Journal ArticleDOI
Effective FCM noise clustering algorithms in medical images
TLDR
This paper presents initial cluster prototypes using prototype initialization method to enhance the robustness of the original clustering algorithms to reduce noise and outliers, and the superiority of the proposed methods has been examined through the experimental study on medical images.About:
This article is published in Computers in Biology and Medicine.The article was published on 2013-02-01. It has received 50 citations till now. The article focuses on the topics: Fuzzy clustering & Cluster analysis.read more
Citations
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Journal ArticleDOI
Conditional spatial fuzzy C-means clustering algorithm for segmentation of MRI images
TL;DR: The experimental results show that the csFCM algorithm has superior performance in terms of qualitative and quantitative studies such as, cluster validity functions, segmentation accuracy, tissue segmentsation accuracy and receiver operating characteristic (ROC) curve on the image segmentation results than the k-means, FCM and some other recently proposed FCM-based algorithms.
Journal ArticleDOI
Improved spatial fuzzy c-means clustering for image segmentation using PSO initialization, Mahalanobis distance and post-segmentation correction
TL;DR: The proposed improvement method, named improved spatial fuzzy c-means IFCMS, was evaluated on several test images including both synthetic images and simulated brain MRI images from the McConnell Brain Imaging Center (BrainWeb) database and demonstrates the efficiency of the ideas presented.
Journal ArticleDOI
Integrating Fuzzy C-Means and TOPSIS for performance evaluation: An application and comparative analysis
TL;DR: This paper introduces the use of Fuzzy C-Means and TOPSIS for organizational performance evaluation purposes and finds that economic performance evaluation is not the best predictor of overall viability of some organizations, especially e-commerce based organizations.
Journal ArticleDOI
Similarity Measure-Based Possibilistic FCM With Label Information for Brain MRI Segmentation
TL;DR: An improved possibilistic fuzzy fuzzy means (FCM) method based on a similarity measure is proposed to improve the segmentation performance for MRI brain images, providing mitigation to the cluster-size problem, resistance to noisy images, and applicability to data with more complex distribution.
Journal ArticleDOI
A modified intuitionistic fuzzy c-means clustering approach to segment human brain MRI image
TL;DR: This paper proposed a modified intuitionistic fuzzy c-means algorithm (MIFCM) and solved analytically the objective function of the MIFCM method using Lagrange method of undetermined multiplier and incorporated hesitation degree to incorporate hesitation degree.
References
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Book
Pattern Recognition with Fuzzy Objective Function Algorithms
TL;DR: Books, as a source that may involve the facts, opinion, literature, religion, and many others are the great friends to join with, becomes what you need to get.
Journal ArticleDOI
Current methods in medical image segmentation.
TL;DR: A critical appraisal of the current status of semi-automated and automated methods for the segmentation of anatomical medical images is presented, with an emphasis on the advantages and disadvantages of these methods for medical imaging applications.
Journal ArticleDOI
A modified fuzzy c-means algorithm for bias field estimation and segmentation of MRI data
TL;DR: A novel algorithm for fuzzy segmentation of magnetic resonance imaging (MRI) data and estimation of intensity inhomogeneities using fuzzy logic and the neighborhood effect acts as a regularizer and biases the solution toward piecewise-homogeneous labelings.
Book
Fuzzy Models and Algorithms for Pattern Recognition and Image Processing
TL;DR: Pattern Recognition, Cluster Analysis for Object Data, Classifier Design, and Image Processing and Computer Vision are studied.
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