title: An implementation of a CBIR system based on SVM learning scheme.
abstract: Content-based image retrieval (CBIR) has been one of the most active areas of research. The retrieval principle of CBIR systems is based on visual features such as colour, texture and shape or the semantic meaning of the images. A CBIR system can be used to locate medical images in large databases. This paper presents a CBIR system for retrieving digital human brain magnetic resonance images (MRI) based on textural features and the support vector machine (SVM) learning method. This system can retrieve similar images from the database in two groups: normal and tumoural. This research uses the knowledge of the CBIR approach to the application of medical decision support and discrimination between the normal and abnormal medical images based on features. This study presents and compares the results of the proposed method with the CBIR systems used in recent works. The experimental results indicate that the proposed method is reliable and has high image retrieval efficiency compared with the previous works.
authors.affiliation: Department of Engineering, Abhar Branch, Islamic Azad University, Abhar, Iran. manatarjoman@gmail.com
authors.forename: Mana
authors.initials: M
authors.lastname: Tarjoman
authors.forename: Emad
authors.initials: E
authors.lastname: Fatemizadeh
authors.forename: Kambiz
authors.initials: K
authors.lastname: Badie
mesh_terms: D001921:Brain / Q000033:anatomy & histology; D001932:Brain Neoplasms / Q000473:pathology; D016208:Databases, Factual; D006801:Humans; D007091:Image Processing, Computer-Assisted / Q000379:methods*; D008279:Magnetic Resonance Imaging / Q000379:methods*; D060388:Support Vector Machine