Random walks-based (RW) segmentation methods have been proven to have a potential application in segmenting the medical image with minimal interactive guidance. However, the approach leads to large-scale graphs due to number of nodes equal to voxel number. Also, segmentation is inaccurate because of the unavailability of appropriate initial seed points. It is a challenge to use the RW-based segmentation algorithm to segment organ regions from 3D medical images interactively. In this paper, a knowledge-based segmentation framework for multiple organs is proposed based on random walks. This method employs the previous segmented slice as prior knowledge (the shape and intensity constraints) for automatic segmentation of other slices, which can reduce the graph scale and significantly speed up the optimization procedure of the graph. To assess the efficiency of our proposed method, experiments were performed on liver tissues, spleen tissues and hepatic cancer and it was extensively evaluated both quantitatively and qualitatively. Comparing our method with conventional RW and state-of-the-art interactive segmentation methods, our results show an improvement in the accuracy for multi-organ segmentation (p < 0.001).
All Science Journal Classification (ASJC) codes
- Computer Science(all)