Conference Papers
Permanent URI for this collectionhttps://idr.nitk.ac.in/handle/123456789/28506
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Item A new nonlocal maximum likelihood estimation method for denoising magnetic resonance images(2013) Rajan, J.; den Dekker, A.J.; Juntu, J.; Sijbers, J.Denoising of Magnetic Resonance images is important for proper visual analysis, accurate parameter estimation, and for further preprocessing of these images. Maximum Likelihood (ML) estimation methods were proved to be very effective in denoising Magnetic Resonance (MR) images. Among the ML based methods, the recently proposed Non Local Maximum Likelihood (NLML) approach gained much attention. In the NLML method, the samples for the ML estimation of the true underlying intensity are selected in a non local way based on the intensity similarity of the pixel neighborhoods. This similarity is generally measured using the Euclidean distance. A drawback of this approach is the usage of a fixed sample size for the ML estimation and, as a result, optimal results cannot be achieved because of over- or under-smoothing. In this work, we propose an NLML estimation method for denoising MR images in which the samples are selected in an adaptive way using the Kolmogorov-Smirnov (KS) similarity test. The method has been tested both on simulated and real data, showing its effectiveness. © Springer-Verlag 2013.Item A semi-automatic method for carotid artery wall segmentation in MR images(Institute of Electrical and Electronics Engineers Inc., 2017) Kumar, P.K.; Kesavadas, C.; Rajan, J.The quantification of carotid artery stenosis via imaging techniques guides the physicians to take a decision regarding surgical interventions. The measurement of wall thickness from magnetic resonance (MR) images is a promising approach to measure the degree of carotid stenosis. Manual tracing of the carotid vessel walls is time consuming and is sensitive to observer variability. Further, the existing segmentation techniques are limited by the poor contrast and presence of noise in MR images. The objective this paper is to present a novel segmentation strategy for carotid lumen and outer wall from MR images. The segmentation has been carried out in two stages which starts with a user assisted region of interest selection. In the first stage, an active contour based global segmentation has been applied to classify the lumen region. In the second stage, morphological gradient of the region of interest has been computed. This is followed by particle swarm optimization based localized segmentation to separate the wall region. The results demonstrate excellent correspondence between the automatic and manual tracings for lumen and outer walls of the carotid artery. © 2016 IEEE.