Birkbeck et al., 2013 - Google Patents
Robust segmentation of challenging lungs in CT using multi-stage learning and level set optimizationBirkbeck et al., 2013
View PDF- Document ID
- 4021146422747902925
- Author
- Birkbeck N
- Sofka M
- Kohlberger T
- Zhang J
- Wetzl J
- Kaftan J
- Zhou S
- Publication year
- Publication venue
- Computational intelligence in biomedical imaging
External Links
Snippet
Automatic segmentation of lung tissue in thoracic CT scans is useful for diagnosis and treatment planning of pulmonary diseases. Unlike healthy lung tissue that is easily identifiable in CT scans, diseased lung parenchyma is hard to segment automatically due to …
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
- G06T2207/10088—Magnetic resonance imaging [MRI]
-
- 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
- G06T2207/20116—Active contour; Active surface; Snakes
-
- 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
- G06T2207/20—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
- G06K9/62—Methods or arrangements for recognition using electronic means
- G06K9/6217—Design or setup of recognition systems and techniques; Extraction of features in feature space; Clustering techniques; Blind source separation
-
- 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
- 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
- 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
- G06F—ELECTRICAL DIGITAL DATA PROCESSING
Similar Documents
Publication | Publication Date | Title |
---|---|---|
Ebrahimkhani et al. | A review on segmentation of knee articular cartilage: from conventional methods towards deep learning | |
Donner et al. | Global localization of 3D anatomical structures by pre-filtered Hough Forests and discrete optimization | |
Masood et al. | A survey on medical image segmentation | |
Nakagomi et al. | Multi-shape graph cuts with neighbor prior constraints and its application to lung segmentation from a chest CT volume | |
JP6005297B2 (en) | Bone and cartilage classification method and data processing system in magnetic resonance (MR) image | |
Nosrati et al. | Incorporating prior knowledge in medical image segmentation: a survey | |
Mittal et al. | Lung field segmentation in chest radiographs: a historical review, current status, and expectations from deep learning | |
Withey et al. | Medical image segmentation: Methods and software | |
Erdt et al. | Regmentation: A new view of image segmentation and registration | |
Shi et al. | Low-rank and sparse decomposition based shape model and probabilistic atlas for automatic pathological organ segmentation | |
US7876938B2 (en) | System and method for whole body landmark detection, segmentation and change quantification in digital images | |
Roy et al. | A review on automated brain tumor detection and segmentation from MRI of brain | |
EP1851722B1 (en) | Image processing device and method | |
US8724866B2 (en) | Multi-level contextual learning of data | |
Poudel et al. | Evaluation of commonly used algorithms for thyroid ultrasound images segmentation and improvement using machine learning approaches | |
Ochs et al. | Automated classification of lung bronchovascular anatomy in CT using AdaBoost | |
Zhou | Medical image recognition, segmentation and parsing: machine learning and multiple object approaches | |
Kamble et al. | A review on lung and nodule segmentation techniques | |
Asaturyan et al. | Morphological and multi-level geometrical descriptor analysis in CT and MRI volumes for automatic pancreas segmentation | |
Saad et al. | Exploration and visualization of segmentation uncertainty using shape and appearance prior information | |
Roy et al. | International journal of advanced research in computer science and software engineering | |
Suji et al. | Optical flow methods for lung nodule segmentation on LIDC-IDRI images | |
Hansen et al. | Unsupervised supervoxel-based lung tumor segmentation across patient scans in hybrid PET/MRI | |
Park et al. | Hierarchical MRF of globally consistent localized classifiers for 3D medical image segmentation | |
Birkbeck et al. | Robust segmentation of challenging lungs in CT using multi-stage learning and level set optimization |