Results 1 - 10
of
38
ADNI, “Consistent reconstruction of cortical surfaces from longitudinal brain MR images
- Neuroimage
"... Abstract. Accurate and consistent reconstruction of cortical surfaces from longitudinal human brain MR images is of great importance in studying subtle morphological changes of the cerebral cortex. This paper presents a new deformable surface method for consistent and accurate reconstruction of inne ..."
Abstract
-
Cited by 8 (5 self)
- Add to MetaCart
(Show Context)
Abstract. Accurate and consistent reconstruction of cortical surfaces from longitudinal human brain MR images is of great importance in studying subtle morphological changes of the cerebral cortex. This paper presents a new deformable surface method for consistent and accurate reconstruction of inner, central and outer cortical surfaces from longitudinal MR images. Specifically, the cortical surfaces of the group-mean image of all aligned longitudinal images of the same subject are first reconstructed by a deformable surface method driven by a force derived from the Laplace’s equation. And then the longitudinal cortical surfaces are consistently reconstructed by jointly deforming the cortical surfaces from the group-mean image to all longitudinal images. The proposed method has been successfully applied to both simulated and real longitudinal images, demonstrating its validity.
Phenomenological model of diffuse global and regional atrophy using finite-element methods
- Medical Imaging, IEEE Transactions on
, 2006
"... Abstract—The main goal of this work is the generation of ground-truth data for the validation of atrophy measurement techniques, commonly used in the study of neurodegenerative diseases such as dementia. Several techniques have been used to measure atrophy in cross-sectional and longitudinal studies ..."
Abstract
-
Cited by 5 (2 self)
- Add to MetaCart
(Show Context)
Abstract—The main goal of this work is the generation of ground-truth data for the validation of atrophy measurement techniques, commonly used in the study of neurodegenerative diseases such as dementia. Several techniques have been used to measure atrophy in cross-sectional and longitudinal studies, but it is extremely difficult to compare their performance since they have been applied to different patient populations. Furthermore, assessment of performance based on phantom measurements or simple scaled images overestimates these techniques ’ ability to capture the complexity of neurodegeneration of the human brain. We propose a method for atrophy simulation in structural mag-netic resonance (MR) images based on finite-element methods. The method produces cohorts of brain images with known change that is physically and clinically plausible, providing data for objective evaluation of atrophy measurement techniques. Atrophy
Mapping region-specific longitudinal cortical surface expansion from birth to 2 years of age”. Cereb. Cortex
"... The human cerebral cortex develops rapidly and dynamically in the first 2 years of life. It has been shown that cortical surface expansion from term infant to adult is highly nonuniform in a cross-sectional study. However, little is known about the longitudinal cortical surface expansion during earl ..."
Abstract
-
Cited by 3 (2 self)
- Add to MetaCart
(Show Context)
The human cerebral cortex develops rapidly and dynamically in the first 2 years of life. It has been shown that cortical surface expansion from term infant to adult is highly nonuniform in a cross-sectional study. However, little is known about the longitudinal cortical surface expansion during early postnatal stages. In this article, we generate the first longitudinal surface-based atlases of human cortical struc-tures at 0, 1, and 2 years of age from 73 healthy subjects. On the basis of the surface-based atlases, we study the longitudinal cortical surface expansion in the first 2 years of life and find that cortical surface expansion is age related and region specific. In the first year, cortical surface expands dramatically, with an average expansion of 1.80 times. In particular, regions of superior and medial temporal, superior parietal, medial orbitofrontal, lateral anterior prefrontal, occi-pital cortices, and postcentral gyrus expand relatively larger than other regions. In the second year, cortical surface still expands sub-stantially, with an average expansion of 1.20 times. In particular, regions of superior and middle frontal, orbitofrontal, inferior temporal, inferior parietal, and superior parietal cortices expand relatively larger than other regions. These region-specific patterns of cortical surface expansion are related to cognitive and functional development at these stages.
Building Spatiotemporal Anatomical Models using Joint 4-D Segmentation, Registration, and Subject-Specific Atlas Estimation
"... Longitudinal analysis of anatomical changes is a vital component in many personalized-medicine applications for predicting disease onset, determining growth/atrophy patterns, evaluating disease progression, and monitoring recovery. Estimating anatomical changes in longitudinal studies, especially th ..."
Abstract
-
Cited by 3 (2 self)
- Add to MetaCart
(Show Context)
Longitudinal analysis of anatomical changes is a vital component in many personalized-medicine applications for predicting disease onset, determining growth/atrophy patterns, evaluating disease progression, and monitoring recovery. Estimating anatomical changes in longitudinal studies, especially through magnetic resonance (MR) images, is challenging because of temporal variability in shape (e.g. from growth/atrophy) and appearance (e.g. due to imaging parameters and tissue properties affecting intensity contrast, or from scanner calibration). This paper proposes a novel mathematical framework for constructing subject-specific longitudinal anatomical models. The proposed method solves a generalized problem of joint segmentation, registration, and subjectspecific atlas building, which involves not just two images, but an entire longitudinal image sequence. The proposed framework describes a novel approach that integrates fundamental principles that underpin methods for image segmentation, image registration, and atlas construction. This paper presents evaluation on simulated longitudinal data and on clinical longitudinal brain MRI data. The results demonstrate that the proposed framework effectively integrates information from 4-D spatiotemporal data to generate spatiotemporal models that allow analysis of anatomical changes over time. 1.
