Shape differences of the brain ventricles in Alzheimer's disease

Ferrarini, Luca; Palm, Walter M.; Olofsen, Hans; Buchem, Mark A. van; Reiber, J. H. C.; Admiraal-Behloul, Faiza

doi:10.1016/j.neuroimage.2006.05.048

Cited by 101 publications

(104 citation statements)

References 32 publications

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“…Zhou and Rajapaske (2005) proposed an automated method using fuzzy templates to segment different brain structures based on information extracted from a set of training image volumes. Ferrarini et al (2006) used artificial neural networks to study ventricular shape variations in healthy elderly and AD subjects, generating a control average surface and a cloud of corresponding nodes across a data set. Heckemann et al (2006) performed label propagation using free-form deformations and decision fusion to provide automated anatomical segmentations of the brain.…”

Section: Discussionmentioning

confidence: 99%

Automated ventricular mapping with multi-atlas fluid image alignment reveals genetic effects in Alzheimer's disease

Chou¹,

Leporé²,

Zubicaray

et al. 2008

NeuroImage

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We developed and validated a new method to create automated 3D parametric surface models of the lateral ventricles in brain MRI scans, providing an efficient approach to monitor degenerative disease in clinical studies and drug trials. First, we used a set of parameterized surfaces to represent the ventricles in four subjects' manually labeled brain MRI scans (atlases). We fluidly registered each atlas and mesh model to MRIs from 17 Alzheimer's disease (AD) patients and 13 age-and gendermatched healthy elderly control subjects, and 18 asymptomatic ApoE4-carriers and 18 age-and gender-matched non-carriers. We examined genotyped healthy subjects with the goal of detecting subtle effects of a gene that confers heightened risk for Alzheimer's disease. We averaged the meshes extracted for each 3D MR data set, and combined the automated segmentations with a radial mapping approach to localize ventricular shape differences in patients. Validation experiments comparing automated and expert manual segmentations showed that (1) the Hausdorff labeling error rapidly decreased, and (2) the power to detect disease-and gene-related alterations improved, as the number of atlases, N, was increased from 1 to 9. In surface-based statistical maps, we detected more widespread and intense anatomical deficits as we increased the number of atlases. We formulated a statistical stopping criterion to determine the optimal number of atlases to use. Healthy ApoE4-carriers and those with AD showed local ventricular abnormalities. This high-throughput method for morphometric studies further motivates the combination of genetic and neuroimaging strategies in predicting AD progression and treatment response.

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Section: Discussionmentioning

confidence: 99%

Automated ventricular mapping with multi-atlas fluid image alignment reveals genetic effects in Alzheimer's disease

Chou¹,

Leporé²,

Zubicaray

et al. 2008

NeuroImage

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“…The shape models represent the variation of the shape of the brain ventricles within two populations: a group of AD patients and a control group of healthy patients. It has been shown [37] that brain ventricles enlarge significantly due to the loss of brain tissue caused by AD. Figure 10 shows a visualization using our technique of the mean shapes of both groups, in which this shape difference is clearly visible.…”

Section: Statistical Shape Modelsmentioning

confidence: 99%

“…We plan to integrate the intersecting surfaces techniques into our software in the future, as well as to extend these visualizations to include feedback on the statistical properties of the model. Given two shape models for different populations, statistically significant differences can be determined between the models themselves [37]. By including such statistical information, our visualizations could help to link such statistical differences to physical differences in shape.…”

Section: Future Workmentioning

confidence: 99%

Image-based rendering of intersecting surfaces for dynamic comparative visualization

