Balasrinivasa Rao Sajja

Unified Approach for Multiple Sclerosis Lesion Segmentation on Brain MRI

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 the information from the proton density (PD)- and T2-weighted and fluid attenuation inversion recovery (FLAIR) images. This strategy involves CSF and lesion classification using the Parzen window classifier. Image processing, morphological operations, and ratio maps of PD- and T2-weighted images are used for minimizing false positives. Contextual information is exploited for minimizing the false negative lesion classifications using hidden Markov random field-expectation maximization (HMRF-EM) algorithm. Lesions are delineated using fuzzy connectivity. The performance of this algorithm is quantitatively evaluated on 23 MS patients. Similarity index, percentages of over, under, and correct estimations of lesions are computed by spatially comparing the results of present procedure with expert manual segmentation. The automated processing scheme detected 80% of the manually segmented lesions in the case of low lesion load and 93% of the lesions in those cases with high lesion load.

Segmentation and quantification of black holes in multiple sclerosis.

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 masking the segmented images with images obtained from the orthogonalization of T2-weighted and T1 images. Enhancing lesion voxels on postcontrast images were automatically identified and eliminated from being included in the black hole volume. Fuzzy connectivity was used for the delineation of black holes. The performance of this algorithm was quantitatively evaluated on 14 MS patients.

Longitudinal magnetic resonance spectroscopic imaging of primary progressive multiple sclerosis patients treated with glatiramer acetate: Multicenter study

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 followed over a period of 3 years. MRSI data were acquired by all the centers using the same pulse sequence, and spectral analysis was performed at a single site using a customized analysis software package. Quantitative metabolite ratios, N-acetyl aspartate (NAA)/creatine (Cr) and choline (Cho)/Cr, were compared between GA-treated and placebo-treated PPMS patients. There was no significant difference in metabolite ratios between GA-treated and placebo-treated patients. The difference in metabolite ratios between the normal-appearing tissues (NAT) and lesion-containing regions (LCR) in GA treated patients was not significantly different from placebo treated patients. Strong lipid resonances, even in the absence of lesions, were observed on MRSI data in both gray matter and white matter in placebo- and GA-treated PPMS patients. No significant difference in number of patients with lipids between the two groups over a period of 3 years was found. Multiple Sclerosis 2008; 14: 73—80. http://msj.sagepub.com

Generalized fuzzy clustering for segmentation of multi-spectral magnetic resonance images.

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 clusters shape in the feature space. The bias field is modeled as a linear combination of smooth polynomial basis functions for fast computation in the clustering iterations. Regularization terms for the neighborhood continuity of intensity are added into the FCM cost functions. To reduce the computational complexity, the contextual regularizations are separated from the clustering iterations. Since the feature space is not isotropic, distance measure adopted in Gustafson-Kessel (G-K) algorithm is used instead of the Euclidean distance, to account for the non-spherical shape of the clusters in the feature space. These algorithms are quantitatively evaluated on MR brain images using the similarity measures.

Segmentation of gadolinium-enhanced lesions on MRI in multiple sclerosis

To develop and implement a method for identification and quantification of gadolinium (Gd) enhancements with minimal human intervention.

Volume and Shape in Feature Space on Adaptive FCM in MRI Segmentation

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 functions for fast computation in the clustering iterations. Regularization terms for the neighborhood continuity of either intensity or membership are added into the FCM cost functions. Since the feature space is not isotropic, distance measures, other than the Euclidean distance, are used to account for the shape and volumetric effects of clusters in the feature space. The performance of segmentation is improved by combining the adaptive FCM scheme with the criteria used in Gustafson-Kessel (G-K) and Gath-Geva (G-G) algorithms through the inclusion of the cluster scatter measure. The performance of this integrated approach is quantitatively evaluated on normal MR brain images using the similarity measures. The improvement in the quality of segmentation obtained with our method is also demonstrated by comparing our results with those produced by FSL (FMRIB Software Library), a software package that is commonly used for tissue classification.

A unified approach for lesion segmentation on MRI of multiple sclerosis

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 classifier. Image processing, morphological operations, and ratio map of proton density (PD) and T2 weighted images are used for minimizing false positives. Lesions are delineated using fuzzy connectedness principle. Contextual information was used for minimizing false negative lesion classifications. Gray and white matter classification is realized using HMRF-EM algorithm.

Implementation of High Dimensional Feature Map for Segmentation of MR Images

A method that considerably reduces the computational and memory complexities associated with the generation of high-dimensional (≥3) feature maps for image segmentation is described. The method is based on the K-nearest neighbor (KNN) classification and consists of two parts: preprocessing of feature space and fast KNN. This technique is implemented on a PC and applied for generating 3D and 4D feature maps for segmenting MR brain images of multiple sclerosis patients.

Mouse Brain Fixation to Preserve In Vivo Manganese Enhancement for Ex Vivo MEMRI

To develop a tissue fixation method that preserves in vivo manganese enhancement for ex vivo magnetic resonance imaging (MRI). The needs are clear, as conventional in vivo manganese‐enhanced MRI (MEMRI) applied to live animals is time‐limited, hence limited in spatial resolution and signal‐to‐noise ratio (SNR). Ex vivo applications can achieve superior spatial resolution and SNR through increased signal averaging and optimized radiofrequency coil designs. A tissue fixation method that preserves in vivo Mn2+ enhancement postmortem is necessary for ex vivo MEMRI.

Mouse brain fixation to preserve In vivo manganese enhancement for ex vivo manganese-enhanced MRI: Mouse Brain Fixation for Ex Vivo MEMRI

To develop a tissue fixation method that preserves in vivo manganese enhancement for ex vivo magnetic resonance imaging (MRI). The needs are clear, as conventional in vivo manganese‐enhanced MRI (MEMRI) applied to live animals is time‐limited, hence limited in spatial resolution and signal‐to‐noise ratio (SNR). Ex vivo applications can achieve superior spatial resolution and SNR through increased signal averaging and optimized radiofrequency coil designs. A tissue fixation method that preserves in vivo Mn2+ enhancement postmortem is necessary for ex vivo MEMRI.