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

Abstract

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.

Brightness preserving optimized weighted bi‐histogram equalization algorithm and its application to MR brain image segmentation

“…Computerized detection and segmentation of brain region and brain abnormalities are very essential in the context of accurate measurement. Segmentation of GM, WM, and CSF is also very effective on several diseases …”

“…Segmentation of GM, WM, and CSF is also very effective on several diseases. 24 Fuzzy level set algorithm A fuzzy level set algorithm 25 is a fully automated technique for MR brain image segmentation into GM, WM, and CSF. It starts with spatial fuzzy clustering, whose results are exploited to initiate level set segmentation, estimate controlling parameters, and regularize level set evolution.…”

Multiple Sclerosis Diagnosis with Fuzzy C-Means

“…The suggestion of clustering is to identify natural groupings of data from a large dataset to create a concise representation of a system's behavior. Fuzzy c-means (FCM) is a data clustering algorithm in which a data set is grouped into n clusters with any data point in the data set belonging to every cluster to the main degrees [17]. The FCM algorithm assumes that each point belongs to more than one cluster with given a dataset …”

Segmentation of Multiple Sclerosis Lesion in Brain MR Images Using Fuzzy C-Means

“…The suggestion of clustering is to identify natural groupings of data from a large dataset to create a concise representation of a system's behavior. Semi-supervised classification algorithms based on fuzzy c-means (FCM) clustering in which a data set is grouped into n clusters with any data point in the data set belonging to every cluster to the main degrees [20]. The FCM algorithm assumes that each spike concerns to more than one cluster with given a dataset…”