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.

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 cluster's 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.

Segmentation of gadolinium-enhanced lesions on MRI in multiple sclerosis.

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 postcontrast T1-weighted images based on morphological operations.

Fast quantification of proton magnetic resonance spectroscopic imaging with artificial neural networks.

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 for quantifying the phase corrected (as opposed to magnitude) spectra.

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.

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 (> or =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.

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.

A graphical user interface for automatic image registration software designed for radiotherapy treatment planning.

Medical imaging forms a vital component of radiotherapy treatment planning and its evaluation. The integration of the useful data obtained from multiple imaging modalities for radiotherapy planning is achieved by image registration softwares. In radiotherapy planning systems, normally the computed tomography (CT) slices are kept as a standard upon which other modality images (magnetic resonance imaging [MRI], single photon emission computed tomography [SPECT], positron emission tomography [PET], etc.

Multicentre proton magnetic resonance spectroscopy imaging of primary progressive multiple sclerosis.

Multicentre baseline proton magnetic resonance spectroscopic data on primary progressive multiple sclerosis (PPMS) patients are acquired and analysed, using automatic analysis software. The metabolite ratios did not differ from centre to centre. The average N-acetylaspartate/creatine (NAA/Cr) ratio in PPMS was significantly lower compared to normal controls.

Brain abscesses: etiologic categorization with in vivo proton MR spectroscopy.

Purpose: To compare the metabolite patterns observed at in vivo proton magnetic resonance (MR) spectroscopy of brain abscesses in patients for whom bacteriologic information was obtained from cultures and to categorize the MR spectral patterns with respect to the underlying etiologic agents.

Materials And Methods: MR imaging and in vivo single-voxel proton MR spectroscopic data obtained from 75 patients with brain abscesses were retrospectively analyzed. Ex vivo spectroscopic experiments with the pus from 45 of these patients also were performed, and the data were further categorized on the basis of bacteriologic information.

Magnetization transfer and T2 quantitation in normal appearing cortical gray matter and white matter adjacent to focal abnormality in patients with traumatic brain injury.

Traumatic brain injury (TBI) is one of the commonest causes of morbidity and mortality in the developed countries with posttraumatic epilepsy and functional disability being its major sequelae. The purpose of this study was to test the hypothesis whether the normal appearing adjacent gray and white matter regions on T2 and T1 weighted magnetization transfer (MT) weighted images show any abnormality on quantitative imaging in patients with TBI. A total of 51 patients with TBI and 10 normal subjects were included in this study.