MR IMAGE CONTRAST SYNTHESIS FOR CONSISTENT SEGMENTATION by
, 2013
"... Magnetic resonance (MR) is a noninvasive imaging modality that has been widely used to image the human brain. Many image processing algorithms, such as segmen-tation and registration, when applied to MR images, provide insights about brain tissues that are used to further the understanding of normal ..."
Abstract
-
Cited by 1 (1 self)
- Add to MetaCart
Magnetic resonance (MR) is a noninvasive imaging modality that has been widely used to image the human brain. Many image processing algorithms, such as segmen-tation and registration, when applied to MR images, provide insights about brain tissues that are used to further the understanding of normal aging, as well as the detection and progression of diseases like Multiple Sclerosis and Alzheimer’s Dis-ease. Brain image segmentation divides brain images into several major tissues, e.g., cerebro-spinal fluid, gray matter and white matter. Most segmentation techniques use image intensities as primary features. However, unlike computed tomography, MR intensities are not calibrated to represent a specific tissue property and they vary widely in both range and distribution, depending on the physical properties of the scanners and the pulse sequences used to image the brain. Image processing results are usually affected by inconsistencies due to these variations. The research presented here primarily focuses on normalizing scans acquired in a variety of scanners and pulse sequences to achieve consistency in their segmentations,
Longitudinal intensity normalization of magnetic resonance images using patches
- in Proceedings of SPIE Medical Imaging (SPIE-MI 2013), Lake Buena Vista, FL
"... This paper presents a patch based method to normalize temporal intensities from longitudinal brain magnetic resonance (MR) images. Longitudinal intensity normalization is relevant for subsequent processing, such as segmentation, so that rates of change of tissue volumes, cortical thickness, or shape ..."
Abstract
-
Cited by 1 (1 self)
- Add to MetaCart
This paper presents a patch based method to normalize temporal intensities from longitudinal brain magnetic resonance (MR) images. Longitudinal intensity normalization is relevant for subsequent processing, such as segmentation, so that rates of change of tissue volumes, cortical thickness, or shapes of brain structures becomes stable and smooth over time. Instead of using intensities at each voxel, we use patches as image features as a patch encodes neighborhood information of the center voxel. Once all the time-points of a longitudinal dataset are registered, the longitudinal intensity change at each patch is assumed to follow an auto-regressive (AR(1)) process. An estimate of the normalized intensities of a patch at every time-point are generated from a hidden Markov model, where the hidden states are the unobserved normalized patches and the outputs are the observed patches. A validation study on a phantom dataset shows good segmentation overlap with the truth, and an experiment with real data shows more stable rates of change for tissue volumes with the temporal normalization than without.
A Survey on Brain Image Segmentation Methods
"... Abstract: For the past decade, many image segmentation techniques have been proposed. These segmentation techniques can be categorized into three classes, (1) characteristic feature thresholding or clustering, (2) edge detection, and (3) region extraction. This survey summarizes some of these techn ..."
Abstract
- Add to MetaCart
Abstract: For the past decade, many image segmentation techniques have been proposed. These segmentation techniques can be categorized into three classes, (1) characteristic feature thresholding or clustering, (2) edge detection, and (3) region extraction. This survey summarizes some of these techniques. In the area of biomedical image segmentation, most proposed techniques fall into the categories of characteristic feature thresholding or clustering and edge detection. We present current segmentation approaches are reviewed with an emphasis placed on revealing the advantages and disadvantages of these methods for medical imaging applications.
unknown title
"... This article appeared in a journal published by Elsevier. The attached copy is furnished to the author for internal non-commercial research and education use, including for instruction at the authors institution and sharing with colleagues. Other uses, including reproduction and distribution, or sel ..."
Abstract
- Add to MetaCart
(Show Context)
This article appeared in a journal published by Elsevier. The attached copy is furnished to the author for internal non-commercial research and education use, including for instruction at the authors institution and sharing with colleagues. Other uses, including reproduction and distribution, or selling or licensing copies, or posting to personal, institutional or third party websites are prohibited. In most cases authors are permitted to post their version of the article (e.g. in Word or Tex form) to their personal website or institutional repository. Authors requiring further information regarding Elsevier’s archiving and manuscript policies are encouraged to visit:
Pattern Recognition
"... This article appeared in a journal published by Elsevier. The attached copy is furnished to the author for internal non-commercial research and education use, including for instruction at the authors institution and sharing with colleagues. Other uses, including reproduction and distribution, or sel ..."
Abstract
- Add to MetaCart
(Show Context)
This article appeared in a journal published by Elsevier. The attached copy is furnished to the author for internal non-commercial research and education use, including for instruction at the authors institution and sharing with colleagues. Other uses, including reproduction and distribution, or selling or licensing copies, or posting to personal, institutional or third party websites are prohibited. In most cases authors are permitted to post their version of the article (e.g. in Word or Tex form) to their personal website or institutional repository. Authors requiring further information regarding Elsevier’s archiving and manuscript policies are encouraged to visit:
