Graphical Models and Image Processing
Scale-based fuzzy connected image segmentation: theory, algorithms, and validation
Computer Vision and Image Understanding - Special issue on analysis of volumetric image
Pattern Recognition with Fuzzy Objective Function Algorithms
Pattern Recognition with Fuzzy Objective Function Algorithms
Affinity functions in fuzzy connectedness based image segmentation I: Equivalence of affinities
Computer Vision and Image Understanding
Computer Vision and Image Understanding
Computer Vision and Image Understanding
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Image segmentation is an important research topic in image processing and computer vision community. In this paper, a new unsupervised method for MR brain image segmentation is proposed based on fuzzy c-means (FCM) and fuzzy connectedness. FCM is a widely used unsupervised clustering algorithm for pattern recognition and image processing problems. However, FCM does not consider the spatial coherence of images and is sensitive to noise. On the other hand, fuzzy connectedness method has achieved good performance for medical image segmentation. However, in the computation of fuzzy connectedness, one needs to select seeds manually which is elaborative and time-consuming. Our new method used FCM as the first step to select salient seeded points and then applied fuzzy connectedness algorithm based on those seeds. Thus our method achieved unsupervised automatic segmentation for brain MR images. Experiments on simulated and real data sets proved it is effective and robust to noise.