scispace - formally typeset
Journal ArticleDOI

Segmentation of subcortical brain structures using fuzzy templates

Juan Helen Zhou, +1 more
- 01 Dec 2005 - 
- Vol. 28, Iss: 4, pp 915-924
TLDR
A novel method to automatically segment subcortical structures of human brain in magnetic resonance images by using fuzzy templates that does not require specific expert definition of each structure or manual interactions during segmentation process is proposed.
About
This article is published in NeuroImage.The article was published on 2005-12-01. It has received 70 citations till now. The article focuses on the topics: Scale-space segmentation & Segmentation.

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Citations
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Journal ArticleDOI

Patch-based segmentation using expert priors: application to hippocampus and ventricle segmentation.

TL;DR: Inspired by recent work in image denoising, the proposed nonlocal patch-based label fusion produces accurate and robust segmentation in quantitative magnetic resonance analysis.
Journal ArticleDOI

Hippocampus segmentation in MR images using atlas registration, voxel classification, and graph cuts

TL;DR: Direct quantitative and qualitative comparisons showed that the proposed method outperforms a multi-atlas based segmentation method and shows significant associations with cognitive decline and dementia, similar to the manually measured volumes.
Journal ArticleDOI

Atlas Renormalization for Improved Brain MR Image Segmentation Across Scanner Platforms

TL;DR: An intensity renormalization procedure is introduced that automatically adjusts the prior atlas intensity model to new input data and reduces the sensitivity of the whole brain segmentation method to changes in scanner platforms and improves its accuracy and robustness.
Journal ArticleDOI

An evaluation of four automatic methods of segmenting the subcortical structures in the brain.

TL;DR: Four novel methods of fully automated segmentation of subcortical structures using volumetric, spatial overlap and distance-based measures are evaluated and it is shown that all four methods perform on par with recently published methods.
Journal ArticleDOI

FreeSurfer-Initiated Fully-Automated Subcortical Brain Segmentation in MRI Using Large Deformation Diffeomorphic Metric Mapping

TL;DR: This work combines the probabilistic-based FreeSurfer method with the Large Deformation Diffeomorphic Metric Mapping-based label-propagation method to create a fully-automated subcortical brain segmentation method (FS+LDDMM).
References
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Journal ArticleDOI

Voxel-Based Morphometry—The Methods

TL;DR: In this paper, the authors describe the steps involved in VBM, with particular emphasis on segmenting gray matter from MR images with non-uniformity artifact and provide evaluations of the assumptions that underpin the method, including the accuracy of the segmentation and the assumptions made about the statistical distribution of the data.
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Whole brain segmentation: automated labeling of neuroanatomical structures in the human brain.

TL;DR: In this paper, a technique for automatically assigning a neuroanatomical label to each voxel in an MRI volume based on probabilistic information automatically estimated from a manually labeled training set is presented.
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Spatial registration and normalization of images

TL;DR: A general technique that facilitates nonlinear spatial (stereotactic) normalization and image realignment is presented that minimizes the sum of squares between two images following non linear spatial deformations and transformations of the voxel (intensity) values.
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Current methods in medical image segmentation.

TL;DR: A critical appraisal of the current status of semi-automated and automated methods for the segmentation of anatomical medical images is presented, with an emphasis on the advantages and disadvantages of these methods for medical imaging applications.
Journal ArticleDOI

Nonlinear spatial normalization using basis functions

TL;DR: A fast algorithm has been developed that utilizes Taylor's theorem and the separable nature of the basis functions, meaning that most of the nonlinear spatial variability between images can be automatically corrected within a few minutes.
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