An implementation of a CBIR system based on SVM learning scheme

Tarjoman, M ; Sharif University of Technology | 2013

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  1. Type of Document: Article
  2. DOI: 10.3109/03091902.2012.742157
  3. Publisher: 2013
  4. Abstract:
  5. 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
  6. Keywords:
  7. Content-based image retrieval ; Feature extraction ; Magnetic resonance image ; SVM ; Active area ; CBIR system ; Digital humans ; Image retrieval efficiency ; Large database ; Learning methods ; Learning schemes ; Magnetic resonance images ; Medical decision making ; Medical images ; Similar image ; Textural feature ; Visual feature ; Content based retrieval ; Magnetic resonance imaging ; Semantics ; Search engines ; Aََlgorithm ; Content based image retrieval ; Decision support system ; Diagnostic accuracy ; Image analysis ; Image retrieval ; Neuroimaging ; Nuclear magnetic resonance imaging ; Statistical significance ; Support vector machine ; Brain tumor ; Factual database ; Histology ; Human ; Image processing ; Methodology ; Pathology ; Brain ; Brain Neoplasms ; Databases, Factual ; Humans ; Image Processing, Computer-Assisted ; Support Vector Machines
  8. Source: Journal of Medical Engineering and Technology ; Volume 37, Issue 1 , 2013 , Pages 43-47 ; 03091902 (ISSN)
  9. URL: http://www.tandfonline.com/doi/abs/10.3109/03091902.2012.742157