Balasrinivasa Rao Sajja

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The presence of large number of false lesion classification on segmented brain MR images is a major problem in the accurate determination of lesion volumes in multiple sclerosis (MS) brains. In order to minimize the false lesion classifications, a strategy that combines parametric and nonparametric techniques is developed and implemented. This approach uses(More)
Proton magnetic resonance spectroscopy ((1)H-MRS) provides tissue metabolic information in vivo. This article reviews the role of MRS-determined metabolic alterations in lesions, normal-appearing white matter, gray matter, and spinal cord in advancing our knowledge of pathologic changes in multiple sclerosis (MS). In addition, the role of MRS in objectively(More)
A technique that involves minimal operator intervention was developed and implemented for identification and quantification of black holes on T1-weighted magnetic resonance images (T1 images) in multiple sclerosis (MS). Black holes were segmented on T1 images based on grayscale morphological operations. False classification of black holes was minimized by(More)
PURPOSE To develop and implement a method for identification and quantification of gadolinium (Gd) enhancements with minimal human intervention. MATERIALS AND METHODS Dual fast spin echo (FSE), fluid attenuation inversion recovery (FLAIR), and pre- and postcontrast T1-weighted spin echo were acquired on 22 subjects. The enhancements were identified on the(More)
Accurate determination of lesion volumes on brain MR images is hampered by the presence of a large number of false positive and negative classifications. A strategy that combines parametric and nonparametric techniques is developed and implemented for minimizing the false classifications. Initially, CSF and lesions are segmented using Parzen window(More)
Purpose. To develop a technique to automate landmark selection for point-based interpolating transformations for nonlinear medical image registration. Materials and Methods. Interpolating transformations were calculated from homologous point landmarks on the source (image to be transformed) and target (reference image). Point landmarks are placed at regular(More)
An integrated approach for multi-spectral segmentation of MR images is presented. This method is based on the fuzzy c-means (FCM) and includes bias field correction and contextual constraints over spatial intensity distribution and accounts for the non-spherical cluster's shape in the feature space. The bias field is modeled as a linear combination of(More)
Multicenter proton magnetic resonance spectroscopic imaging (MRSI) studies were performed on 58 primary progressive multiple sclerosis (PPMS) patients from four centers for investigating the efficacy of glatiramer acetate (GA) treatment. These patients were drawn from 943 subjects who participated in the PROMiSe trial. In these MRSI studies, patients were(More)
Intensity non-uniformity (bias field) correction, contextual constraints over spatial intensity distribution and non-spherical cluster’s shape in the feature space are incorporated into the fuzzy c-means (FCM) for segmentation of three-dimensional multi-spectral MR images. The bias field is modeled by a linear combination of smooth polynomial basis(More)
Accurate quantification of the MRSI-observed regional distribution of metabolites involves relatively long processing times. This is particularly true in dealing with large amount of data that is typically acquired in multi-center clinical studies. To significantly shorten the processing time, an artificial neural network (ANN)-based approach was explored(More)