[go: up one dir, main page]
More Web Proxy on the site http://driver.im/

Gemme et al., 2017 - Google Patents

Automatic MPST-cut for segmentation of carpal bones from MR volumes

Gemme et al., 2017

Document ID
7554382742772124497
Author
Gemme L
Nardotto S
Dellepiane S
Publication year
Publication venue
Computers in Biology and Medicine

External Links

Snippet

In the context of rheumatic diseases, several studies suggest that Magnetic Resonance Imaging (MRI) allows the detection of the three main signs of Rheumatoid Arthritis (RA) at higher sensitivities than available through conventional radiology. The rapid, accurate …
Continue reading at www.sciencedirect.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
    • 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
    • G06T2207/30—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
    • G06T2207/20—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
    • G06T2207/30—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
    • G06T2207/20092—Interactive image processing based on input by user
    • G06T2207/20104—Interactive definition of region of interest [ROI]
    • 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
    • G06T2207/20156—Automatic seed setting
    • 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
    • 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
    • G06K9/62—Methods or arrangements for recognition using electronic means
    • G06K9/6267—Classification techniques
    • 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

Similar Documents

Publication Publication Date Title
Ebrahimkhani et al. A review on segmentation of knee articular cartilage: from conventional methods towards deep learning
JP7542578B2 (en) Methods and systems for utilizing quantitative imaging - Patents.com
Ahmad et al. Deep belief network modeling for automatic liver segmentation
Nayak et al. Computer-aided diagnosis of cirrhosis and hepatocellular carcinoma using multi-phase abdomen CT
US10176408B2 (en) Systems and methods for analyzing pathologies utilizing quantitative imaging
Khalifa et al. 3D Kidney Segmentation from Abdominal Images Using Spatial‐Appearance Models
El-Baz et al. Computer‐aided diagnosis systems for lung cancer: challenges and methodologies
Mahapatra Analyzing training information from random forests for improved image segmentation
Carvalho et al. 3D segmentation algorithms for computerized tomographic imaging: a systematic literature review
Peng et al. 3D liver segmentation using multiple region appearances and graph cuts
Asaturyan et al. Morphological and multi-level geometrical descriptor analysis in CT and MRI volumes for automatic pancreas segmentation
US20100266173A1 (en) Computer-aided detection (cad) of a disease
Ananth et al. Liver And Hepatic Tumors Segmentation in 3-D CT Images
Moreno et al. Study of medical image processing techniques applied to lung cancer
Kaur et al. A survey of kidney segmentation techniques in CT images
US7853062B2 (en) System and method for polyp detection in tagged or non-tagged stool images
Gemme et al. Automatic MPST-cut for segmentation of carpal bones from MR volumes
Wieclawek 3D marker-controlled watershed for kidney segmentation in clinical CT exams
Selvan et al. Extracting tree structures in CT data by tracking multiple statistically ranked hypotheses
Munoz et al. 3D-Morphomics, Morphological Features on CT scans for lung nodule malignancy diagnosis
Li et al. 3D Multiple-Contextual ROI-Attention Network for Efficient and Accurate Volumetric Medical Image Segmentation
Babu et al. A review on acute/sub-acute ischemic stroke lesion segmentation and registration challenges
Voiculescu et al. An overview of current evaluation methods used in medical image segmentation
Agnes Boundary Aware Semantic Segmentation using Pyramid-dilated Dense U-Net for Lung Segmentation in Computed Tomography Images
Heryan et al. Automatic extraction of the pelvicalyceal system for preoperative planning of minimally invasive procedures