Fully-automated approach to hippocampus segmentation using a graph-cuts algorithm combined with atlas-based segmentation and morphological opening

Kwak, Kichang; Yoon, Uicheul; Lee, Dong-Kyun; Kim, Geon Ha; Seo, Sang Won; Na, Duk L.; Shim, Hackjoon; Lee, Jong-Min

doi:10.1016/j.mri.2013.04.008

Cited by 44 publications

(32 citation statements)

References 45 publications

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“…Hippocampus segmentation was performed separately using an automated method based on the graph-cuts algorithm [61] combined with atlas-based segmentation and morphological opening [62]. A priori information combining atlas-based segmentation with estimation of partial volume probabilities at each voxel was applied to define the initial hippocampal region for the graph-cuts algorithm in this framework.…”

Section: Methodsmentioning

confidence: 99%

Multimodal Discrimination of Alzheimer’s Disease Based on Regional Cortical Atrophy and Hypometabolism

et al. 2015

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Structural MR image (MRI) and 18F-Fluorodeoxyglucose-positron emission tomography (FDG-PET) have been widely employed in diagnosis of both Alzheimer’s disease (AD) and mild cognitive impairment (MCI) pathology, which has led to the development of methods to distinguish AD and MCI from normal controls (NC). Synaptic dysfunction leads to a reduction in the rate of metabolism of glucose in the brain and is thought to represent AD progression. FDG-PET has the unique ability to estimate glucose metabolism, providing information on the distribution of hypometabolism. In addition, patients with AD exhibit significant neuronal loss in cerebral regions, and previous AD research has shown that structural MRI can be used to sensitively measure cortical atrophy. In this paper, we introduced a new method to discriminate AD from NC based on complementary information obtained by FDG and MRI. For accurate classification, surface-based features were employed and 12 predefined regions were selected from previous studies based on both MRI and FDG-PET. Partial least square linear discriminant analysis was employed for making diagnoses. We obtained 93.6% classification accuracy, 90.1% sensitivity, and 96.5% specificity in discriminating AD from NC. The classification scheme had an accuracy of 76.5% and sensitivity and specificity of 46.5% and 89.6%, respectively, for discriminating MCI from AD. Our method exhibited a superior classification performance compared with single modal approaches and yielded parallel accuracy to previous multimodal classification studies using MRI and FDG-PET.

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Section: Methodsmentioning

confidence: 99%

Multimodal Discrimination of Alzheimer’s Disease Based on Regional Cortical Atrophy and Hypometabolism

et al. 2015

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“…In the first test set, ADNI D , with manual labels traced with the same protocol of ADNI T , the segmentation results were excellent with median Dice index 0.929 0.003 = ± for both left and right hippocampi. In the second test set OASIS D , with tracings obtained with a different protocol, the performance of HUMAN (Dice index 0.869 0.002 ± ) were less impressive, but yet satisfactory if compared with other recently reported studies (Cardoso et al 2013, Kwak et al 2013, Pipitone et al 2014. While the best performing methods of MICCAI SATA challenge reported Dice indexes approaching 0.90 median values, based on its performance on OASIS D , HUMAN would have still placed itself among the best five performing algorithms 13 .…”

Section: Discussionmentioning

confidence: 76%

Hippocampal unified multi-atlas network (HUMAN): protocol and scale validation of a novel segmentation tool

et al. 2015

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In this study we present a novel fully automated Hippocampal Unified Multi-Atlas-Networks (HUMAN) algorithm for the segmentation of the hippocampus in structural magnetic resonance imaging. In multi-atlas approaches atlas selection is of crucial importance for the accuracy of the segmentation. Here we present an optimized method based on the definition of a small peri-hippocampal region to target the atlas learning with linear and non-linear embedded manifolds. All atlases were co-registered to a data driven template resulting in a computationally efficient method that requires only one test registration. The optimal atlases identified were used to train dedicated artificial neural networks whose labels were then propagated and fused to obtain the final segmentation. To quantify data heterogeneity and protocol inherent effects, HUMAN was tested on two independent data sets provided by the Alzheimer's Disease Neuroimaging Initiative and the Open Access

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“…remove black spots). They smooth the outline of the object, and at the same time ensure that it does not produce global geometric distortion [1,10].…”

Section: Boundary Point Extractionmentioning

confidence: 99%

Contour Extraction based on Circular Hough Transform for Forest Canopy Digital Hemispherical Photography

Zhu¹

2018

IJPE

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Analysis of forest canopy hemisphere images is one of the most important methods for measuring forest canopy structure parameters. This paper focuses on improvements to contour extraction method for the circular imaging region in forest canopy. We proposed an effective contour extraction algorithm based on circular hough transform for the circular imaging region of digital canopy hemispherical photography (DHP). In order to eliminate the threshold segmentation error, the morphological opening and closing was used to remove these desultory points and smooth the outline of the imaging region. After the boundary points of the imaging area were extracted through erosion, the circular hough transform was applied to fit the boundary point for finding the position of the center and gaining the radius. The experimental results proved its feasibility in terms of effective implementation and robustness.

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Fully-automated approach to hippocampus segmentation using a graph-cuts algorithm combined with atlas-based segmentation and morphological opening

Cited by 44 publications

References 45 publications

Multimodal Discrimination of Alzheimer’s Disease Based on Regional Cortical Atrophy and Hypometabolism

Multimodal Discrimination of Alzheimer’s Disease Based on Regional Cortical Atrophy and Hypometabolism

Hippocampal unified multi-atlas network (HUMAN): protocol and scale validation of a novel segmentation tool

Contour Extraction based on Circular Hough Transform for Forest Canopy Digital Hemispherical Photography

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