Treffer: Combining visual and textual features for medical image modality classification with ℓp - norm multiple kernel learning

Title:
Combining visual and textual features for medical image modality classification with ℓp - norm multiple kernel learning
Authors:
HONG WU, LELE HE
Source:
Neurocomputing (Amsterdam). 147:387-394
Publisher Information:
Amsterdam: Elsevier, 2015.
Publication Year:
2015
Physical Description:
print, 38 ref
Original Material:
INIST-CNRS
Subject Terms:
Cognition, Computer science, Informatique, Sciences exactes et technologie, Exact sciences and technology, Sciences appliquees, Applied sciences, Informatique; automatique theorique; systemes, Computer science; control theory; systems, Logiciel, Software, Organisation des mémoires. Traitement des données, Memory organisation. Data processing, Traitement des données. Listes et chaînes de caractères, Data processing. List processing. Character string processing, Intelligence artificielle, Artificial intelligence, Apprentissage et systèmes adaptatifs, Learning and adaptive systems, Reconnaissance des formes. Traitement numérique des images. Géométrie algorithmique, Pattern recognition. Digital image processing. Computational geometry, Sciences biologiques et medicales, Biological and medical sciences, Sciences medicales, Medical sciences, Informatique, statistique et modelisations biomedicales, Computerized, statistical medical data processing and models in biomedicine, Documentation médicale informatisée, Computerized medical documentation, Algorithme apprentissage, Learning algorithm, Algoritmo aprendizaje, Analyse image, Image analysis, Análisis imagen, Analyse texture, Texture analysis, Análisis textura, Classification automatique, Automatic classification, Clasificación automática, Donnée textuelle, Textual data, Dato textual, Filtre Gabor, Gabor filter, Filtro Gabor, Imagerie médicale, Medical imagery, Imaginería médica, Information visuelle, Visual information, Información visual, Informatique biomédicale, Biomedical data processing, Informática biomédical, Méthode noyau, Kernel method, Método núcleo, Résultat expérimental, Experimental result, Resultado experimental, Test statistique, Statistical test, Test estadístico, Texte, Text, Texto, Traitement image, Image processing, Procesamiento imagen, Vision ordinateur, Computer vision, Visión ordenador, Appariement image, Image matching, reconocimiento de patrones en imágenes, Classification image, Image classification, Clasificación de imágenes, Classification multiple, Multiple classification, clasificación múltiple, Recherche image, Image retrieval, Búsqueda de imagen, Feature combination, Medical image, Modality classification, Multiple kernel learning
Document Type:
Fachzeitschrift Article
File Description:
text
Language:
English
Author Affiliations:
School of Computer Science and Engineering, University of Electronic Science and Technology of China, 611731 Chengdu, China
ISSN:
0925-2312
Access URL:
http://pascal-francis.inist.fr/vibad/index.php?action=search&terms=28836764
Rights:
Copyright 2015 INIST-CNRS
CC BY 4.0
Sauf mention contraire ci-dessus, le contenu de cette notice bibliographique peut être utilisé dans le cadre d’une licence CC BY 4.0 Inist-CNRS / Unless otherwise stated above, the content of this bibliographic record may be used under a CC BY 4.0 licence by Inist-CNRS / A menos que se haya señalado antes, el contenido de este registro bibliográfico puede ser utilizado al amparo de una licencia CC BY 4.0 Inist-CNRS
Notes:
Computer science; theoretical automation; systems

Public health. Hygiene-occupational medicine. Information processing
Accession Number:
edscal.28836764
Database:
PASCAL Archive

Weitere Informationen

Automatic modality classification of medical images is an important tool for medical image retrieval. In this paper, we combine visual and textual information for modality classification. The visual features used are SIFT feature, LBP feature, Gabor texture feature and Tamura texture feature. And the textual feature is a tf-idf feature vector drawn from image description text. We combine these features by ℓp-norm multiple kernel learning (ℓp-norm MKL), and use One-vs-All approach for this multi-class problem. ℓp-norm MKL is explored with different norm value (p≥ 1). These MKL based methods are compared with several other feature combination methods and evaluated on the dataset of modality classification task in ImageCLEFmed 2010. The experimental results indicate that multiple kernel learning is a promising approach to combine visual and textual features for modality classification, and outperforms other simple kernel combination methods and the traditional early fusion method.