Generalized rough fuzzy c-means algorithm for brain MR image segmentation

Fuzzy sets and rough sets have been widely used in many clustering algorithms for medical image segmentation, and have recently been combined together to better deal with the uncertainty implied in observed image data. Despite of their wide spread applications, traditional hybrid approaches are sens...

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Vydáno v:Computer methods and programs in biomedicine Ročník 108; číslo 2; s. 644 - 655
Hlavní autoři: Ji, Zexuan, Sun, Quansen, Xia, Yong, Chen, Qiang, Xia, Deshen, Feng, Dagan
Médium: Journal Article
Jazyk:angličtina
Vydáno: Kidlington Elsevier Ireland Ltd 01.11.2012
Elsevier
Témata:
Algorithms
Bias
Biological and medical sciences
Brain
Brain - anatomy & histology
Brain magnetic resonance image
Clustering
Fuzzy c-means algorithm
Fuzzy Logic
Fuzzy set theory
Humans
Image segmentation
Intensity inhomogeneity
Internal Medicine
Magnetic Resonance Imaging - methods
Mathematical models
Medical sciences
Other
Radiotherapy. Instrumental treatment. Physiotherapy. Reeducation. Rehabilitation, orthophony, crenotherapy. Diet therapy and various other treatments (general aspects)
Rough set
Rough-fuzzy c-means algorithm
Technology. Biomaterials. Equipments. Material. Instrumentation
Image segmentation
Intensity inhomogeneity
Fuzzy c-means algorithm
Brain magnetic resonance image
Rough-fuzzy c-means algorithm
Rough set
Image processing
Nuclear magnetic resonance imaging
Biomedical engineering
ISSN:0169-2607, 1872-7565, 1872-7565
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Shrnutí:Fuzzy sets and rough sets have been widely used in many clustering algorithms for medical image segmentation, and have recently been combined together to better deal with the uncertainty implied in observed image data. Despite of their wide spread applications, traditional hybrid approaches are sensitive to the empirical weighting parameters and random initialization, and hence may produce less accurate results. In this paper, a novel hybrid clustering approach, namely the generalized rough fuzzy c-means (GRFCM) algorithm is proposed for brain MR image segmentation. In this algorithm, each cluster is characterized by three automatically determined rough-fuzzy regions, and accordingly the membership of each pixel is estimated with respect to the region it locates. The importance of each region is balanced by a weighting parameter, and the bias field in MR images is modeled by a linear combination of orthogonal polynomials. The weighting parameter estimation and bias field correction have been incorporated into the iterative clustering process. Our algorithm has been compared to the existing rough c-means and hybrid clustering algorithms in both synthetic and clinical brain MR images. Experimental results demonstrate that the proposed algorithm is more robust to the initialization, noise, and bias field, and can produce more accurate and reliable segmentations.
Bibliografie:ObjectType-Article-1
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content type line 23
ISSN:0169-2607
1872-7565
1872-7565
DOI:10.1016/j.cmpb.2011.10.010