Al-Tamimi et al., 2015 - Google Patents
A New Method for Detecting Cerebral Tissues Abnormality in Magnetic Resonance ImagesAl-Tamimi et al., 2015
View PDF- Document ID
- 13121779032931157553
- Author
- Al-Tamimi M
- Sulong G
- Publication year
- Publication venue
- Modern Applied Science
External Links
Snippet
We propose a new method for detecting the abnormality in cerebral tissues present within Magnetic Resonance Images (MRI). Present classifier is comprised of cerebral tissue extraction, image division into angular and distance span vectors, acquirement of four …
- 210000001519 tissues 0 title abstract description 33
Classifications
-
- 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
- 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
- G06K9/6232—Extracting features by transforming the feature space, e.g. multidimensional scaling; Mappings, e.g. subspace methods
- G06K9/6247—Extracting features by transforming the feature space, e.g. multidimensional scaling; Mappings, e.g. subspace methods based on an approximation criterion, e.g. principal component analysis
-
- 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
- G06F19/30—Medical informatics, i.e. computer-based analysis or dissemination of patient or disease data
- G06F19/34—Computer-assisted medical diagnosis or treatment, e.g. computerised prescription or delivery of medication or diets, computerised local control of medical devices, medical expert systems or telemedicine
- G06F19/345—Medical expert systems, neural networks or other automated diagnosis
-
- 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
- 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
-
- 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
- G06K9/00221—Acquiring or recognising human faces, facial parts, facial sketches, facial expressions
- G06K9/00268—Feature extraction; Face representation
- G06K9/00281—Local features and components; Facial parts ; Occluding parts, e.g. glasses; Geometrical relationships
-
- 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
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06N—COMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS
- G06N99/00—Subject matter not provided for in other groups of this subclass
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| Garali et al. | Histogram-based features selection and volume of interest ranking for brain PET image classification | |
| US8340437B2 (en) | Methods and systems for determining optimal features for classifying patterns or objects in images | |
| Ghafoorian et al. | Automated detection of white matter hyperintensities of all sizes in cerebral small vessel disease | |
| El Azami et al. | Detection of lesions underlying intractable epilepsy on T1-weighted MRI as an outlier detection problem | |
| Naik et al. | Denouements of machine learning and multimodal diagnostic classification of Alzheimer’s disease | |
| Gupta et al. | Classification of patients with tumor using MR FLAIR images | |
| Yang et al. | A review of artificial intelligence technologies for early prediction of Alzheimer's disease | |
| Gumaste et al. | A hybrid method for brain tumor detection using advanced textural feature extraction | |
| Abdullah et al. | Multi-sectional views textural based SVM for MS lesion segmentation in multi-channels MRIs | |
| Illán et al. | Projecting independent components of SPECT images for computer aided diagnosis of Alzheimer’s disease | |
| Iqbal et al. | AMIAC: adaptive medical image analyzes and classification, a robust self-learning framework | |
| Zemmal et al. | CAD system for classification of mammographic abnormalities using transductive semi supervised learning algorithm and heterogeneous features | |
| Ho et al. | Feature-level ensemble approach for COVID-19 detection using chest X-ray images | |
| Salas-Gonzalez et al. | Analysis of SPECT brain images for the diagnosis of Alzheimer's disease using moments and support vector machines | |
| Bahadure et al. | Feature extraction and selection with optimization technique for brain tumor detection from MR images | |
| Tiwari | Wilson’s disease classification using higher-order Gabor tensors and various classifiers on a small and imbalanced brain MRI dataset | |
| US20110103656A1 (en) | Quantification of Plaques in Neuroimages | |
| Garali et al. | Brain region of interest selection for 18FDG positrons emission tomography computer-aided image classification | |
| Shah et al. | A Bibliography of multiple sclerosis lesions detection methods using brain MRIs | |
| Al-Tamimi et al. | A New Method for Detecting Cerebral Tissues Abnormality in Magnetic Resonance Images | |
| Basheera et al. | Leung-Malik features and Adaboost perform classification of Alzheimer’s disease stages | |
| Padilla et al. | Alzheimer's disease detection in functional images using 2D Gabor wavelet analysis | |
| Abdulla | A computer-aided diagnosis system for brain tumors in magnetic resonance imaging (MRI) | |
| Kareem et al. | Medical image categorization combining image segmentation and machine learning | |
| Chaddad | Deep radiomics for autism diagnosis and age prediction |