Luts et al., 2009 - Google Patents
- ️Thu Jan 01 2009
Luts et al., 2009
View PDF-
Document ID
- 6549513675018103632 Author
- Laudadio T
- Idema A
- Simonetti A
- Heerschap A
- Vandermeulen D
- Suykens J
- Van Huffel S Publication year
- 2009 Publication venue
- NMR in Biomedicine: An International Journal Devoted to the Development and Application of Magnetic Resonance In vivo
External Links
Snippet
A new technique is presented to create nosologic images of the brain based on magnetic resonance imaging (MRI) and magnetic resonance spectroscopic imaging (MRSI). A nosologic image summarizes the presence of different tissues and lesions in a single image …
- 230000011218 segmentation 0 title abstract description 58
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
- 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
-
- 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
- G06T2207/20092—Interactive image processing based on input by user
- G06T2207/20104—Interactive definition of region of interest [ROI]
-
- 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
- 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
- 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
- G01—MEASURING; TESTING
- G01R—MEASURING ELECTRIC VARIABLES; MEASURING MAGNETIC VARIABLES
- G01R33/00—Arrangements or instruments for measuring magnetic variables
- G01R33/20—Arrangements or instruments for measuring magnetic variables involving magnetic resonance
- G01R33/44—Arrangements or instruments for measuring magnetic variables involving magnetic resonance using nuclear magnetic resonance [NMR]
Similar Documents
Publication | Publication Date | Title |
---|---|---|
Luts et al. | 2009 | Nosologic imaging of the brain: segmentation and classification using MRI and MRSI |
Cárdenes et al. | 2009 | A multidimensional segmentation evaluation for medical image data |
US20100260396A1 (en) | 2010-10-14 | integrated segmentation and classification approach applied to medical applications analysis |
Cai et al. | 2020 | Fully automated segmentation of head CT neuroanatomy using deep learning |
Goncalves et al. | 2014 | Self-supervised MRI tissue segmentation by discriminative clustering |
Zacharaki et al. | 2011 | Abnormality segmentation in brain images via distributed estimation |
Bahadure et al. | 2017 | Feature extraction and selection with optimization technique for brain tumor detection from MR images |
Radhakrishnan et al. | 2020 | Canny edge detection model in mri image segmentation using optimized parameter tuning method |
van de Sande et al. | 2023 | A review of machine learning applications for the proton MR spectroscopy workflow |
Menze et al. | 2008 | Mimicking the human expert: pattern recognition for an automated assessment of data quality in MR spectroscopic images |
Wegmayr et al. | 2019 | Generative aging of brain MR-images and prediction of Alzheimer progression |
Abdulkareem et al. | 2022 | Predicting post-contrast information from contrast agent free cardiac MRI using machine learning: Challenges and methods |
US12033755B2 (en) | 2024-07-09 | Method and arrangement for identifying similar pre-stored medical datasets |
Sanchez et al. | 2023 | FetMRQC: an open-source machine learning framework for multi-centric fetal brain MRI quality control |
Belkacem-Boussaid et al. | 2010 | Computer-aided classification of centroblast cells in follicular lymphoma |
Tran et al. | 2013 | High-dimensional MRI data analysis using a large-scale manifold learning approach |
Anand et al. | 2022 | Detection of tumor affected part from histopathological bone images using morphological classification and recurrent convoluted neural networks |
Saneipour et al. | 2019 | Improvement of MRI brain image segmentation using Fuzzy unsupervised learning |
Saad et al. | 2021 | A review on image segmentation techniques for MRI brain stroke lesion |
Noorizadeh et al. | 2020 | Multi-atlas based neonatal brain extraction using atlas library clustering and local label fusion |
Pota et al. | 2019 | Multivariate fuzzy analysis of brain tissue volumes and relaxation rates for supporting the diagnosis of relapsing-remitting multiple sclerosis |
Commowick et al. | 2015 | Diffusion MRI abnormalities detection with orientation distribution functions: A multiple sclerosis longitudinal study |
Ouarda | 2016 | MR Brain Real Images Segmentation Based Modalities Fusion and Estimation Et Maximization Approach |
Anderson et al. | 2007 | Automated classification of atherosclerotic plaque from magnetic resonance images using predictive models |
Luts et al. | 2011 | Nosologic Imaging of Brain Tumors Using MRI and MRSI |