Book Chapter10.1007/978-981-10-3153-3_43
A Modified Genetic Algorithm Based FCM Clustering Algorithm for Magnetic Resonance Image Segmentation
Sunanda Das,Sourav De +1 more
- 01 Jan 2017
- pp 435-443
8
TL;DR: The proposed modified genetic algorithm (MfGA) based fuzzy C-means algorithm, which segment magnetic resonance (MR) images, improves the population initialization and crossover parts of GA and generates the optimized class levels of the multilevel MR images.
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Abstract: In this article, we have devised modified genetic algorithm (MfGA) based fuzzy C-means algorithm, which segment magnetic resonance (MR) images. In FCM, local minimum point can be easily derived for not selecting the centroids correctly. The proposed MfGA improves the population initialization and crossover parts of GA and generate the optimized class levels of the multilevel MR images. After that, the derived optimized class levels are applied as the initial input in FCM. An extensive performance comparison of the proposed method with the conventional FCM on two MR images establishes the superiority of the proposed approach.
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References
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Pattern Recognition with Fuzzy Objective Function Algorithms
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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.
17.9K
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.
Fuzzy c-means clustering with spatial information for image segmentation.
Keh-Shih Chuang,Hong Long Tzeng,Hong Long Tzeng,Sharon C.-A. Chen,Jay Wu,Jay Wu,Tzong-Jer Chen +6 more
TL;DR: This paper presents a fuzzy c-means (FCM) algorithm that incorporates spatial information into the membership function for clustering and yields regions more homogeneous than those of other methods.
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399
New methods for MRI denoising based on sparseness and self-similarity.
TL;DR: Two new methods for the three-dimensional denoising of magnetic resonance images that exploit the sparseness and self-similarity properties of the images are proposed, making them usable in most clinical and research settings.
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