the lymph nodes is still performed manually by the doctors. In order to avoid the disadvantages and limitations of manual segmentation, the present paper proposes a lymph node segmentation algorithm based on Hessian matrix and Region-Scalable Fitting(RSF) model. At first, the algorithm uses Hessian matrix to enhance the lymph node in CT image and obtain the rough boundary of the lymph node. Then the rough boundary is adopted as the initial contour for the RSF model. Finally, the rough lymph node contour is evolved by the RSF model to segment the target lymph node effectively. The algorithm was applied to the lymph node CT images of 6 cases. The preliminary experimental results were compared with the doctor’s manual segmentation results. The average overlap rate was 93.3%, and the average Hausdorff distance was 3.8 mm.更多还原