et al. 2010

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Nested or intersecting surfaces are proven techniques for visualizing shape differences between static 3D objects (Weigle and Taylor II, IEEE Visualization, Proceedings, pp. 503-510, 2005). In this paper we present an imagebased formulation for these techniques that extends their use to dynamic scenarios, in which surfaces can be manipulated or even deformed interactively. The formulation is based on our new layered rendering pipeline, a generic image-based approach for rendering nested surfaces based on depth peeling and deferred shading.We use layered rendering to enhance the intersecting surfaces visualization. In addition to enabling interactive performance, our enhancements address several limitations of the original technique. Contours remove ambiguity regard- ing the shape of intersections. Local distances between the surfaces can be visualized at any point using either depth fogging or distance fields: Depth fogging is used as a cue for the distance between two surfaces in the viewing direction, whereas closest-point distance measures are visualized interactively by evaluating one surface's distance field on the other surface. Furthermore, we use these measures to define a three-way surface segmentation, which visualizes regions of growth, shrinkage, and no change of a test surface compared with a reference surface.Finally, we demonstrate an application of our technique in the visualization of statistical shape models. We evaluate our technique based on feedback provided by medical image analysis researchers, who are experts in working with such models.

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“…Visual assessment of ventricle volume or shape change has shown to be quite reliable [17][18] in detecting AD. Fourier analysis applied for image features [19], [20], application of wavelets [21], Haralick components [22] are applied to extract features for brain MRI analysis.…”

Section: Introductionmentioning

confidence: 99%

Automated Classification of Dementia Using PSO based Least Square Support Vector Machine

Sivapriya¹,

Kamal²,

Thavavel³

2013

IJMLC

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Abstract-Machine learning techniques are widely used now for neuro-imaging based diagnosis. These methods yield fully automated clinical decisions, unbiased by variable radiological expertise. This research paper compares and evaluates the performance and reliability of conventional Least Square Support Vector Machine (LSSVM) with that of Particle Swarm Optimization (PSO) based LSSVM in the diagnosis of dementia. The manual interpretation of large volume of brain MRI and cognitive measures may lead to incomplete diagnosis. The PSO-LSSVM approach is trained with multiple biomarkers to facilitate effective, accurate classification which is a requirement of the hour. Wavelet based texture features and multiple biomarkers are fed as input to the classifier. PSO-LSSVM yields 98% accurate results and outperforms LSSVM classifier in terms of sensitivity, specificity and accuracy in this analysisIndex Terms-Classification, dementia, least square support vector machine, particle swarm optimization. I. INTRODUCTIONMedical data and neuroimaging has increasingly employed techniques from machine learning and computer aided diagnostics. For instance, a set of training input data is made to yield a desired output by means of a supervised machine learning algorithm that is "trained" for the purpose. Automated classification methods are commonly used for the analysis of neuroimaging studies. Several multi-resolution approaches have been proposed to detect significant changes in the brain volume using neighbourhood information. Various computer-aided techniques have been proposed in the past and include the study of texture changes in signal intensity [1], grey matter (GM) concentration differences [2], atrophy of sub-cortical limbic structures [3]- [5], and general cortical atrophy [6]- [8].Brain image analyses have widely relied on univariate voxel-wise analyses, such as voxel-based morphometry (VBM) for structural MRI [9]. In such analyses, brain images are first spatially registered to a common stereotaxic space, and then mass univariate statistical tests are performed in each voxel to detect significant group differences. However, the sensitivity of these approaches is limited when the differences are spatially complex and involve a combination of different voxels or brain structures [10]. Recently, there has been a growing interest in support vector machines (SVM) Manuscript received December 30, 2012; revised April 11, 2013. T. R. Sivapriya is with the Department of Computer Science, Lady Doak College, Madurai, Tamilnadu, India. (e-mail:spriya.tr@gmail.com Research papers with different approaches for image classification and segmentation are reported in the literature. This study sets out to investigate the reliability and efficiency of PSO based LSSVM techniques in detecting demented and non-demented patients combining MMSE and Clinical Dementia Ratio (CDR) with brain volume. To achieve a high degree of success in treatment, one requirement is to establish proper classification and diagnosis of patients with complex neur...

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Shape differences of the brain ventricles in Alzheimer's disease

Cited by 101 publications

References 32 publications

Automated ventricular mapping with multi-atlas fluid image alignment reveals genetic effects in Alzheimer's disease

Automated ventricular mapping with multi-atlas fluid image alignment reveals genetic effects in Alzheimer's disease

Image-based rendering of intersecting surfaces for dynamic comparative visualization

Automated Classification of Dementia Using PSO based Least Square Support Vector Machine

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