FUZZY C-MEANS ALGORITHM WITH LOCAL THRESHOLDING FOR GRAY-SCALE IMAGES-Reference-Cited by-同舟云学术

FUZZY C-MEANS ALGORITHM WITH LOCAL THRESHOLDING FOR GRAY-SCALE IMAGES

Published:2008-08 Issue:04 Volume:17 Page:765-775
ISSN:0218-2130
Container-title:International Journal on Artificial Intelligence Tools
language:en
Short-container-title:Int. J. Artif. Intell. Tools

Author:

NG HSIAO PIAU¹,ONG SIM HENG²,FOONG KELVIN WENG CHIONG³,GOH POH SUN⁴,NOWINSKI WIESLAW L.¹

Affiliation:

1. Biomedical Imaging Lab, Agency for Science Technology and Research, 30 Biopolis Street, #07-01 Matrix, Singapore 138671, Singapore

2. Department of Electrical and Computer Engineering, National University of Singapore, 21 Lower Kent Ridge Road, Singapore 119077, Singapore

3. Department of Preventive Dentistry, National University of Singapore, 21 Lower Kent Ridge Road, Singapore 119077, Singapore

4. Department of Diagnostic Imaging, National University Hospital, 5 Lower Kent Ridge Road, Singapore 119074, Singapore

Abstract

An improved fuzzy C-means (FCM) clustering method is proposed. It incorporates Otsu thresholding with conventional FCM to reduce FCM's susceptibility to local minima, as well as its tendency to derive a threshold that is biased towards the component with larger probability, and derive threshold values with greater accuracy. Thresholding is performed at the cluster boundary region in feature space. A comparison of the results produced by improved and conventional algorithms confirms the superior performance of the former.

Publisher

World Scientific Pub Co Pte Lt

Subject

Artificial Intelligence,Artificial Intelligence

Link

https://www.worldscientific.com/doi/pdf/10.1142/S021821300800414X

Reference13 articles.

1. Pattern Recognition with Fuzzy Objective Function Algorithms

2. Parameter selection for suppressed fuzzy c-means with an application to MRI segmentation

3. A Fuzzy C-Means (FCM)-Based Approach for Computerized Segmentation of Breast Lesions in Dynamic Contrast-Enhanced MR Images1

4. Estimation of CSF, white and gray matter volumes in hydrocephalic children using fuzzy clustering of Mr Images

5. Multicontext fuzzy clustering for separation of brain tissues in magnetic resonance images

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