Abstract
The fuzzy C-means clustering (FCM) algorithm is used widely in medical image segmentation and suitable for segmenting brain tumors. Therefore, an intuitionistic fuzzy C-means algorithm based on membership information transferring and similarity measurements (IFCM-MS) is proposed to segment brain tumor magnetic resonance images (MRI) in this paper. The original FCM lacks spatial information, which leads to a high noise sensitivity. To address this issue, the membership information transfer model is adopted to the IFCM-MS. Specifically, neighborhood information and the similarity of adjacent iterations are incorporated into the clustering process. Besides, FCM uses simple distance measurements to calculate the membership degree, which causes an unsatisfactory result. So, a similarity measurement method is designed in the IFCM-MS to improve the membership calculation, in which gray information and distance information are fused adaptively. In addition, the complex structure of the brain results in MRIs with uncertainty boundary tissues. To overcome this problem, an intuitive fuzzy attribute is embedded into the IFCM-MS. Experiments performed on real brain tumor images demonstrate that our IFCM-MS has low noise sensitivity and high segmentation accuracy.
Highlights
Recent years have witnessed the increasement of prevalence in glioma
To overcome the defects of the algorithms as mentioned, we propose an intuitionistic fuzzy C-means algorithm based on membership filtering and similarity measurements, named the IFCM-MS
IFCM-MS: intuitionistic fuzzy C-means algorithm based on membership information transferring and similarity measurements
Summary
Recent years have witnessed the increasement of prevalence in glioma. The high incidence and mortality of glioma have threatened human health seriously. Segmenting tumor information in brain magnetic resonance images (MRI) by computer technology has become a current hot research field. A variety of image segmentation techniques have been proposed, such as algorithms based on manual segmentation [2,3], boundary [4], atlas [5,6,7], kernel function [8,9,10,11], region growing technology [12,13,14,15,16] and clustering [17,18,19,20,21,22,23,24,25,26,27,28,29,30,31,32]. The algorithm based on clustering consists of two main types of architectures, including the soft and hard clustering-based segmentation methods. Conventional hard clustering-based segmentation often loses small-sized information, whereas the soft clustering-based algorithm achieves better properties in segmenting MRI information. In this paper, we will focus on MRI segmentation based on soft clustering
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