He et al., 2017 - Google Patents
Extraction Technique of Spicules-Based Features for the Classification of Pulmonary Nodules on Computed TomographyHe et al., 2017
- Document ID
- 13321572250558192189
- Author
- He X
- Gong J
- Wang L
- Nie S
- Publication year
- Publication venue
- Advanced Computational Methods in Life System Modeling and Simulation: International Conference on Life System Modeling and Simulation, LSMS 2017 and International Conference on Intelligent Computing for Sustainable Energy and Environment, ICSEE 2017, Nanjing, China, September 22-24, 2017, Proceedings, Part I
External Links
Snippet
To avoid the deformation of spicules surrounding pulmonary nodules caused by the classic rubber band straightening transform (RBST), we propose a novel RBST technique to extract spicules-based features. In this paper, the run-length statistics (RLS) features are extracted …
- 206010054107 Nodule 0 title abstract description 23
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
- G06T2207/10024—Color image
-
- 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
- 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
- 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
- G06T11/00—2D [Two Dimensional] image generation
-
- 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
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06F—ELECTRICAL DIGITAL DATA PROCESSING
- G06F17/00—Digital computing or data processing equipment or methods, specially adapted for specific functions
Similar Documents
Publication | Publication Date | Title |
---|---|---|
Shi et al. | Prediction of occult invasive disease in ductal carcinoma in situ using deep learning features | |
Putra et al. | Enhanced skin condition prediction through machine learning using dynamic training and testing augmentation | |
Senan et al. | Analysis of dermoscopy images by using ABCD rule for early detection of skin cancer | |
Sang et al. | Automated detection and classification for early stage lung cancer on CT images using deep learning | |
Rajinikanth et al. | Skin melanoma assessment using Kapur’s entropy and level set—a study with bat algorithm | |
Jaworek-Korjakowska | Computer‐aided diagnosis of micro‐malignant melanoma lesions applying support vector machines | |
Stalin David et al. | A new expert system based on hybrid colour and structure descriptor and machine learning algorithms for early glaucoma diagnosis | |
Wahba et al. | A novel cumulative level difference mean based GLDM and modified ABCD features ranked using eigenvector centrality approach for four skin lesion types classification | |
Junior et al. | A mass classification using spatial diversity approaches in mammography images for false positive reduction | |
Liu et al. | Symmetric-constrained irregular structure inpainting for brain mri registration with tumor pathology | |
Tripathi et al. | Non-invasively grading of brain tumor through noise robust textural and intensity based features | |
Amkrane et al. | Towards breast cancer response prediction using artificial intelligence and radiomics | |
Zhao et al. | Segmentation of dermoscopy images based on deformable 3D convolution and ResU-NeXt++ | |
Shanker et al. | Brain tumor segmentation of normal and lesion tissues using hybrid clustering and hierarchical centroid shape descriptor | |
Momeni et al. | Dropout-enabled ensemble learning for multi-scale biomedical data | |
Xiao et al. | FastNet: a lightweight convolutional neural network for tumors fast identification in mobile-computer-assisted devices | |
Krivov et al. | MRI augmentation via elastic registration for brain lesions segmentation | |
Bangalore Yogananda et al. | Disparity autoencoders for multi-class brain tumor segmentation | |
Ma et al. | Automatic pulmonary ground‐glass opacity nodules detection and classification based on 3D neural network | |
Shanker et al. | Brain tumor segmentation of normal and pathological tissues using K-mean clustering with fuzzy C-mean clustering | |
Wang et al. | Mutual learning model for skin lesion classification | |
US8913843B2 (en) | Image processing method and computer program | |
Goceri et al. | Automated Detection of Facial Disorders (ADFD): a novel approach based-on digital photographs | |
He et al. | Extraction Technique of Spicules-Based Features for the Classification of Pulmonary Nodules on Computed Tomography | |
Almakady et al. | Volumetric texture analysis based on three-dimensional gaussian markov random fields for copd detection |