Fuzzy C-means clustering for segmenting carotid artery ultrasound images

Authors:
Amr R. Abdel-Dayem;Mahmoud R. El-Sakka
Affiliations:
Computer Science Department, University of Western Ontario, London, Ontario, Canada;Computer Science Department, University of Western Ontario, London, Ontario, Canada
Venue:
ICIAR'07 Proceedings of the 4th international conference on Image Analysis and Recognition
Year:
2007

Citing 10
Cited 3

A Computational Approach to Edge Detection

IEEE Transactions on Pattern Analysis and Machine Intelligence
On active contour models and balloons

CVGIP: Image Understanding
Watersheds in Digital Spaces: An Efficient Algorithm Based on Immersion Simulations

IEEE Transactions on Pattern Analysis and Machine Intelligence
Ziplock Snakes

International Journal of Computer Vision
Digital Image Processing

Digital Image Processing
Real-Time Extraction of Carotid Artery Contours from Ultrasound Images

CBMS '00 Proceedings of the 13th IEEE Symposium on Computer-Based Medical Systems (CBMS'00)
Automatic Detection of the Intimal and the Adventitial Layers of the Common Carotid Artery Wall in Ultrasound B-Mode Images Using Snakes

ICIAP '99 Proceedings of the 10th International Conference on Image Analysis and Processing
Pattern Classification (2nd Edition)

Pattern Classification (2nd Edition)
Watershed segmentation for carotid artery ultrasound images

AICCSA '05 Proceedings of the ACS/IEEE 2005 International Conference on Computer Systems and Applications
Carotid artery ultrasound image segmentation using fuzzy region growing

ICIAR'05 Proceedings of the Second international conference on Image Analysis and Recognition

Diffusion-Based Detection of Carotid Artery Lumen from Ultrasound Images

ICIAR '09 Proceedings of the 6th International Conference on Image Analysis and Recognition
Detection of arterial lumen in sonographic images based on active contours and diffusion filters

ICIAR'10 Proceedings of the 7th international conference on Image Analysis and Recognition - Volume Part II
Carotid artery image segmentation using modified spatial fuzzy c-means and ensemble clustering

Computer Methods and Programs in Biomedicine

Quantified Score

Hi-index	0.00

Visualization

Abstract

This paper introduces a fully automated segmentation scheme for carotid artery ultrasound images. The proposed scheme is based on fuzzy cmeans clustering. It consists of four major stages. These stages are preprocessing, feature extraction, fuzzy c-means clustering, and finally boundary extraction. Experimental results demonstrated the efficiency of the proposed scheme in segmenting carotid artery ultrasound images.