Book ChapterDOI
Fast Generalized Fuzzy C-means Using Particle Swarm Optimization for Image Segmentation
Dang Cong Tran,Dang Cong Tran,Zhijian Wu,Van Hung Tran +3 more
- pp 263-270
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TLDR
Particle Swarm Optimization is introduced into fast generalized FCM incorporating with local spatial and gray information called PFGFCM, where the membership degree values were modified by applying optimal-selection-based suppressed strategy to show that the proposed method is superior to other fuzzy algorithms.Abstract:
Fuzzy C-means algorithms (FCMs) incorporating local information has been widely used for image segmentation, especially on image corrupted by noise. However, they cannot obtain the satisfying segmentation performance on the image heavily contaminated by noise, sensitivity to initial points, and can be trapped into local optima. Hence, optimization techniques are often used in conjunction with algorithms to improve the performance. In this paper, Particle Swarm Optimization (PSO) is introduced into fast generalized FCM (FGFCM) incorporating with local spatial and gray information called PFGFCM, where the membership degree values were modified by applying optimal-selection-based suppressed strategy. Experimental results on synthetic and real images heavily corrupted by noise show that the proposed method is superior to other fuzzy algorithms.read more
Citations
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Journal ArticleDOI
Multi-channeled MR brain image segmentation: A novel double optimization approach combined with clustering technique for tumor identification and tissue segmentation
Anitha Narayanan,M. Pallikonda Rajasekaran,Yudong Zhang,Vishnuvarthanan Govindaraj,Arunprasath Thiyagarajan +4 more
TL;DR: The strength of the proposed algorithm is proven by comparing it with the state-of-the-art techniques by means of evaluation parameters like mean squared error (MSE), peak signal to noise ratio (PSNR), sensitivity, specificity, etc.,
Journal ArticleDOI
Domain-independent severely noisy image segmentation via adaptive wavelet shrinkage using particle swarm optimization and fuzzy C-means
TL;DR: A Particle Swarm Optimization (PSO)-based feature enhancement approach in the wavelet domain for noisy image segmentation that helps to enhance intensity features for clustering-based denoising, and also provides adaptivity for the system that performs well on a range of real, synthetic, and simulated noisy images with different noise levels and range/spatial properties.
Book ChapterDOI
A New Modification of Fuzzy C-Means via Particle Swarm Optimization for Noisy Image Segmentation
TL;DR: A new clustering-based algorithm for noisy image segmentation that combines Particle Swarm Optimization PSO with Fuzzy C-Means FCM, empowered with a new similarity metric, resulting in more accurate segmentation results in noisy images compared to other state-of-the-art methods.
Journal ArticleDOI
Segmentation and Diagnosis of Papillary Thyroid Carcinomas Based on Generalized Clustering Algorithm in Ultrasound Elastography
TL;DR: A large number of qualitative and quantitative experimental results show that the proposed generalized clustering algorithm can obtain more accurate results when clustering images with high noise and is suitable for intelligent diagnosis of papillary thyroid convolution in clinical examination.
Proceedings ArticleDOI
A heuristic solution for noisy image segmentation using Particle Swarm Optimization and Fuzzy clustering
TL;DR: A new computational algorithm for segmentation of gray images contaminated with impulse noise is proposed and has promising performance in comparison with other existing methods in cases where images have been corrupted with a high density noise.
References
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Proceedings ArticleDOI
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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.
Proceedings ArticleDOI
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TL;DR: The optimization of nonlinear functions using particle swarm methodology is described and implementations of two paradigms are discussed and compared, including a recently developed locally oriented paradigm.
Book
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George J. Klir,Bo Yuan +1 more
TL;DR: Fuzzy Sets and Fuzzy Logic is a true magnum opus; it addresses practically every significant topic in the broad expanse of the union of fuzzy set theory and fuzzy logic.
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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.
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