research-article

Image clustering using improved spatial fuzzy C-means

Authors:

Asmatullah Chaudhry,

Asifullah Khan,

Tran Anh TuanAuthors Info & Claims

ICUIMC '12: Proceedings of the 6th International Conference on Ubiquitous Information Management and Communication

Article No.: 86, Pages 1 - 7

https://doi.org/10.1145/2184751.2184853

Published: 20 February 2012 Publication History

Abstract

Image segmentation plays vital role in computer vision, pattern recognition, disease diagnosing in medical images and many other fields. Medical image segmentation is one of the most attractive areas of research, because successfulness of the post-processing techniques highly depends on the accuracy of image segmentation. Further, during acquisition medical images may have been corrupted from various types of degradations and noises. In medical imagery, ultrasound images are usually of poor quality due to the presence of speckle noise and wave interferences. It takes considerable efforts from experts to analyze and make decision on the basis of acquired images. In this paper, we propose an approach called improved spatial fuzzy c-means modified (sFCMM) for image segmentation. This technique works well compared to conventional fuzzy c-means (FCM) and spatial fuzzy c-means (sFCM) algorithms. Three types of features: moments of gray level histogram (MGH), 2D continuous wavelet transforms (2D-CWT) and gray level co-occurrence matrix (GLCM), are extracted from the ultrasound images for segmentation. Clustering validity indices are computed and have been compared with FCM and sFCM algorithm. Experimental results show the effectiveness of the proposed technique.

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Cited By

Akhtar UHassan M(2019)Big Data Mining Based on Computational Intelligence and Fuzzy ClusteringWeb Services10.4018/978-1-5225-7501-6.ch024(413-430)Online publication date: 2019
https://doi.org/10.4018/978-1-5225-7501-6.ch024
Mengting Xu Min Guo Lin Shang Xiuyi Jia (2016)Multi-value image segmentation based on FCM algorithm and Graph Cut Theory2016 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE)10.1109/FUZZ-IEEE.2016.7737844(1333-1340)Online publication date: Jul-2016
https://doi.org/10.1109/FUZZ-IEEE.2016.7737844
Chaudhry AHassan MKhan A(2016)Robust segmentation and intelligent decision system for cerebrovascular diseaseMedical & Biological Engineering & Computing10.1007/s11517-016-1481-154:12(1903-1920)Online publication date: 7-Apr-2016
https://doi.org/10.1007/s11517-016-1481-1
Show More Cited By

Index Terms

Image clustering using improved spatial fuzzy C-means
1. Computing methodologies
  1. Artificial intelligence
    1. Computer vision
      1. Computer vision problems
        Interest point and salient region detections
  2. Machine learning
    1. Learning paradigms
      1. Unsupervised learning
        Cluster analysis

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Information & Contributors

Information

Published In

cover image ACM Conferences

ICUIMC '12: Proceedings of the 6th International Conference on Ubiquitous Information Management and Communication

February 2012

852 pages

ISBN:9781450311724

DOI:10.1145/2184751

Conference Chairs:
Suk-Han Lee
Sungkyunkwan University, Korea
,
Lajos Hanzo
University of Southampton, UK
,
Roslan Ismail
Universiti Kuala Lumpur, Malaysia
,
Program Chairs:
Dongsoo S. Kim
Indiana University
,
Min Young Chung
Sungkyunkwan University, Korea
,
Sang-Won Lee
Sungkyunkwan University, Korea

Copyright © 2012 ACM.

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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SIGAPP: ACM Special Interest Group on Applied Computing
SKKU: SUNGKYUNKWAN UNIVERSITY

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 20 February 2012

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ICUIMC '12

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SKKU

ICUIMC '12: The 6th International Conference on Ubiquitous Information Management and Communication

February 20 - 22, 2012

Kuala Lumpur, Malaysia

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Overall Acceptance Rate 251 of 941 submissions, 27%

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Cited By

Akhtar UHassan M(2019)Big Data Mining Based on Computational Intelligence and Fuzzy ClusteringWeb Services10.4018/978-1-5225-7501-6.ch024(413-430)Online publication date: 2019
https://doi.org/10.4018/978-1-5225-7501-6.ch024
Mengting Xu Min Guo Lin Shang Xiuyi Jia (2016)Multi-value image segmentation based on FCM algorithm and Graph Cut Theory2016 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE)10.1109/FUZZ-IEEE.2016.7737844(1333-1340)Online publication date: Jul-2016
https://doi.org/10.1109/FUZZ-IEEE.2016.7737844
Chaudhry AHassan MKhan A(2016)Robust segmentation and intelligent decision system for cerebrovascular diseaseMedical & Biological Engineering & Computing10.1007/s11517-016-1481-154:12(1903-1920)Online publication date: 7-Apr-2016
https://doi.org/10.1007/s11517-016-1481-1
Akhtar UHassan M(2015)Big Data Mining Based on Computational Intelligence and Fuzzy ClusteringHandbook of Research on Trends and Future Directions in Big Data and Web Intelligence10.4018/978-1-4666-8505-5.ch007(130-148)Online publication date: 2015
https://doi.org/10.4018/978-1-4666-8505-5.ch007
Chaudhry AHassan MKhan AKim J(2013)Automatic Active Contour-Based Segmentation and Classification of Carotid Artery Ultrasound ImagesJournal of Digital Imaging10.1007/s10278-012-9566-326:6(1071-1081)Online publication date: 16-Feb-2013
https://doi.org/10.1007/s10278-012-9566-3

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