Xia et al., 2003 - Google Patents
A novel methodology for extracting colon's lumen from colonoscopic imagesXia et al., 2003
View PDF- Document ID
- 11084976039613470255
- Author
- Xia S
- Krishnan S
- Tjoa M
- Goh P
- Publication year
- Publication venue
- Journal of Systemics, Cybernetics and Informatics
External Links
Snippet
Recently, computer assisted diagnosis on colonoscopic images is getting more and more attention by many researchers in the world, while the colon's lumen is the most important feature during the process. In this paper, a novel methodology for extracting colon's lumen …
- 238000000034 method 0 title abstract description 59
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
- G06T2207/20—Special algorithmic details
- G06T2207/20112—Image segmentation details
-
- 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
- G06K9/4604—Detecting partial patterns, e.g. edges or contours, or configurations, e.g. loops, corners, strokes, intersections
- G06K9/4609—Detecting partial patterns, e.g. edges or contours, or configurations, e.g. loops, corners, strokes, intersections by matching or filtering
-
- 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
- G06T2207/10024—Color 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
- G06T2207/10—Image acquisition modality
- G06T2207/10072—Tomographic images
-
- 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
- G06K9/6201—Matching; Proximity measures
- G06K9/6202—Comparing pixel values or logical combinations thereof, or feature values having positional relevance, e.g. template matching
-
- 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
- G06T7/00—Image analysis
- G06T7/40—Analysis of texture
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T5/00—Image enhancement or restoration, e.g. from bit-mapped to bit-mapped creating a similar image
- G06T5/007—Dynamic range modification
- G06T5/008—Local, e.g. shadow enhancement
-
- 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 |
---|---|---|
Lou et al. | DC-UNet: rethinking the U-Net architecture with dual channel efficient CNN for medical image segmentation | |
Bernal et al. | Towards automatic polyp detection with a polyp appearance model | |
Rodrigues et al. | Segmentation of optic disc and blood vessels in retinal images using wavelets, mathematical morphology and Hessian-based multi-scale filtering | |
Guo et al. | Giana polyp segmentation with fully convolutional dilation neural networks | |
Nguyen et al. | Robust boundary segmentation in medical images using a consecutive deep encoder-decoder network | |
RU2654199C1 (en) | Segmentation of human tissues in computer image | |
Nguyen et al. | Colorectal segmentation using multiple encoder-decoder network in colonoscopy images | |
Xia et al. | A multi-scale segmentation-to-classification network for tiny microaneurysm detection in fundus images | |
Ramakanth et al. | Approximate nearest neighbour field based optic disk detection | |
Häfner et al. | Delaunay triangulation-based pit density estimation for the classification of polyps in high-magnification chromo-colonoscopy | |
KR102176139B1 (en) | Apparatus and method for segmenting images using consecutive deep encoder-decoder network | |
US11783488B2 (en) | Method and device of extracting label in medical image | |
Yao et al. | Advances on pancreas segmentation: a review | |
Zhang et al. | TUnet-LBF: Retinal fundus image fine segmentation model based on transformer Unet network and LBF | |
Liu et al. | Extracting lungs from CT images via deep convolutional neural network based segmentation and two-pass contour refinement | |
Kaushik et al. | Medical image segmentation using genetic algorithm | |
Phan et al. | Improving liver lesions classification on CT/MRI images based on Hounsfield Units attenuation and deep learning | |
Wieclawek et al. | Watershed based intelligent scissors | |
Xia et al. | A novel methodology for extracting colon’s lumen from colonoscopic images | |
Moradi et al. | Multi-class segmentation of skin lesions via joint dictionary learning | |
CN108765399B (en) | Lesion site recognition device, computer device, and readable storage medium | |
Mazlin et al. | Partitioning intensity inhomogeneity colour images via Saliency-based active contour | |
Liang et al. | Recognizing focal liver lesions in contrast-enhanced ultrasound with discriminatively trained spatio-temporal model | |
Aksenov et al. | An ensemble of convolutional neural networks for the use in video endoscopy | |
Häfner et al. | Endoscopic image classification using edge-based features |