Fuzzy C means integrated with spatial information and contrast enhancement for segmentation of MR brain images

Autor:	R. Shantha Selva Kumari, R. Meena Prakash
Rok vydání:	2016
Předmět:	Pixel business.industry Scale-space segmentation 02 engineering and technology Image segmentation 030218 nuclear medicine & medical imaging Electronic Optical and Magnetic Materials Euclidean distance 03 medical and health sciences 0302 clinical medicine Computer Science::Computer Vision and Pattern Recognition 0202 electrical engineering electronic engineering information engineering Median filter 020201 artificial intelligence & image processing Segmentation Computer vision Computer Vision and Pattern Recognition Artificial intelligence Electrical and Electronic Engineering business Cluster analysis Software Membership function Mathematics
Zdroj:	International Journal of Imaging Systems and Technology. 26:116-123
ISSN:	0899-9457
Popis:	This paper proposes a fully automated method for MR brain image segmentation into Gray Matter, White Matter and Cerebro-spinal Fluid. It is an extension of Fuzzy C Means Clustering Algorithm which overcomes its drawbacks, of sensitivity to noise and inhomogeneity. In the conventional FCM, the membership function is computed based on the Euclidean distance between the pixel and the cluster center. It does not take into consideration the spatial correlation among the neighboring pixels. This means that the membership values of adjacent pixels belonging to the same cluster may not have the same range of membership value due to the contamination of noise and hence misclassified. Hence, in the proposed method, the membership function is convolved with mean filter and thus the local spatial information is incorporated in the clustering process. The method further includes pixel re-labeling and contrast enhancement using non-linear mapping to improve the segmentation accuracy. The proposed method is applied to both simulated and real T1-weighted MR brain images from BrainWeb and IBSR database. Experiments show that there is an increase in segmentation accuracy of around 30% over the conventional methods and 6% over the state of the art methods.
Databáze:	OpenAIRE
Externí odkaz:	https://explore.openaire.eu/search/publication?articleId=doi_________::2e2802d08b00900925557eca0ed94b74 https://doi.org/10.1002/ima.22166 Zobrazit plný text záznamu Plný text ve formátu PDF Plný text ve formátu HTML
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