3D Color Medical Image Segmentation System Based On The Max-flow Algorithm

With the improvement of computer technology and the new needs of medical technology,scholars are not satisfied with the current methods of diagnosing the etiology: doctors usually judge the etiology from many 2D medical slice images based on their medical knowledge.This method relies only on the knowledge of the doctors.If there is an error,it will affect the judgment of the cause.At present,scholars have proposed many algorithms in the field of 2D medical image segmentation,but these methods are not efficient.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.For this purpose,we propose a 3D volume image segmentation algorithm based on the max-flow/min-cut algorithm.Our algorithm 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 segment the directed connected graph.Based on the proposed algorithm,the system we designed is not only helpful for the doctors to judge the etiology,but also used in teaching.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.