An algorithm of medical color image segmentation more effective than existing ones in China

Aim: To our knowledge and in our opinion, Ref. 2 by Liang et al deals with medical color image segmentation more effectively than other Chinese papers. We now propose a medical color image segmentation algorithm which we believe is more effective than that of Ref. 2. In the full paper, we explain our algorithm and its effectiveness in some detail; in this abstract, we just add some pertinent remarks to listing the three topics of explanation. The first topic is: The principles and procedure of the algorithm. In this topic, we transform the RGB (Red, Green, Blue) color information of a medical image into array vectors as comparative sequences and take {1, 1, 1} as reference sequence. Then we calculate their grey relational coefficients and grey relational degrees, as given in eq. (1) And eq. (2) In the full paper. The calculation results produce grey relational images, which are further segmented by using the maximum entropy thresholding method. The second topic is: Experiments and the analysis of their results. In this topic, we do experiments on the color image segmentation of the two images respectively of two cerebrum slices respectively taken from two different positions; the segmentation results are shown in Fig. 1 and Fig. 2. We also compare the segmentation results of our algorithm with those of the algorithm contained in Ref. 2. The comparison results reveal that the segmentation error rate of our algorithm is 1.34% lower; the error of the sum of all edges is 0.28% less; the computing time is 1/10 that of the algorithm in Ref. 2. The third topic is: Quantitative evaluation and conclusions. In this topic, we define segmentation quality evaluation (SQE) and segmentation effectiveness evaluation (SEE); the SQE results, given in Table 1, show that the segmentation quality of our algorithm is about 6 times better than that of Ref. 2. Then we use the SEE to compare the effectiveness of our algorithm with that of the algorithm in Ref. 2; the comparison results, given in Table. 2, indicate that the SEE of our algorithm is 2.43 times the best of 3 SEEs of Ref. 2's algorithm.