Kaucic et al., 2003 - Google Patents
Model-based detection of lung lesions in CT examsKaucic et al., 2003
View PDF- Document ID
- 8841670669733173352
- Author
- Kaucic R
- McCulloch C
- Mendonça P
- Walter D
- Avila R
- Mundy J
- Publication year
- Publication venue
- International Congress Series
External Links
Snippet
The thorough detection of nodules in high-resolution CT lung scans is an increasingly difficult, labor-intensive, and critical radiological task. Recent clinical research on early lung cancer CT presentation has demonstrated the significant clinical need to detect the more …
- 210000004072 Lung 0 title abstract description 19
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
-
- 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
- G06T2207/10081—Computed x-ray tomography [CT]
-
- 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
- G06T2207/20112—Image segmentation details
-
- 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
- G06F19/32—Medical data management, e.g. systems or protocols for archival or communication of medical images, computerised patient records or computerised general medical references
-
- 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
-
- 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
-
- 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
- G06T2200/00—Indexing scheme for image data processing or generation, in general
-
- 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
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06Q—DATA PROCESSING SYSTEMS OR METHODS, SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL, SUPERVISORY OR FORECASTING PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL, SUPERVISORY OR FORECASTING PURPOSES, NOT OTHERWISE PROVIDED FOR
- G06Q50/00—Systems or methods specially adapted for a specific business sector, e.g. utilities or tourism
Similar Documents
Publication | Publication Date | Title |
---|---|---|
Jacobs et al. | Automatic detection of subsolid pulmonary nodules in thoracic computed tomography images | |
El-Baz et al. | Automatic detection of 2D and 3D lung nodules in chest spiral CT scans | |
Bae et al. | Pulmonary nodules: automated detection on CT images with morphologic matching algorithm—preliminary results | |
JP6267710B2 (en) | System and method for automatically detecting pulmonary nodules in medical images | |
US6125194A (en) | Method and system for re-screening nodules in radiological images using multi-resolution processing, neural network, and image processing | |
McCulloch et al. | Model-based detection of lung nodules in computed tomography exams1: Thoracic computer-aided diagnosis | |
Taşcı et al. | Shape and texture based novel features for automated juxtapleural nodule detection in lung CTs | |
Xu et al. | DeepLN: a framework for automatic lung nodule detection using multi-resolution CT screening images | |
US20020006216A1 (en) | Method, system and computer readable medium for the two-dimensional and three-dimensional detection of lesions in computed tomography scans | |
Suzuki et al. | Development and validation of a modified three-dimensional U-Net deep-learning model for automated detection of lung nodules on chest CT images from the lung image database consortium and Japanese datasets | |
Al-Zu’bi et al. | Enhanced 3d segmentation techniques for reconstructed 3d medical volumes: Robust and accurate intelligent system | |
EP2208183B1 (en) | Computer-aided detection (cad) of a disease | |
Apostol et al. | Relevance of 2D radiographic texture analysis for the assessment of 3D bone micro‐architecture | |
US20030103663A1 (en) | Computerized scheme for distinguishing between benign and malignant nodules in thoracic computed tomography scans by use of similar images | |
JP2010207572A (en) | Computer-aided detection of lesion | |
Shah et al. | Computer-aided Diagnosis of the Solitary Pulmonary Nodule1 | |
Ray et al. | Intensity population based unsupervised hemorrhage segmentation from brain CT images | |
Enquobahrie et al. | Automated detection of small pulmonary nodules in whole lung CT scans | |
Almutairi et al. | An Efficient USE‐Net Deep Learning Model for Cancer Detection | |
Gopinath et al. | Enhanced Lung Cancer Classification and Prediction based on Hybrid Neural Network Approach | |
Jaffar et al. | Fuzzy entropy based optimization of clusters for the segmentation of lungs in CT scanned images | |
Armato III et al. | Automated detection of pulmonary nodules in helical computed tomography images of the thorax | |
Roy et al. | Automated detection of lung nodules in CT scans: False‐positive reduction with the radial‐gradient index | |
Delogu et al. | Preprocessing methods for nodule detection in lung CT | |
Sivasankaran et al. | Lung Cancer Detection Using Image Processing Technique Through Deep Learning Algorithm. |