Journal ArticleDOI
Rough-probabilistic clustering and hidden Markov random field model for segmentation of HEp-2 cell and brain MR images
Abhirup Banerjee,Pradipta Maji +1 more
- Vol. 46, pp 558-576
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TLDR
A new clustering algorithm, termed as rough-probabilistic clustering, is presented, integrating judiciously the merits of rough sets and a new probability distribution, called stomped normal (SN) distribution, for accurate and robust segmentation of images.Citations
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Spatially Constrained Student’s t-Distribution Based Mixture Model for Robust Image Segmentation
Abhirup Banerjee,Pradipta Maji +1 more
TL;DR: A novel way to model the image as a mixture of finite number of Student’s t-distribution for image segmentation problem is presented and a novel simultaneous segmentation and bias field correction algorithm has been proposed for segmentation of magnetic resonance (MR) images.
Journal ArticleDOI
An Analytical Review on Rough Set Based Image Clustering
TL;DR: The key issues which are involved during the development of rough set based clustering models are investigated in this paper and the measures of similarity as well as the evaluation criteria for rough clustering are discussed in this study.
Journal ArticleDOI
Robust brain magnetic resonance image segmentation using modified rough-fuzzy C-means with spatial constraints
Anindya Halder,Nur Alom Talukdar +1 more
TL;DR: A novel robust clustering algorithm rough-fuzzy C-means with spatial constraints (RFCMSC) for brain MRI segmentation is proposed, which can better handle the inherent vagueness, uncertainties, overlapping, and indiscernibility present in brain MRI.
Journal ArticleDOI
Brain tissue segmentation using improved kernelized rough-fuzzy C-means with spatio-contextual information from MRI
Anindya Halder,Nur Alom Talukdar +1 more
TL;DR: A robust kernelized rough fuzzy C-means clustering with spatial constraints (KRFCMSC) is proposed in this article for brain tissue segmentation and justifies the superiority and robustness of the proposed method over other state-of-the-art methods.
Journal ArticleDOI
Immune system programming for medical image segmentation
TL;DR: A new segmentation technique is proposed to combine a new evolutionary algorithm, called the Immune System Programming (ISP) algorithm, with the Region Growing (RG) technique, which has the ability to create new mathematical threshold functions.
References
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Journal ArticleDOI
Fast robust automated brain extraction
TL;DR: An automated method for segmenting magnetic resonance head images into brain and non‐brain has been developed and described and examples of results and the results of extensive quantitative testing against “gold‐standard” hand segmentations, and two other popular automated methods.
Journal ArticleDOI
Segmentation of brain MR images through a hidden Markov random field model and the expectation-maximization algorithm
TL;DR: The authors propose a novel hidden Markov random field (HMRF) model, which is a stochastic process generated by a MRF whose state sequence cannot be observed directly but which can be indirectly estimated through observations.
Journal ArticleDOI
On the statistical analysis of dirty pictures
TL;DR: In this paper, the authors proposed an iterative method for scene reconstruction based on a non-degenerate Markov Random Field (MRF) model, where the local characteristics of the original scene can be represented by a nondegenerate MRF and the reconstruction can be estimated according to standard criteria.
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Rough-Fuzzy Clustering for Grouping Functionally Similar Genes from Microarray Data
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