Fuzzy farthest point first method for MRI brain image clustering
Fuzzy farthest point first method for MRI brain image clustering
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DOI：
10.1049/iet-ipr.2018.6618
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发表时间：
2019-11-14
影响因子：
2.3
通讯作者：
Benmeriem, Khaled
中科院分区：
文献类型：
--
作者：
Debakla, Mohammed;Salem, Mohamed;Benmeriem, Khaled
图像聚类被认为是医学图像分析中最重要的任务之一，它经常被要求作为计算机辅助医学图像处理的起始和重要阶段。在脑磁共振成像（MRI）分析中，图像聚类通常用于估计和可视化脑解剖结构，以检测病理区域并指导外科手术。提出了一种基于最远点优先算法和模糊聚类技术的MRI脑图像聚类新方法，该方法不需要任何关于聚类数的先验信息。该算法已被批准对模拟和临床磁共振图像，它已与第四聚类算法进行了比较。实验结果表明，该算法对MRI数据中的白色、灰质和脑脊液进行了合理的分割，在保留图像细节和分割精度方面上级其他四种算法，Jaccard相似度均超过91%。
Image clustering is considered amongst the most important tasks in medical image analysis and it is regularly required as a starter and vital stage in the computer-aided medical image process. In brain magnetic resonance imaging (MRI) analysis, image clustering is regularly used for estimating and visualising the brain anatomical structures, to detect pathological regions and to guide surgical procedures. This study presents a new method for MRI brain images clustering based on the farthest point first algorithm and fuzzy clustering techniques without using any a priori information about the clusters number. The algorithm has been approved against both simulated and clinical magnetic resonance images and it has been compared with the fourth clustered algorithms. Results demonstrate that the proposed algorithm has given reasonable segmentation of white matter, grey matter and cerebrospinal fluid from MRI data, which is superior in preserving image details and segmentation accuracy compared with the other four algorithms giving more than 91% in Jaccard similarity.