[go: up one dir, main page]

Boehm et al., 2008 - Google Patents

Automated classification of normal and pathologic pulmonary tissue by topological texture features extracted from multi-detector CT in 3D

Boehm et al., 2008

View PDF
Document ID
11582252974211547639
Author
Boehm H
Fink C
Attenberger U
Becker C
Behr J
Reiser M
Publication year
Publication venue
European radiology

External Links

Snippet

To provide a novel, robust algorithm for classification of lung tissue depicted by multi- detector computed tomography (MDCT) based on the topology of CT-attenuation values and to compare discriminative results with densitometric methods. Two hundred seventy-five …
Continue reading at www.researchgate.net (PDF) (other versions)

Classifications

    • 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
    • G06T2207/30008—Bone
    • 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
    • G06T2207/30061—Lung
    • 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
    • G06T2207/10088—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
    • G06T2207/10116—X-ray 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
    • G06F19/30—Medical informatics, i.e. computer-based analysis or dissemination of patient or disease data
    • G06F19/34—Computer-assisted medical diagnosis or treatment, e.g. computerised prescription or delivery of medication or diets, computerised local control of medical devices, medical expert systems or telemedicine
    • G06F19/345—Medical expert systems, neural networks or other automated diagnosis
    • 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
    • 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
    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T11/00—2D [Two Dimensional] image generation

Similar Documents

Publication Publication Date Title
JP4469594B2 (en) System for measuring disease-related tissue changes
Boehm et al. Automated classification of normal and pathologic pulmonary tissue by topological texture features extracted from multi-detector CT in 3D
Maldonado et al. Automated quantification of radiological patterns predicts survival in idiopathic pulmonary fibrosis
Xu et al. Computer-aided classification of interstitial lung diseases via MDCT: 3D adaptive multiple feature method (3D AMFM)
US7236619B2 (en) System and method for computer-aided detection and characterization of diffuse lung disease
Le et al. Assessing robustness of carotid artery CT angiography radiomics in the identification of culprit lesions in cerebrovascular events
Oei et al. Review of radiological scoring methods of osteoporotic vertebral fractures for clinical and research settings
WO2017150497A1 (en) Diagnosis support system for lesion in lung field, and method and program for controlling same
US20230154620A1 (en) Apparatus and method for assisting reading of chest medical images
JP5331299B2 (en) Method and system for performing patient-specific analysis of disease-related changes for diseases within an anatomical structure
CN113469934A (en) Assessment of abnormal regions associated with disease from chest CT images
Chen et al. Automatic segmentation and radiomic texture analysis for osteoporosis screening using chest low-dose computed tomography
Huber et al. Classification of interstitial lung disease patterns with topological texture features
Zhu et al. Automatic segmentation of ground-glass opacities in lung CT images by using Markov random field-based algorithms
Yoshiyasu et al. Radiomics technology for identifying early-stage lung adenocarcinomas suitable for sublobar resection
Jin et al. Automatic coronary plaque detection, classification, and stenosis grading using deep learning and radiomics on computed tomography angiography images: a multi-center multi-vendor study
CN109937433B (en) Device for detecting opaque regions in an X-ray image
US20030103663A1 (en) Computerized scheme for distinguishing between benign and malignant nodules in thoracic computed tomography scans by use of similar images
US20240370997A1 (en) Systems and methods for detecting and characterizing covid-19
Alpert et al. Lepidic predominant pulmonary lesions (LPL): CT-based distinction from more invasive adenocarcinomas using 3D volumetric density and first-order CT texture analysis
Owrangi et al. Computed tomography density histogram analysis to evaluate pulmonary emphysema in ex-smokers
Hosseini et al. Detection and severity scoring of chronic obstructive pulmonary disease using volumetric analysis of lung CT images
Yanagawa et al. Commercially available computer-aided detection system for pulmonary nodules on thin-section images using 64 detectors-row CT: preliminary study of 48 cases
Sun et al. Deep learning-based solid component measuring enabled interpretable prediction of tumor invasiveness for lung adenocarcinoma
Boehm et al. Differentiation between post-menopausal women with and without hip fractures: enhanced evaluation of clinical DXA by topological analysis of the mineral distribution in the scan images