원문정보
초록
영어
This paper presents a new clustering algorithm named improved type-2 possibilistic fuzzy c-means (IT2PFCM) for fuzzy segmentation of magnetic resonance imaging, which combines the advantages of type 2 fuzzy set, the fuzzy c-means (FCM) and Possibilistic fuzzy c-means clustering (PFCM). First of all, the type 2 fuzzy is used to fuse the membership function of the two segmentation algorithms (FCM and PCM), the membership function is an interval distribution, the determined fuzzy values which are the outputs of the FCM and PCM. Secondly, the initialization of cluster center and the process of type-reduction are optimized in this algorithm, which can greatly reduce the calculation of IT2PFCM and accelerate the convergence of the algorithm. Finally, experimental results are given to show the effectives of proposed method in contrast to conventional FCM, PFCM and type 2 fuzzy c-means.
목차
1. Introduction
2. Background Information
2.1. Fuzzy C-Means Clustering Algorithm
2.2. Possibilistic Fuzzy C-Means Clustering
2.3. Type-2 Fuzzy C-Means Algorithm
3. Improved Type-2 Possibilistic Fuzzy C-Means Clustering Algorithm
3.1. The Determination of the Initial Clustering Center
3.2. Type Reduction Algorithm
4. Simulation and Results
References