Orkisz et al., 2008 - Google Patents
Models, algorithms and applications in vascular image segmentationOrkisz et al., 2008
View PDF- Document ID
- 15602163465524737883
- Author
- Orkisz M
- Flórez Valencia L
- Hernández Hoyos M
- Publication year
- Publication venue
- Machine Graphics and Vision
External Links
Snippet
A synthesis of the authors' projects in the last decade, in the field of 3D vascular image processing, is provided. This work was motivated by the following applications: display improvement, extraction of geometrical measurements, acquisition optimization, stent-pose …
- 230000002792 vascular 0 title abstract description 65
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
- G06T2207/30048—Heart; Cardiac
-
- 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
- G06T2207/30101—Blood vessel; Artery; Vein; Vascular
-
- 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
- G06T2207/20—Special algorithmic details
- G06T2207/20092—Interactive image processing based on input by user
- G06T2207/20101—Interactive definition of point of interest, landmark or seed
-
- 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
- G06T2207/10081—Computed x-ray tomography [CT]
-
- 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
- G06T2207/20—Special algorithmic details
- G06T2207/20112—Image segmentation details
- G06T2207/20116—Active contour; Active surface; Snakes
-
- 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
- G06T2207/30172—Centreline of tubular or elongated structure
-
- 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
- G06T2207/10116—X-ray image
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T2200/00—Indexing scheme for image data processing or generation, in general
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T5/00—Image enhancement or restoration, e.g. from bit-mapped to bit-mapped creating a similar image
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T3/00—Geometric image transformation in the plane of the image, e.g. from bit-mapped to bit-mapped creating a different image
- G06T3/0031—Geometric image transformation in the plane of the image, e.g. from bit-mapped to bit-mapped creating a different image for topological mapping of a higher dimensional structure on a lower dimensional surface
- G06T3/0037—Reshaping or unfolding a 3D tree structure onto a 2D plane
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| Nain et al. | Vessel segmentation using a shape driven flow | |
| El-Baz et al. | Precise segmentation of 3-D magnetic resonance angiography | |
| Frangi et al. | Model-based quantitation of 3-D magnetic resonance angiographic images | |
| EP1851722B1 (en) | Image processing device and method | |
| Hennemuth et al. | A comprehensive approach to the analysis of contrast enhanced cardiac MR images | |
| Saha et al. | Topomorphologic separation of fused isointensity objects via multiscale opening: Separating arteries and veins in 3-D pulmonary CT | |
| US20070249912A1 (en) | Method for artery-vein image separation in blood pool contrast agents | |
| Scherl et al. | Semi-automatic level-set based segmentation and stenosis quantification of the internal carotid artery in 3D CTA data sets | |
| Göçeri et al. | Fully automated liver segmentation from SPIR image series | |
| Hernández-Hoyos et al. | A deformable vessel model with single point initialization for segmentation, quantification, and visualization of blood vessels in 3D MRA | |
| Suri et al. | Angiography and plaque imaging: advanced segmentation techniques | |
| Law et al. | Segmentation of intracranial vessels and aneurysms in phase contrast magnetic resonance angiography using multirange filters and local variances | |
| Boldak et al. | An improved model-based vessel tracking algorithm with application to computed tomography angiography | |
| Radaelli et al. | On the segmentation of vascular geometries from medical images | |
| Orkisz et al. | Models, algorithms and applications in vascular image segmentation | |
| Hoyos et al. | Assessment of carotid artery stenoses in 3D contrast-enhanced magnetic resonance angiography, based on improved generation of the centerline | |
| Czajkowska et al. | Skeleton graph matching vs. maximum weight cliques aorta registration techniques | |
| Subašić et al. | Model-based quantitative AAA image analysis using a priori knowledge | |
| O’Donnell et al. | Comprehensive cardiovascular image analysis using MR and CT at Siemens Corporate Research | |
| Bergen et al. | 4D MR phase and magnitude segmentations with GPU parallel computing | |
| Giachetti et al. | AQUATICS reconstruction software: the design of a diagnostic tool based on computer vision algorithms | |
| Macedo et al. | Vessel centerline tracking in CTA and MRA images using hough transform | |
| Li et al. | Accurate curvilinear modelling for precise measurements of tubular structures | |
| Hassan et al. | A hybrid approach for vessel enhancement and fast level set segmenatation based 3d blood vessel extraction using MR brain image | |
| Haque et al. | Automated registration of 3d ultrasound and ct/mr images for liver |