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Journal ArticleDOI

Routine quantitative analysis of brain and cerebrospinal fluid spaces with MR imaging

TLDR
A computerized system for processing spin‐echo magnetic resonance (MR) imaging data was implemented to estimate whole brain and cerebrospinal fluid volumes and to display three‐dimensional surface reconstructions of specified tissue classes, showing good reliability for the automated segmentation procedures.
Abstract
A computerized system for processing spin-echo magnetic resonance (MR) imaging data was implemented to estimate whole brain (gray and white matter) and cerebrospinal fluid volumes and to display three-dimensional surface reconstructions of specified tissue classes. The techniques were evaluated by assessing the radiometric variability of MR volume data and by comparing automated and manual procedures for measuring tissue volumes. Results showed (a) the homogeneity of the MR data and (b) that automated techniques were consistently superior to manual techniques. Both techniques, however, were affected by the complexity of the structure, with simpler structures (eg, the intracranial cavity) showing less variability and better spatial correlation of segmentation results between raters. Moreover, the automated techniques were completed for whole brain in a fraction of the time required to complete the equivalent segmentation manually. Additional evaluations included interrater reliability and an evaluation that included longitudinal measurement, in which one subject was imaged sequentially 24 times, with reliability computed from data collected by three raters over 1 year. Results showed good reliability for the automated segmentation procedures.

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Citations
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Simultaneous truth and performance level estimation (STAPLE): an algorithm for the validation of image segmentation

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Journal ArticleDOI

Adaptive segmentation of MRI data

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Journal ArticleDOI

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Geometry-Driven Diffusion in Computer Vision

TL;DR: This paper presents a meta-analyses of differential Invariant Signatures and Flows in Computer Vision: a Symmetry Group approach P. Sapiro, A. Tannenbaum, and a Differential Geometric Approach to Anisotropic Diffusion.
References
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Journal ArticleDOI

Nonlinear anisotropic filtering of MRI data

TL;DR: In contrast to acquisition-based noise reduction methods a postprocess based on anisotropic diffusion is proposed, which overcomes the major drawbacks of conventional filter methods, namely the blurring of object boundaries and the suppression of fine structural details.
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Abnormalities of the left temporal lobe and thought disorder in schizophrenia. A quantitative magnetic resonance imaging study.

TL;DR: New MRI neuroimaging techniques are used to derive volume measurements and three-dimensional reconstructions of temporal-lobe structures in vivo in 15 right-handed men with chronic schizophrenia and 15 matched controls to discover the degree of thought disorder is related to the size of the reduction in volume of the left posterior superior temporal gyrus.
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Three-dimensional segmentation of MR images of the head using probability and connectivity.

TL;DR: A three-dimensional (3D) segmentation method that comprises user interactive identification of tissue classes, calculation of a probability distribution for each tissue, and creation of a feature map of the most probable tissues is described.
Journal ArticleDOI

Two algorithms for the three-dimensional reconstruction of tomograms.

TL;DR: It is believed that the normalized gradient of the original values in the CT or MRI tomograms provides a better estimate for the surface normal and hence results in higher quality 3-D images.
Journal ArticleDOI

Analysis of brain and cerebrospinal fluid volumes with MR imaging. Part I. Methods, reliability, and validation.

TL;DR: The authors believe that their technique to analyze MR images of the brain performed with acceptable levels of accuracy and reliability and that it can be used to measure brain and CSF volumes for clinical research.
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