Fuzzy c-means clustering with spatial information for image segmentation.
Keh-Shih Chuang,Hong Long Tzeng,Hong Long Tzeng,Sharon C.-A. Chen,Jay Wu,Jay Wu,Tzong-Jer Chen +6 more
TLDR
This paper presents a fuzzy c-means (FCM) algorithm that incorporates spatial information into the membership function for clustering and yields regions more homogeneous than those of other methods.About:
This article is published in Computerized Medical Imaging and Graphics.The article was published on 2006-01-01 and is currently open access. It has received 1296 citations till now. The article focuses on the topics: Fuzzy clustering & Image segmentation.read more
Citations
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Journal ArticleDOI
MRI Segmentation of the Human Brain: Challenges, Methods, and Applications
TL;DR: This paper first introduces the basic concepts of image segmentation, then explains different MRI preprocessing steps including image registration, bias field correction, and removal of nonbrain tissue.
Journal ArticleDOI
Image segmentation by generalized hierarchical fuzzy C-means algorithm
TL;DR: This paper introduces a new generalized hierarchical FCM (GHFCM), which is more robust to image noise with the spatial constraints: the generalized mean, and introduces a more flexibility function which considers the distance function itself as a sub-FCM.
Journal ArticleDOI
Integrating spatial fuzzy clustering with level set methods for automated medical image segmentation
TL;DR: A new fuzzy level set algorithm is proposed in this paper to facilitate medical image segmentation that is able to directly evolve from the initial segmentation by spatial fuzzy clustering and enhanced with locally regularized evolution.
Journal ArticleDOI
Medical Image Segmentation Methods, Algorithms, and Applications
Alireza Norouzi,Mohd Shafry Mohd Rahim,Ayman Altameem,Tanzila Saba,Abdolvahab Ehsani Rad,Amjad Rehman,Mueen Uddin +6 more
TL;DR: The latest segmentation methods applied in medical image analysis are described and the advantages and disadvantages of each method are described besides examination of each algorithm with its application in Magnetic Resonance Imaging and Computed Tomography image analysis.
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A Multiple-Kernel Fuzzy C-Means Algorithm for Image Segmentation
TL;DR: The proposed MKFCM algorithm provides a new flexible vehicle to fuse different pixel information in image-segmentation problems and is shown that two successful enhanced KFCM-based image- Segmentation algorithms are special cases ofMKFCM.
References
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A validity measure for fuzzy clustering
X.L. Xie,Gerardo Beni +1 more
TL;DR: The authors present a fuzzy validity criterion based on a validity function which identifies compact and separate fuzzy c-partitions without assumptions as to the number of substructures inherent in the data.
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A modified fuzzy c-means algorithm for bias field estimation and segmentation of MRI data
TL;DR: A novel algorithm for fuzzy segmentation of magnetic resonance imaging (MRI) data and estimation of intensity inhomogeneities using fuzzy logic and the neighborhood effect acts as a regularizer and biases the solution toward piecewise-homogeneous labelings.
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Cluster Validity with Fuzzy Sets
TL;DR: This paper uses membership function matrices associated with fuzzy c-partitions of X, together with their values in the Euclidean (matrix) norm, to formulate an a posteriori method for evaluating algorithmically suggested clusterings of X.
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Review of MR image segmentation techniques using pattern recognition.
TL;DR: This paper has reviewed, with somewhat variable coverage, the nine MR image segmentation techniques itemized in Table II; each has its merits and drawbacks.
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Adaptive fuzzy segmentation of magnetic resonance images
Dzung L. Pham,Jerry L. Prince +1 more
TL;DR: 3-D AFCM yields lower error rates than both the standard fuzzy C-means (FCM) algorithm and two other competing methods, when segmenting corrupted images, and its efficacy is further demonstrated using real 3-D scalar and multispectral MR brain images.