Vellore Institute of Technology (VIT) is a private university located in&nbsp;Tamil Nadu, India. Founded in 1984, as Vellore Engineering College, the institution offers 20 undergraduate, 34 postgraduate, four integrated and four research programs. It has campuses in Vellore, Amravati, Bhopal and Chennai.

VIT is one of the top ranked private universities in India according to NIRF, THE and QS Rankings.&nbsp;Govt. of India has recognized&nbsp;VIT, Vellore as an&nbsp;Institution of Eminence. This has allowed VIT to take independent quality initiatives and move up in world ranking.

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VIT University

Segmentation of brain MR image tissues has been a challenge because of the embedded nonlinear bias field acquired during the image acquisition process. This problem is further compounded due to the presence of noise. In order to deal with such issues, we have proposed a Variable Variance Adaptive Mean-Shift (VVAMS) algorithm which not only removes noise but also reinforces the clustering attribute by its mode seeking ability. We have formulated the problem for jointly estimating the bias field, tissue class labels and noise free pixels. Since, all the parameters are unknown and interdependent it is hard to obtain optimal estimates. In this regard, we have proposed a recursive framework to obtain the estimates of the parameters, which are partial optimal ones. In the first step of the recursion, the possibilistic fuzzy clustering algorithms has been applied to determine different clusters and bias field. These clusters are noisy and hence in the second step of the recursion, VVAMS algorithm has been applied on each cluster to eliminate noise and reinforce the modes of the clusters. These two steps constitute one combined iteration. Theoretically, the recursive framework is supposed to converge after large number of recursions but in practice it converges after a few iterations. This proposed scheme has successfully been tested with 50 biased noisy slices from Brainweb database and some real brain MR image data from IBSR database. The results have been quantitatively evaluated by percentage of misclassification, Rand Index, t-test, fuzzy partition coefficient (Vpc), fuzzy partition entropy (Vpe) and Tanimoto index. The quantitative evaluations of the tissue class labels demonstrate the superiority of proposed scheme over the existing methods. © 2017 Elsevier Ltd

School of Electronics Engineering

Chennai Campus

World University root node

Expert Systems with Applications

Variable Variance Adaptive Mean-Shift and possibilistic fuzzy C-means based recursive framework for brain MR image segmentation

Estimation of bias field together with the tissue class of a noisy Magnetic Resonance image has been a challenging task because of the nonlinear nature of bias field. In order to address this issue we have proposed two new schemes. The first one is the recursive framework, where class labels and bias fields have been estimated simultaneously. In one part of the recursion, a variable variance Adaptive Meanshift based Algorithm (VVAMS) with inherent mode seeking ability has been proposed to eliminate noise. In the other part of the recursion, class labels have been estimated using the modified possibilistic fuzzy algorithms of Jeetashree et al. [1]. The proposed scheme has successfully been tested with a large number of noisy slices from Brainweb database. The quantitative evaluation of the segmented image demonstrates the improved performance of the proposed algorithm as compared to other existing methods. © 2017 IEEE.

2017 4th International Conference on Signal Processing and Integrated Networks, SPIN 2017

A recursive framework for bias field estimation and segmentation of noisy brain MR images

The brain magnetic resonance (MR) image has an embedded bias field. This field needs to be corrected to obtain the actual MR image for classification. Bias field, being a slowly varying nonlinear field, needs to be estimated. In this paper, we have proposed three schemes and in turn three algorithms to segment the given MR image while estimating the bias field. The problem is compounded when the MR image is corrupted with noise in addition to the inherent bias field. The notions of possibilistic and fuzzy membership have been combined to take care of the modeling of the bias field and noise. The weighted typicality measure together with the weighted fuzzy membership has been used to model the image. The above resulted in the proposed Bias Corrected Possibilistic Fuzzy C-Means (BCPFCM) strategy and the algorithm. Further reinforcing the neighbourhood data to the modeling aspect has resulted in the two other strategies namely Bias Corrected Possibilistic Neighborhood Fuzzy C-Means (BCPNFCM) and Bias Corrected Separately weighted Possibilistic Neighborhood Fuzzy C-Means (BCSPNFCM). The proposed algorithms have successfully been tested with synthetic data with bias field of low and high spatial frequency. Noisy brain MR images with Gaussian Noise of varying strength have been considered from the BrainWeb database. The algorithms have also been tested on real brain MR data set with axial and sagittal view and it has been found that the proposed algorithms produced segmentation results with less percentage of misclassification errors as compared to the Bias Corrected Fuzzy C-Means (BCFCM) algorithm proposed by Ahmed et al. [4]. The performance of the proposed algorithms has been compared with algorithms from other paradigm in the context of Tanimoto's index. © 2016 Elsevier B.V. All rights reserved.

Applied Soft Computing Journal

Modified possibilistic fuzzy C-means algorithms for segmentation of magnetic resonance image

The brain Magnetic Resonance (MR) image has an embedded bias field. This field need to be corrected to obtain the actual MR image for classification. In this paper, we have proposed three new schemes to simultaneously estimate the bias field and obtain segmentation. These algorithms are modification of Ahmed et al.'s [4] Bias Corrected FCM (BCFCM) algorithm. The first proposed scheme considers the weighted typicality measure for the data set. This results in Bias Corrected Possibilistic FCM (BCPFCM) algorithm. Besides, to improve the segmentation accuracy, we have considered the joint effect of weighted membership and typicality of the neighborhood pixels resulting in Bias Corrected Possibilistic Neighborhood FCM (BCPNFCM) algorithm. The third notion considers the weighted membership and weighted typicality separately weighted for the neighboring pixels in addition to the pixel in consideration. The corresponding algorithm is Bias Corrected Separately weighted Possibilistic Neighborhood FCM (BCSPNFCM) algorithm. The proposed algorithms have successfully been tested on synthetic image and also found to produce appreciable results in case of axial brain MR image data as compared to BCFCM algorithm. © 2015 IEEE.