A Hybrid Approach to MR Imaging Segmentation Using Unsupervised Clustering and Approximate Reducts

Widz, Sebastian; Revett, Kenneth; Ślęzak, Dominik

doi:10.1007/11548706_39

Cited by 10 publications

(6 citation statements)

References 17 publications

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“…Our approach forms the basis of a comprehensive automated segmentation algorithm. First, voxels not subject to PVE may be identified and segmented using previously developed methodology [14,15]. The remaining voxels, presumed to suffer from PVE, may be processed accordingly.…”

Section: Resultsmentioning

confidence: 99%

“…We put the voxel's value as 1 in case inhomogeneity is detected and 0 otherwise. MAGNITUDE attributes are derived using the magnitude histogram clustering [14]. Its idea is to produce clusters as intervals around the histogram's peaks and within the gaps.…”

Section: Data Preparationmentioning

confidence: 99%

“…Results are obtained for the data from Simulated Brain Database (SBD) 1 . They can be incorporated with our previous work on MRI segmentation [14,15].…”

Section: Introductionmentioning

confidence: 99%

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A Rough Set-Based Magnetic Resonance Imaging Partial Volume Detection System

Widz¹,

Revett

Ślęzak

2005

Lecture Notes in Computer Science

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Segmentation of magnetic resonance imaging (MRI) data entails assigning tissue class labels to voxels. The primary source of segmentation error is the partial volume effect (PVE) which occurs most often with low resolution imaging -With large voxels, the probability of a voxel containing multiple tissue classes increases. Although the PVE problem has not been solved, the first stage entails correctly identifying PVE voxels. We employ rough sets to identify them automatically.

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Section: Resultsmentioning

confidence: 99%

Section: Data Preparationmentioning

confidence: 99%

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A Rough Set-Based Magnetic Resonance Imaging Partial Volume Detection System

Widz¹,

Revett

Ślęzak

2005

Lecture Notes in Computer Science

Self Cite

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“…This approach is relatively new as compared to connectionist and fuzzy set theoretic approaches. Rough set theory has been applied successfully to pattern classification [10,32,122,123,289,314,320,322,323], clustering [15,82,142,196,207,208,256], feature selection [71,75,211,214,319,321,323], microarray data analysis [68,98,211,318,324,350], prediction of biological activity of molecules [210], and image processing [158,235,259,[363][364][365].…”

Section: Relevance Of Soft Computingmentioning

confidence: 99%

Introduction to Pattern Recognition and Bioinformatics

Maji

Paul

2014

Scalable Pattern Recognition Algorithms

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“…The segmentation of MR images using fuzzy c-means has been reported in [9,13,19,20]. Image segmentation using rough sets has also been done [21,22,23,24,25].…”

Section: Introductionmentioning

confidence: 99%

Maximum Class Separability for Rough-Fuzzy C-Means Based Brain MR Image Segmentation

Maji

Pal

2008

Transactions on Rough Sets IX

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Abstract. Image segmentation is an indispensable process in the visualization of human tissues, particularly during clinical analysis of magnetic resonance (MR) images. In this paper, the rough-fuzzy c-means (RFCM) algorithm is presented for segmentation of brain MR images. The RFCM algorithm comprises a judicious integration of the rough sets, fuzzy sets, and c-means algorithm. While the concept of lower and upper approximations of rough sets deals with vagueness and incompleteness in class definition of brain MR images, the membership function of fuzzy sets enables efficient handling of overlapping classes. The crisp lower bound and fuzzy boundary of a class, introduced in the RFCM algorithm, enable efficient segmentation of brain MR images. One of the major issues of the RFCM based brain MR image segmentation is how to select initial prototypes of different classes or categories. The concept of discriminant analysis, based on the maximization of class separability, is used to circumvent the initialization and local minima problems of the RFCM. Some quantitative indices are introduced to extract local features of brain MR images for accurate segmentation. The effectiveness of the RFCM algorithm, along with a comparison with other related algorithms, is demonstrated on a set of brain MR images.

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A Hybrid Approach to MR Imaging Segmentation Using Unsupervised Clustering and Approximate Reducts

Cited by 10 publications

References 17 publications

A Rough Set-Based Magnetic Resonance Imaging Partial Volume Detection System

A Rough Set-Based Magnetic Resonance Imaging Partial Volume Detection System

Introduction to Pattern Recognition and Bioinformatics

Maximum Class Separability for Rough-Fuzzy C-Means Based Brain MR Image Segmentation

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