[go: up one dir, main page]

Stocker et al., 1996 - Google Patents

Stability study of some neural networks applied to tissue characterization of brain magnetic resonance images

Stocker et al., 1996

Document ID
2617443533288391157
Author
Stocker A
Sipila O
Visa A
Salonen O
Katila T
Publication year
Publication venue
Proceedings of 13th International Conference on Pattern Recognition

External Links

Snippet

This study investigates the segmentation ability of unsupervised clustering of the image feature space. A self-organizing map, a feed-forward neural network and a k-nearest neighbor classifier were compared in labeling brain slices from magnetic resonance …
Continue reading at ieeexplore.ieee.org (other versions)

Classifications

    • GPHYSICS
    • G06COMPUTING; CALCULATING; COUNTING
    • G06TIMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T2207/00Indexing scheme for image analysis or image enhancement
    • G06T2207/30Subject of image; Context of image processing
    • G06T2207/30004Biomedical image processing
    • GPHYSICS
    • G06COMPUTING; CALCULATING; COUNTING
    • G06TIMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T7/00Image analysis
    • G06T7/0002Inspection of images, e.g. flaw detection
    • G06T7/0012Biomedical image inspection
    • GPHYSICS
    • G06COMPUTING; CALCULATING; COUNTING
    • G06KRECOGNITION OF DATA; PRESENTATION OF DATA; RECORD CARRIERS; HANDLING RECORD CARRIERS
    • G06K9/00Methods or arrangements for reading or recognising printed or written characters or for recognising patterns, e.g. fingerprints
    • G06K9/62Methods or arrangements for recognition using electronic means
    • G06K9/6217Design or setup of recognition systems and techniques; Extraction of features in feature space; Clustering techniques; Blind source separation
    • GPHYSICS
    • G06COMPUTING; CALCULATING; COUNTING
    • G06TIMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T2207/00Indexing scheme for image analysis or image enhancement
    • G06T2207/10Image acquisition modality
    • GPHYSICS
    • G06COMPUTING; CALCULATING; COUNTING
    • G06FELECTRICAL DIGITAL DATA PROCESSING
    • G06F19/00Digital computing or data processing equipment or methods, specially adapted for specific applications
    • G06F19/30Medical informatics, i.e. computer-based analysis or dissemination of patient or disease data
    • G06F19/32Medical data management, e.g. systems or protocols for archival or communication of medical images, computerised patient records or computerised general medical references
    • GPHYSICS
    • G06COMPUTING; CALCULATING; COUNTING
    • G06TIMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T2207/00Indexing scheme for image analysis or image enhancement
    • G06T2207/20Special algorithmic details
    • GPHYSICS
    • G06COMPUTING; CALCULATING; COUNTING
    • G06TIMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T7/00Image analysis
    • G06T7/40Analysis of texture
    • GPHYSICS
    • G06COMPUTING; CALCULATING; COUNTING
    • G06KRECOGNITION OF DATA; PRESENTATION OF DATA; RECORD CARRIERS; HANDLING RECORD CARRIERS
    • G06K9/00Methods or arrangements for reading or recognising printed or written characters or for recognising patterns, e.g. fingerprints
    • G06K9/00127Acquiring and recognising microscopic objects, e.g. biological cells and cellular parts

Similar Documents

Publication Publication Date Title
Collewet et al. Influence of MRI acquisition protocols and image intensity normalization methods on texture classification
Kannan A new segmentation system for brain MR images based on fuzzy techniques
US4945478A (en) Noninvasive medical imaging system and method for the identification and 3-D display of atherosclerosis and the like
US7317821B2 (en) Automatic abnormal tissue detection in MRI images
Acharya et al. An accurate and generalized approach to plaque characterization in 346 carotid ultrasound scans
Raja'S et al. Labeling of lumbar discs using both pixel-and object-level features with a two-level probabilistic model
Giger et al. Computerized characterization of mammographic masses: analysis of spiculation
Bezdek et al. Medical image analysis with fuzzy models
Korchiyne et al. A combined method of fractal and GLCM features for MRI and CT scan images classification
EP1974313A2 (en) An integrated segmentation and classification approach applied to medical applications analysis
Mayerhoefer et al. Texture analysis for tissue discrimination on T1‐weighted MR images of the knee joint in a multicenter study: transferability of texture features and comparison of feature selection methods and classifiers
Gentillon et al. Parameter set for computer-assisted texture analysis of fetal brain
Wismuller et al. Cluster analysis of dynamic cerebral contrast-enhanced perfusion MRI time-series
Omiotek et al. Fractal analysis of the computed tomography images of vertebrae on the thoraco-lumbar region in diagnosing osteoporotic bone damage
US6674880B1 (en) Convolution filtering of similarity data for visual display of enhanced image
KR102373992B1 (en) Method and apparatut for alzheimer's disease classification using texture features
US20100303318A1 (en) Method for Analysing an Image of the Brain of a Subject, Computer Program Product for Analysing Such Image and Apparatus for Implementing the Method
Seifert et al. A knowledge-based approach to soft tissue reconstruction of the cervical spine
Twellmann et al. Model-free visualization of suspicious lesions in breast MRI based on supervised and unsupervised learning
Meyer-Bäse et al. Unsupervised clustering of fMRI and MRI time series
AU1796001A (en) Convolution filtering of similarity data for visual display of enhanced image
Stocker et al. Stability study of some neural networks applied to tissue characterization of brain magnetic resonance images
Glass et al. Hybrid artificial neural network segmentation of precise and accurate inversion recovery (PAIR) images from normal human brain☆
Saad et al. Brain lesion segmentation of Diffusion-weighted MRI using gray level co-occurrence matrix
Rodrigues et al. Severity classification in cases of Collagen VI-related myopathy with Convolutional Neural Networks and handcrafted texture features