Image clustering using improved spatial fuzzy C-means

Authors:
Asmatullah Chaudhry;Mehdi Hassan;Asifullah Khan;Jin Young Kim;Tran Anh Tuan
Affiliations:
HRD, PINSTECH, Nilore, Islamabad, Pakistan and School of Electronics & Computer Engineering, CNU, Gwangju, S. Korea;PIEAS, Nilore, Islamabad, Pakistan;PIEAS, Nilore, Islamabad, Pakistan;School of Electronics & Computer Engineering, CNU, Gwangju, S. Korea;School of Electronics & Computer Engineering, CNU, Gwangju, S. Korea
Venue:
Proceedings of the 6th International Conference on Ubiquitous Information Management and Communication
Year:
2012

Citing 6
Cited 1

Feature-based fuzzy classification for interpretation of mammograms

Fuzzy Sets and Systems
Pattern Classification (2nd Edition)

Pattern Classification (2nd Edition)
Genetic perceptual shaping: Utilizing cover image and conceivable attack information during watermark embedding

Information Fusion
Machine learning based adaptive watermark decoding in view of anticipated attack

Pattern Recognition
Medical image segmentation with transform and moment based features and incremental supervised neural network

Digital Signal Processing
BCH coding and intelligent watermark embedding: Employing both frequency and strength selection

Applied Soft Computing

Robust information gain based fuzzy c-means clustering and classification of carotid artery ultrasound images

Computer Methods and Programs in Biomedicine

Quantified Score

Hi-index	0.00

Visualization

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.