基于图割和水平集的肾脏医学图像分割

Renal Cortex Segmentation Using Graph Cuts and Level Sets

肾脏医学图像分割是医学图像分析和非侵入式计算机辅助诊断系统中的关键步骤。从CT、MRI图像中分割出肾脏及肾皮质,计算其体积和皮质厚度等信息,有助于评估肾脏的功能,从而制定相应的治疗方案。根据肾脏序列图像相邻切片之间结构灰度分布的相似性,提出了一种基于图割和水平集方法的自动肾脏及肾皮质分割方法。选取皮质区域具有足够对比度和清晰度的切片为初始参考图像,使用霍夫森林算法检测肾脏区域,对前景、背景进行均值聚类以估计其灰度分布,获取图割模型能量函数,分割出肾脏整体;通过形态学处理得到相邻切片肾脏的分割候选区域,重复上述分割。以此初步分割结果作为水平集方法的初始轮廓,进一步分割得到三维的肾脏整体和肾皮质区域。实验结果表明,基于图割和水平集的肾脏分割方法能够比较准确地分割出肾脏及肾皮质。

Kidney segmentation is the key step for medical image analysis and non-invasive computer aided diagnosis. The region of kidney and renal cortex are extracted in order to compute the volume and thickness of the cortex. These measurements are used to assess the renal function and design the treatment planning. Based on the similarity between the consecutive slices of three dimensioal renal image, an automatic kidney and renal cortex segmentation algorithm with graph cuts and level sets was proposed in this paper. The slice with enough intensity contrast and high definition is taken as as the initial reference. Hough forest is applied in detecting the region of kidney to estimate its intensity distri- bution and acquire the energy function for the kidney segmentation. Then,mathematical morphology is used to achieve the rough contour of next slices. Based on the initial segmentation result, the initial contours are positioned and the level sets are used to partition the renal cortex. This processing will be continued until all sliced is segmented. The test results show that the proposed algorithm is effective to segment the kidney and renal cortex.