Sprindzuk et al., 2010 - Google Patents
Lung cancer differential diagnosis based on the computer assisted radiology: The state of the artSprindzuk et al., 2010
View HTML- Document ID
- 6680422875055860773
- Author
- Sprindzuk M
- Kovalev V
- Snezhko E
- Kharuzhyk S
- Publication year
- Publication venue
- Polish journal of radiology
External Links
Snippet
The concepts of the modern computer-aided diagnosis (CAD), the methods of pulmonary nodules detection and facts derived from the available literature on the pulmonary nodule differential CAD topic are compiled in one source and described in some details. Several …
- 206010058467 Lung neoplasm malignant 0 title abstract description 57
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
- 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
- G06T2207/10072—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
- G06T2207/10084—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
- G06T2207/10—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
- G06T2207/10072—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
- G06T2207/10116—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
- 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
- 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
-
- A—HUMAN NECESSITIES
- A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
- A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
- A61B5/00—Detecting, measuring or recording for diagnostic purposes; Identification of persons
- A61B5/05—Detecting, measuring or recording for diagnosis by means of electric currents or magnetic fields; Measuring using microwaves or radiowaves
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| Ganeshan et al. | Texture analysis in non-contrast enhanced CT: impact of malignancy on texture in apparently disease-free areas of the liver | |
| Wiemker et al. | Aspects of computer-aided detection (CAD) and volumetry of pulmonary nodules using multislice CT | |
| Giger et al. | Computer-aided diagnosis | |
| Summers et al. | Colonic polyps: complementary role of computer-aided detection in CT colonography | |
| Bağcı et al. | Computer-assisted detection of infectious lung diseases: a review | |
| Ge et al. | Computer‐aided detection of lung nodules: false positive reduction using a 3D gradient field method and 3D ellipsoid fitting | |
| Reeves et al. | Computer-aided diagnosis for lung cancer | |
| Xu et al. | Application of radiomics in predicting the malignancy of pulmonary nodules in different sizes | |
| US20150356730A1 (en) | Quantitative predictors of tumor severity | |
| US20040252870A1 (en) | System and method for three-dimensional image rendering and analysis | |
| JPWO2017150497A1 (en) | Diagnosis support apparatus for lung field lesion, control method and program for the apparatus | |
| Wei et al. | Computerized detection of noncalcified plaques in coronary CT angiography: evaluation of topological soft gradient prescreening method and luminal analysis | |
| Ko et al. | Lung nodule detection and characterization with multislice CT | |
| Li et al. | Application analysis of ai technology combined with spiral CT scanning in early lung cancer screening | |
| Lee et al. | Potential of computer-aided diagnosis to improve CT lung cancer screening | |
| Wan et al. | Spatio‐temporal texture (SpTeT) for distinguishing vulnerable from stable atherosclerotic plaque on dynamic contrast enhancement (DCE) MRI in a rabbit model | |
| Rezaie et al. | Detection of lung nodules on medical images by the use of fractal segmentation | |
| Shi et al. | Computed tomography enterography radiomics and machine learning for identification of Crohn’s disease | |
| Buongiorno et al. | UIP-Net: A decoder-encoder CNN for the detection and quantification of usual interstitial pneumoniae pattern in lung CT scan images | |
| Suzuki | Pixel-based machine learning in computer-aided diagnosis of lung and colon cancer | |
| Sprindzuk et al. | Lung cancer differential diagnosis based on the computer assisted radiology: The state of the art | |
| Delogu et al. | Preprocessing methods for nodule detection in lung CT | |
| Wiemker et al. | Computer-aided detection (CAD) and volumetry of pulmonary nodules on high-resolution CT data | |
| Moriakov et al. | Improving lesion volume measurements on digital mammograms | |
| Stember et al. | The self-overlap method for assessment of lung nodule morphology in chest CT |