A Validity Measure for Fuzzy Clustering
IEEE Transactions on Pattern Analysis and Machine Intelligence
Pattern Recognition Letters
Pattern Recognition with Fuzzy Objective Function Algorithms
Pattern Recognition with Fuzzy Objective Function Algorithms
Computer Vision
Improving Performance of Similarity-Based Clustering by Feature Weight Learning
IEEE Transactions on Pattern Analysis and Machine Intelligence
IPMI '97 Proceedings of the 15th International Conference on Information Processing in Medical Imaging
Improving fuzzy c-means clustering based on feature-weight learning
Pattern Recognition Letters
SIP '07 Proceedings of the Ninth IASTED International Conference on Signal and Image Processing
A framework for unsupervised segmentation of multi-modal medical images
CVAMIA'06 Proceedings of the Second ECCV international conference on Computer Vision Approaches to Medical Image Analysis
Fuzzy clustering with partial supervision
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
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In this paper we introduce a new clustering method and apply it to brain magnetic resonance imaging (MRI) lateral ventricular compartments segmentation. The method uses Gaussian smoothing to enable fuzzy c-mean (FCM) to create both a more homogeneous clustering result and reduce effect caused by noise. With the objective of finding the optimal clustering results, we present a weighted clustering scheme which is applied to a Gaussian smoothed image using bootstrapping approach of feature weighting. The scheme is called weighted FCM with Gaussian smoothing (WGFCM). In addition to the observations on the clustering results of the MR images, we use validity functions and clustering centroids to evaluate the clustering results. Compared with the standard FCM with or without Gaussian smoothing, we found that the proposed scheme provides a better clustering performance for brain MRI lateral ventricular compartments segmentation.