Neural Networks
Editorial: Medical image segmentation: Quo Vadis
Computer Methods and Programs in Biomedicine
Computational Intelligence and Feature Selection: Rough and Fuzzy Approaches
Computational Intelligence and Feature Selection: Rough and Fuzzy Approaches
Entropy based fuzzy C-Mean for item-based collaborative filtering
ISCIT'09 Proceedings of the 9th international conference on Communications and information technologies
A robust fuzzy local information C-means clustering algorithm
IEEE Transactions on Image Processing
Computer Methods and Programs in Biomedicine
Segmentation of ultrasound images of the carotid using RANSAC and cubic splines
Computer Methods and Programs in Biomedicine
A non-local fuzzy segmentation method: Application to brain MRI
Pattern Recognition
Image clustering using improved spatial fuzzy C-means
Proceedings of the 6th International Conference on Ubiquitous Information Management and Communication
An Integrated System for the Segmentation of Atherosclerotic Carotid Plaque
IEEE Transactions on Information Technology in Biomedicine
IEEE Transactions on Information Technology in Biomedicine
Carotid artery image segmentation using modified spatial fuzzy c-means and ensemble clustering
Computer Methods and Programs in Biomedicine
Computer Methods and Programs in Biomedicine
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In this paper, a robust method is proposed for segmentation of medical images by exploiting the concept of information gain. Medical images contain inherent noise due to imaging equipment, operating environment and patient movement during image acquisition. A robust medical image segmentation technique is thus inevitable for accurate results in subsequent stages. The clustering technique proposed in this work updates fuzzy membership values and cluster centroids based on information gain computed from the local neighborhood of a pixel. The proposed approach is less sensitive to noise and produces homogeneous clustering. Experiments are performed on medical and non-medical images and results are compared with state of the art segmentation approaches. Analysis of visual and quantitative results verifies that the proposed approach outperforms other techniques both on noisy and noise free images. Furthermore, the proposed technique is used to segment a dataset of 300 real carotid artery ultrasound images. A decision system for plaque detection in the carotid artery is then proposed. Intima media thickness (IMT) is measured from the segmented images produced by the proposed approach. A feature vector based on IMT values is constructed for making decision about the presence of plaque in carotid artery using probabilistic neural network (PNN). The proposed decision system detects plaque in carotid artery images with high accuracy. Finally, effect of the proposed segmentation technique has also been investigated on classification of carotid artery ultrasound images.