Hammon et al., 2013 - Google Patents
Model-based pancreas segmentation in portal venous phase contrast-enhanced CT imagesHammon et al., 2013
View HTML- Document ID
- 2961767229445912139
- Author
- Hammon M
- Cavallaro A
- Erdt M
- Dankerl P
- Kirschner M
- Drechsler K
- Wesarg S
- Uder M
- Janka R
- Publication year
- Publication venue
- Journal of digital imaging
External Links
Snippet
This study aims to automatically detect and segment the pancreas in portal venous phase contrast-enhanced computed tomography (CT) images. The institutional review board of the University of Erlangen-Nuremberg approved this study and waived the need for informed …
- 210000000496 Pancreas 0 title abstract description 76
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
- 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
- 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
- G06T2207/10088—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
- G06T2207/20—Special algorithmic details
- G06T2207/20112—Image segmentation details
-
- 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
- G06T2207/20076—Probabilistic image processing
-
- 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
- G06T2207/20—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
- 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
-
- 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
- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T11/00—2D [Two Dimensional] image generation
-
- 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
- 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
- G06F—ELECTRICAL DIGITAL DATA PROCESSING
Similar Documents
Publication | Publication Date | Title |
---|---|---|
Haque et al. | Deep learning approaches to biomedical image segmentation | |
Aljabri et al. | Towards a better understanding of annotation tools for medical imaging: a survey | |
Rebouças Filho et al. | Novel and powerful 3D adaptive crisp active contour method applied in the segmentation of CT lung images | |
Kalaiselvi et al. | Survey of using GPU CUDA programming model in medical image analysis | |
Hammon et al. | Model-based pancreas segmentation in portal venous phase contrast-enhanced CT images | |
EP3444781B1 (en) | Image processing apparatus and image processing method | |
Egger | PCG-cut: graph driven segmentation of the prostate central gland | |
Erdt et al. | Automatic pancreas segmentation in contrast enhanced CT data using learned spatial anatomy and texture descriptors | |
Rayed et al. | Deep learning for medical image segmentation: State-of-the-art advancements and challenges | |
Asaturyan et al. | Morphological and multi-level geometrical descriptor analysis in CT and MRI volumes for automatic pancreas segmentation | |
Mughal et al. | Adaptive hysteresis thresholding segmentation technique for localizing the breast masses in the curve stitching domain | |
Maity et al. | Automatic lung parenchyma segmentation using a deep convolutional neural network from chest X-rays | |
Alirr et al. | An automated liver tumour segmentation from abdominal CT scans for hepatic surgical planning | |
Tummala et al. | Liver tumor segmentation from computed tomography images using multiscale residual dilated encoder‐decoder network | |
Kéchichian et al. | Automatic 3D multiorgan segmentation via clustering and graph cut using spatial relations and hierarchically-registered atlases | |
Dorgham et al. | U-NetCTS: U-Net deep neural network for fully automatic segmentation of 3D CT DICOM volume | |
Wang et al. | Automated delineation of nasopharynx gross tumor volume for nasopharyngeal carcinoma by plain CT combining contrast-enhanced CT using deep learning | |
Ahmed et al. | An appraisal of the performance of AI tools for chronic stroke lesion segmentation | |
Jain et al. | An automatic cascaded approach for pancreas segmentation via an unsupervised localization using 3D CT volumes | |
Astaraki et al. | Autopaint: A self-inpainting method for unsupervised anomaly detection | |
US20050036691A1 (en) | Method and system for using structure tensors to detect lung nodules and colon polyps | |
Talat et al. | A novel enhanced normalization technique for a mandible bones segmentation using deep learning: batch normalization with the dropout | |
Khan et al. | Segmentation of prostate in MRI images using depth separable convolution operations | |
da Silva et al. | Enhanced pre-processing for deep learning in MRI whole brain segmentation using orthogonal moments | |
Suzuki et al. | Interactive segmentation of pancreases from abdominal CT images by use of the graph cut technique with probabilistic atlases |