Jaffar et al., 2018 - Google Patents
Ensemble classification of pulmonary nodules using gradient intensity feature descriptor and differential evolutionJaffar et al., 2018
- Document ID
- 3509534015980204435
- Author
- Jaffar M
- Siddiqui A
- Mushtaq M
- Publication year
- Publication venue
- Cluster Computing
External Links
Snippet
For detection and classification of pulmonary nodules, there are two major issues exists in the existing computer aided diagnosis system. First major problem is automatic threshold to segment lungs and nodules. Threshold selection is a critical preprocessing step for medical …
- 206010054107 Nodule 0 title abstract description 38
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
- G06T2207/10—Image acquisition modality
- G06T2207/10072—Tomographic images
- G06T2207/10088—Magnetic resonance imaging [MRI]
-
- 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
- G06K9/6268—Classification techniques relating to the classification paradigm, e.g. parametric or non-parametric approaches
-
- 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
- G06K9/52—Extraction of features or characteristics of the image by deriving mathematical or geometrical properties from the whole image
- G06K9/527—Scale-space domain transformation, e.g. with wavelet analysis
-
- 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
- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T7/00—Image analysis
- G06T7/40—Analysis of texture
-
- 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
- G06K2209/05—Recognition of patterns in medical or anatomical images
-
- 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
- G06F—ELECTRICAL DIGITAL DATA PROCESSING
- G06F19/00—Digital computing or data processing equipment or methods, specially adapted for specific applications
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| Halder et al. | Lung nodule detection from feature engineering to deep learning in thoracic CT images: a comprehensive review | |
| Meraj et al. | Lung nodules detection using semantic segmentation and classification with optimal features | |
| Firmino et al. | Computer-aided detection (CADe) and diagnosis (CADx) system for lung cancer with likelihood of malignancy | |
| Kallenberg et al. | Unsupervised deep learning applied to breast density segmentation and mammographic risk scoring | |
| Moftah et al. | Adaptive k-means clustering algorithm for MR breast image segmentation | |
| Mohammed et al. | Artificial neural networks for automatic segmentation and identification of nasopharyngeal carcinoma | |
| Lu et al. | Fully automatic liver segmentation combining multi-dimensional graph cut with shape information in 3D CT images | |
| Wang et al. | Shape–intensity prior level set combining probabilistic atlas and probability map constrains for automatic liver segmentation from abdominal CT images | |
| El-Regaily et al. | Survey of computer aided detection systems for lung cancer in computed tomography | |
| Taşcı et al. | Shape and texture based novel features for automated juxtapleural nodule detection in lung CTs | |
| Manickavasagam et al. | Automatic detection and classification of lung nodules in CT image using optimized neuro fuzzy classifier with cuckoo search algorithm | |
| Liu et al. | A CADe system for nodule detection in thoracic CT images based on artificial neural network | |
| Peng et al. | Segmentation of lung in chest radiographs using hull and closed polygonal line method | |
| Jaffar et al. | Ensemble classification of pulmonary nodules using gradient intensity feature descriptor and differential evolution | |
| Cai | Segmentation and diagnosis of liver carcinoma based on adaptive scale-kernel fuzzy clustering model for CT images | |
| Govindarajan et al. | Extreme learning machine based differentiation of pulmonary tuberculosis in chest radiographs using integrated local feature descriptors | |
| Sivasankaran et al. | Lung Cancer Detection Using Image Processing Technique Through Deep Learning Algorithm. | |
| Rani et al. | Superpixel with nanoscale imaging and boosted deep convolutional neural network concept for lung tumor classification | |
| Gonçalves et al. | Learning lung nodule malignancy likelihood from radiologist annotations or diagnosis data | |
| Cairone et al. | Robustness of radiomics features to varying segmentation algorithms in magnetic resonance images | |
| Jaffar et al. | An ensemble shape gradient features descriptor based nodule detection paradigm: a novel model to augment complex diagnostic decisions assistance | |
| Li et al. | Segmentation of pulmonary nodules using adaptive local region energy with probability density function-based similarity distance and multi-features clustering | |
| Kumar et al. | Lung nodule segmentation using 3-dimensional convolutional neural networks | |
| Liu et al. | Lung CT image segmentation via dilated U-Net model and multi-scale gray correlation-based approach | |
| Siddiqui et al. | Computed tomography image Processing methods for lung nodule detection and classification: a review |