Extracting 3D structures from voxel based images can make doctors more directly observe the situation of the target in the clinic, making it easier for doctors to diagnose the condition and make the medicine teaching more directly and easier to understand. For this purpose, we propose
a 3D volume image segmentation method based on the max-flow/min-cut algorithm. Our segmentation method can be applied directly to 3D volume image. After users marking small amount tags (foreground and background pixels), we put forward a method to use a directed connected graph structure to
represent the volume image. In the directed connected graph, in order to speed up the efficiency of the segmentation in subsequent steps, we divide each voxel node in the graph into different color ranges, and each color range match up with an auxiliary node. In order to divide the color range
more finely, we propose a method to calculate the color similarity. We then use the max-flow/min-cut algorithm to segment the directed connected graph. The result of experiments performed in multiple sets of slice images shows that our proposed method improves the efficiency, reduces human
error on the 3D volume image segmentation task, and the result is complete and accurate.
Spectral Embedding and Min Cut for Image Segmentation
