Effective FCM noise clustering algorithms in medical images

Abstract The main motivation of this paper is to introduce a class of robust non-Euclidean distance measures for the original data space to derive new objective function and thus clustering the non-Euclidean structures in data to enhance the robustness of the original clustering algorithms to reduce...

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Vydáno v:Computers in biology and medicine Ročník 43; číslo 2; s. 73 - 83
Hlavní autoři: Kannan, S.R, Devi, R, Ramathilagam, S, Takezawa, K
Médium: Journal Article
Jazyk:angličtina
Vydáno: United States Elsevier Ltd 01.02.2013
Elsevier Limited
Témata:
Accuracy
Algorithms
Brain - anatomy & histology
Brain - pathology
Breast - anatomy & histology
Breast - pathology
Cluster Analysis
Databases, Factual
Entropy
Entropy FCM
Female
Fuzzy Logic
Fuzzy sets
Humans
Image Processing, Computer-Assisted - methods
Internal Medicine
Magnetic Resonance Imaging - methods
Medical images
Medical imaging
Methods
motivation
Noise
Noise clustering
Other
Product development
prototypes
Segmentation
Studies
Tomography
Noise clustering
Medical images
Segmentation
Entropy FCM
ISSN:0010-4825, 1879-0534, 1879-0534
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Shrnutí:Abstract The main motivation of this paper is to introduce a class of robust non-Euclidean distance measures for the original data space to derive new objective function and thus clustering the non-Euclidean structures in data to enhance the robustness of the original clustering algorithms to reduce noise and outliers. The new objective functions of proposed algorithms are realized by incorporating the noise clustering concept into the entropy based fuzzy C-means algorithm with suitable noise distance which is employed to take the information about noisy data in the clustering process. This paper presents initial cluster prototypes using prototype initialization method, so that this work tries to obtain the final result with less number of iterations. To evaluate the performance of the proposed methods in reducing the noise level, experimental work has been carried out with a synthetic image which is corrupted by Gaussian noise. The superiority of the proposed methods has been examined through the experimental study on medical images. The experimental results show that the proposed algorithms perform significantly better than the standard existing algorithms. The accurate classification percentage of the proposed fuzzy C-means segmentation method is obtained using silhouette validity index.
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ISSN:0010-4825
1879-0534
1879-0534
DOI:10.1016/j.compbiomed.2012.10.002