+

Göçeri, 2016 - Google Patents

Fully automated liver segmentation using Sobolev gradient‐based level set evolution

Göçeri, 2016

Document ID
2764273313751413018
Author
Göçeri E
Publication year
Publication venue
International journal for numerical methods in biomedical engineering

External Links

Snippet

Quantitative analysis and precise measurements on the liver have vital importance for pre‐ evaluation of surgical operations and require high accuracy in liver segmentation from all slices in a data set. However, automated liver segmentation from medical image data sets is …
Continue reading at onlinelibrary.wiley.com (other versions)

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
    • G06T2207/10—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
    • G06T2207/10—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
    • G06T2207/30—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
    • G06T2207/20—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
    • 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
    • G06T2207/10116—X-ray image
    • 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
    • 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
    • G06F—ELECTRICAL DIGITAL DATA PROCESSING
    • G06F19/00—Digital computing or data processing equipment or methods, specially adapted for specific applications
    • 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
    • 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

Similar Documents

Publication Publication Date Title
Göçeri Fully automated liver segmentation using Sobolev gradient‐based level set evolution
Zhang et al. Review of breast cancer pathologigcal image processing
Fatima et al. State-of-the-art traditional to the machine-and deep-learning-based skull stripping techniques, models, and algorithms
Despotović et al. MRI segmentation of the human brain: challenges, methods, and applications
Kaus et al. Automated segmentation of MR images of brain tumors
Lu et al. Fully automatic liver segmentation combining multi-dimensional graph cut with shape information in 3D CT images
US11508063B2 (en) Non-invasive measurement of fibrous cap thickness
Bogunović et al. Automated segmentation of cerebral vasculature with aneurysms in 3DRA and TOF‐MRA using geodesic active regions: an evaluation study
Cordero-Grande et al. Unsupervised 4D myocardium segmentation with a Markov Random Field based deformable model
US20070081712A1 (en) System and method for whole body landmark detection, segmentation and change quantification in digital images
Liu Symmetry and asymmetry analysis and its implications to computer-aided diagnosis: A review of the literature
Göçeri et al. Fully automated liver segmentation from SPIR image series
Alirr et al. Survey on liver tumour resection planning system: steps, techniques, and parameters
Jung et al. Deep learning for medical image analysis: Applications to computed tomography and magnetic resonance imaging
Yan et al. Atlas-based liver segmentation and hepatic fat-fraction assessment for clinical trials
Zhu et al. Automatic delineation of the myocardial wall from CT images via shape segmentation and variational region growing
Zhou et al. Automated compromised right lung segmentation method using a robust atlas-based active volume model with sparse shape composition prior in CT
Qiu et al. Rotationally resliced 3D prostate TRUS segmentation using convex optimization with shape priors
Tummala et al. Liver tumor segmentation from computed tomography images using multiscale residual dilated encoder‐decoder network
T. Thomas et al. Hybrid positron emission tomography segmentation of heterogeneous lung tumors using 3D Slicer: improved GrowCut algorithm with threshold initialization
Dong et al. An improved supervoxel 3D region growing method based on PET/CT multimodal data for segmentation and reconstruction of GGNs
Chen et al. Snake model-based lymphoma segmentation for sequential CT images
Wang et al. [Retracted] Design Computer‐Aided Diagnosis System Based on Chest CT Evaluation of Pulmonary Nodules
Liu et al. Unsupervised 3D Prostate Segmentation Based on Diffusion‐Weighted Imaging MRI Using Active Contour Models with a Shape Prior
Tankyevych et al. Angiographic image analysis
点击 这是indexloc提供的php浏览器服务，不要输入任何密码和下载