基于改进区域生长算法的肝脏分割方法研究

Liver Segmentation Research Using Improved Region Growing Algorithm

目的:把肝脏从医学图像中提取出来,为肝脏三维定位以及放疗计划制定提供准确的数据。肝脏与其周围器官组织灰度差别小、边界不明显,而传统区域生长算法生长准则单一,不能满足分割精确度需求,并且未经处理的轮廓比较粗糙。针对这些问题,本文提出一种改进的区域生长算法。方法:本文算法主要从三个方面改进:基于先验经验和肝脏特性的种子区域选择;基于Canny算子边缘检测结果的区域生长准则动态优化;基于漫水填充法和曲线拟合的轮廓后处理。结果:本文使用多套临床实际腹部CT序列测试算法,以医生手动勾画结果为标准进行评价。在大多数CT切片上的肝脏自动分割都能取得较好的结果,并且分割用时很短,保证了效率。结论:测试结果表明,本文算法在动态控制区域生长和平滑轮廓方面有很好的作用,在保证速度的同时有效提高了肝脏自动分割精度。

Objective：Extracting the liver organ from medical images can provide accurate data for the 3D liver positioning and making radiotherapy plan.For the complex characteristics of abdominal medical image,original region growing algorithm is not able to segment liver accurately.In order to control region growing to fit liver area and smooth contour,an improved region growing algorithm was developed.Methods：In this article,region growing algorithm was improved based on three different aspects：seed area choose based on prior experience and liver characters;dynamic optimization of region growing rule based on edge detection results by Canny operator;contour post processing based on flood fill and curve fitting.Results：Several series of abdominal CT slice were used to test the algorithm and the results were compared with manual segmentation results by doctor,liver was segmented accurately on most CT slices in very short time.Conclusions：The experiment results showed that–the algorithm works well in controlling region growing and smoothing contour,it can improve the accuracy of liver auto segmentation and ensure speed at the same time.