Wang et al., 2015 - Google Patents
Adaptive mesh expansion model (AMEM) for liver segmentation from CT imageWang et al., 2015
View HTML- Document ID
- 12862714622896782443
- Author
- Wang X
- Yang J
- Ai D
- Zheng Y
- Tang S
- Wang Y
- Publication year
- Publication venue
- PLoS one
External Links
Snippet
This study proposes a novel adaptive mesh expansion model (AMEM) for liver segmentation from computed tomography images. The virtual deformable simplex model (DSM) is introduced to represent the mesh, in which the motion of each vertex can be easily …
- 210000004185 Liver 0 title abstract description 93
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
- G06T2207/10—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
- G06T2207/30—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
- G06T7/00—Image analysis
- G06T7/0002—Inspection of images, e.g. flaw detection
- G06T7/0012—Biomedical image inspection
-
- 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
- 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
- G06T2207/20—Special algorithmic details
- G06T2207/20076—Probabilistic image processing
-
- 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
- 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
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06K—RECOGNITION OF DATA; PRESENTATION OF DATA; RECORD CARRIERS; HANDLING RECORD CARRIERS
- G06K9/00—Methods or arrangements for reading or recognising printed or written characters or for recognising patterns, e.g. fingerprints
- G06K9/36—Image preprocessing, i.e. processing the image information without deciding about the identity of the image
- G06K9/46—Extraction of features or characteristics of the image
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T11/00—2D [Two Dimensional] image generation
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06K—RECOGNITION OF DATA; PRESENTATION OF DATA; RECORD CARRIERS; HANDLING RECORD CARRIERS
- G06K2209/00—Indexing scheme relating to methods or arrangements for reading or recognising printed or written characters or for recognising patterns, e.g. fingerprints
- G06K2209/05—Recognition of patterns in medical or anatomical images
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T17/00—Three dimensional [3D] modelling, e.g. data description of 3D objects
-
- 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
- G06F—ELECTRICAL DIGITAL DATA PROCESSING
Similar Documents
Publication | Publication Date | Title |
---|---|---|
Lu et al. | Fully automatic liver segmentation combining multi-dimensional graph cut with shape information in 3D CT images | |
Wang et al. | Shape–intensity prior level set combining probabilistic atlas and probability map constrains for automatic liver segmentation from abdominal CT images | |
Vivanti et al. | Patient-specific and global convolutional neural networks for robust automatic liver tumor delineation in follow-up CT studies | |
Wolz et al. | Automated abdominal multi-organ segmentation with subject-specific atlas generation | |
Cao et al. | Cascaded SE-ResUnet for segmentation of thoracic organs at risk | |
Wang et al. | Adaptive mesh expansion model (AMEM) for liver segmentation from CT image | |
Erdt et al. | Regmentation: A new view of image segmentation and registration | |
US9218542B2 (en) | Localization of anatomical structures using learning-based regression and efficient searching or deformation strategy | |
Moghbel et al. | Automatic liver tumor segmentation on computed tomography for patient treatment planning and monitoring | |
Pu et al. | Shape “break-and-repair” strategy and its application to automated medical image segmentation | |
US10405834B2 (en) | Surface modeling of a segmented echogenic structure for detection and measurement of anatomical anomalies | |
Moghbel et al. | Automatic liver segmentation on computed tomography using random walkers for treatment planning | |
Göçeri | Fully automated liver segmentation using Sobolev gradient‐based level set evolution | |
Dong et al. | Segmentation of liver and spleen based on computational anatomy models | |
Zhang et al. | A segmentation method for lung nodule image sequences based on superpixels and density-based spatial clustering of applications with noise | |
Egger | PCG-cut: graph driven segmentation of the prostate central gland | |
Wang et al. | Automatic Approach for Lung Segmentation with Juxta‐Pleural Nodules from Thoracic CT Based on Contour Tracing and Correction | |
Cha et al. | Segmentation and tracking of lung nodules via graph‐cuts incorporating shape prior and motion from 4D CT | |
Huynh et al. | Fully automated MR liver volumetry using watershed segmentation coupled with active contouring | |
Yan et al. | Atlas-based liver segmentation and hepatic fat-fraction assessment for clinical trials | |
Qiu et al. | Rotationally resliced 3D prostate TRUS segmentation using convex optimization with shape priors | |
Zheng et al. | Feature learning based random walk for liver segmentation | |
Wang et al. | Liver segmentation from CT images using a sparse priori statistical shape model (SP-SSM) | |
Hao et al. | Juxta‐vascular pulmonary nodule segmentation in PET‐CT imaging based on an LBF active contour model with information entropy and joint vector | |
Ciecholewski | Automatic liver segmentation from 2D CT images using an approximate contour model |