Eskildsen et al., 2012 - Google Patents
BEaST: brain extraction based on nonlocal segmentation techniqueEskildsen et al., 2012
View PDF- Document ID
- 13996185338294826898
- Author
- Eskildsen S
- Coupé P
- Fonov V
- Manjón J
- Leung K
- Guizard N
- Wassef S
- Østergaard L
- Collins D
- Alzheimer's Disease Neuroimaging Initiative
- et al.
- Publication year
- Publication venue
- NeuroImage
External Links
Snippet
Brain extraction is an important step in the analysis of brain images. The variability in brain morphology and the difference in intensity characteristics due to imaging sequences make the development of a general purpose brain extraction algorithm challenging. To address …
- 210000004556 Brain 0 title abstract description 127
Classifications
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T2207/00—Indexing scheme for image analysis or image enhancement
- G06T2207/30—Subject of image; Context of image processing
- G06T2207/30004—Biomedical image processing
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T7/00—Image analysis
- G06T7/0002—Inspection of images, e.g. flaw detection
- G06T7/0012—Biomedical image inspection
- G06T7/0014—Biomedical image inspection using an image reference approach
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T2207/00—Indexing scheme for image analysis or image enhancement
- G06T2207/10—Image acquisition modality
- G06T2207/10072—Tomographic images
- G06T2207/10088—Magnetic resonance imaging [MRI]
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T2207/00—Indexing scheme for image analysis or image enhancement
- G06T2207/10—Image acquisition modality
- G06T2207/10072—Tomographic images
- G06T2207/10084—Hybrid tomography; Concurrent acquisition with multiple different tomographic modalities
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T2207/00—Indexing scheme for image analysis or image enhancement
- G06T2207/10—Image acquisition modality
- G06T2207/10072—Tomographic images
- G06T2207/10104—Positron emission tomography [PET]
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T2207/00—Indexing scheme for image analysis or image enhancement
- G06T2207/20—Special algorithmic details
- G06T2207/20112—Image segmentation details
-
- G—PHYSICS
- G01—MEASURING; TESTING
- G01R—MEASURING ELECTRIC VARIABLES; MEASURING MAGNETIC VARIABLES
- G01R33/00—Arrangements or instruments for measuring magnetic variables
- G01R33/20—Arrangements or instruments for measuring magnetic variables involving magnetic resonance
- G01R33/44—Arrangements or instruments for measuring magnetic variables involving magnetic resonance using nuclear magnetic resonance [NMR]
- G01R33/48—NMR imaging systems
- G01R33/54—Signal processing systems, e.g. using pulse sequences, Generation or control of pulse sequences ; Operator Console
- G01R33/56—Image enhancement or correction, e.g. subtraction or averaging techniques, e.g. improvement of signal-to-noise ratio and resolution
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06F—ELECTRICAL DIGITAL DATA PROCESSING
- G06F19/00—Digital computing or data processing equipment or methods, specially adapted for specific applications
- G06F19/30—Medical informatics, i.e. computer-based analysis or dissemination of patient or disease data
-
- A—HUMAN NECESSITIES
- A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
- A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
- A61B5/00—Detecting, measuring or recording for diagnostic purposes; Identification of persons
- A61B5/05—Detecting, measuring or recording for diagnosis by means of electric currents or magnetic fields; Measuring using microwaves or radiowaves
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| Eskildsen et al. | BEaST: brain extraction based on nonlocal segmentation technique | |
| Forkel et al. | Lesion mapping in acute stroke aphasia and its implications for recovery | |
| Iglesias et al. | Bayesian segmentation of brainstem structures in MRI | |
| Yushkevich et al. | Automated volumetry and regional thickness analysis of hippocampal subfields and medial temporal cortical structures in mild cognitive impairment | |
| Leung et al. | Automated cross-sectional and longitudinal hippocampal volume measurement in mild cognitive impairment and Alzheimer's disease | |
| Struyfs et al. | Automated MRI volumetry as a diagnostic tool for Alzheimer's disease: Validation of icobrain dm | |
| Eskildsen et al. | Structural imaging biomarkers of Alzheimer's disease: predicting disease progression | |
| Heckemann et al. | Improving intersubject image registration using tissue-class information benefits robustness and accuracy of multi-atlas based anatomical segmentation | |
| Coupé et al. | Scoring by nonlocal image patch estimator for early detection of Alzheimer's disease | |
| Lötjönen et al. | Fast and robust extraction of hippocampus from MR images for diagnostics of Alzheimer's disease | |
| US9940712B2 (en) | Quantitating disease progression from the MRI images of multiple sclerosis patients | |
| Hutton et al. | Voxel-based cortical thickness measurements in MRI | |
| Verma et al. | Multiparametric tissue characterization of brain neoplasms and their recurrence using pattern classification of MR images | |
| Nestor et al. | A direct morphometric comparison of five labeling protocols for multi-atlas driven automatic segmentation of the hippocampus in Alzheimer's disease | |
| Xiao et al. | Multi-contrast unbiased MRI atlas of a Parkinson’s disease population | |
| US8838201B2 (en) | Atlas-based analysis for image-based anatomic and functional data of organism | |
| Himmelberg et al. | Comparing retinotopic maps of children and adults reveals a late-stage change in how V1 samples the visual field | |
| Fennema-Notestine et al. | Feasibility of multi-site clinical structural neuroimaging studies of aging using legacy data | |
| Pannek et al. | The average pathlength map: a diffusion MRI tractography-derived index for studying brain pathology | |
| Lindemer et al. | White Matter Signal Abnormality Quality Differentiates MCI that Converts to Alzheimer's Disease from Non-converters | |
| US8634614B2 (en) | System and method for volumetric analysis of medical images | |
| Weier et al. | Rapid automatic segmentation of the human cerebellum and its lobules (RASCAL)—Implementation and application of the patch‐based label‐fusion technique with a template library to segment the human cerebellum | |
| Li et al. | Mapping fetal brain development based on automated segmentation and 4D brain atlasing | |
| Huang et al. | Brain extraction based on locally linear representation-based classification | |
| US20050244036A1 (en) | Method and apparatus for evaluating regional changes in three-dimensional tomographic images |