Efficient Feature Extraction for Fast Segmentation of MR Brain Images
Automated brain MR image segmentation is a challenging problem and received significant attention lately. Various techniques have been proposed, several improvements have been made to the standard fuzzy c-means (FCM) algorithm, in order to reduce its sensitivity to Gaussian, impulse, and intensity non-uniformity noises. In this paper we present a modified FCM algorithm, which aims at accurate segmentation in case of mixed noises, and performs at a high processing speed. As a first step, a scalar feature value is extracted from the neighborhood of each pixel, using a filtering technique that deals with both spatial and gray level distances. These features are clustered afterwards using the histogram-based approach of the enhanced FCM algorithm. The experiments using 2-D synthetic phantoms and real MR images show, that the proposed method provides better results compared to other reported FCM-based techniques. The produced segmentation and fuzzy membership values can serve as excellent support for level set based cortical surface reconstruction techniques.
Keywordsimage segmentation fuzzy c-means algorithm feature extraction noise elimination magnetic resonance imaging
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