Research Of Medical Image Segmentation Based On Fuzzy Clustering Algorithms

Image segmentation is one of the most important topics in such fields as com-puter vision, image understanding, etc, and is also the core and hot topics in medical imaging. Formally, medical image segmentation is to partition a medical image into non-overlapping different organs or tissues with homogeneous characteristics. By med-ical image segmentation, interesting organ/issue can be retrieved from medical images, and lesions can be analyzed qualitatively and quantitatively. In addition, medical im-age segmentation is the basis of image processing, such as3D reconstruction, and the segmentation results affect the accuracy of reconstruction model directly. Therefore, re-search of medical image segmentation is of important significance and practical value.Due to the complexity of medical images, medical image segmentation has been one bottleneck to restrict medical applications. Considering such phenomena as intensi-ty inhomogeneity, partial volume effect(PVE) and noise, this paper investigates medical image segmentation based on fuzzy clustering algorithms. Compared with other seg-mentation methods, fuzzy clustering is one typical "soft" algorithm. In fuzzy clustering algorithm, pixels can belong to different organs or tissues concurrently with different membership degrees, and can deal with PVE in medical images. When applied in im-age segmentation, fuzzy clustering method can retain as much information as possible from the original image, and thus can retrieve good results. However, since spatial in-formation is not considered, traditional fuzzy clustering algorithm is sensitive to noise and artifacts in medical images, and performs poor in noisy images. Moreover, the ef-ficiency is very low, and cannot satisfies the requirement of medical image processing. For these two problems, the research of this paper are performed from the following aspects:(1) Aiming at low efficiency of FCM, this paper analyzes this problem from the aspect of cluster center computation. In this paper, low efficiency is partly due to the fact that cluster center is computed on the basis of all pixels. Based on this, one stratified segmentation technology is proposed in this paper, in which cluster centers are supposed to be based on pixels in corresponding organ/issues, unrelated with pixels belonging to other clusters. In the proposed algorithm, threshold technique is adopted to partition the medical image into different organ/issue roughly, and then FCM is performed to revise the rough results, which will decease the computation and thus improve the efficiency.(2) This paper investigated the real-time segmentation of medical image segmenta-tion, which can solve low efficiency of FCM and poor result of FCM-related algorithm-s. In this paper, current segmentation algorithms originating from FCM are analyzed deeply, and low efficiency of FCM is due to the fact that the hidden information is not mined sufficiently. Based on this suppose, one improved algorithm based on histogram is proposed, named HisFCM. Peak detection is adopted to partition the histogram of the given image into different intervals, and suppose clustering centers are decided by cor-responding intervals, unrelated to others. Based on these intervals, FCM is performed on the histogram, which can improve the efficiency greatly. Generally, it needs less than0.1s to perform segmentation, which can satisfy the real-time requirement of medical image segmentation.(3) Image segmentation based on neighbor information is proposed. FCMS is one improved algorithm to enhance the segmentation results of FCM, and one constant a is assigned to reflect the impact of neighbor pixels on central one. By such processing, the algorithm can resist the effect of noise. However, when images without noise are segmented, the pixels along the edge maybe misclassified, and fuzzy phenomena appear. This paper proposed the relevance model based on spatial information and intensity information. Then pixel relevance is adopted to replace the impact factor of neighbor pixels on central one. By such, segmentation results can be improved greatly, and fuzzy phenomena can also be eliminated.