An interactive cbir system based on anfis learning scheme for human brain magnetic resonance images retrieval

Tarjoman, M ; Sharif University of Technology | 2012

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  1. Type of Document: Article
  2. DOI: 10.1142/S1016237212002925
  3. Publisher: 2012
  4. Abstract:
  5. Content-based image retrieval (CBIR) has turned into an important and active potential research field with the advance of multimedia and imaging technology. It makes use of image features, such as color, texture and shape, to index images with minimal human intervention. A CBIR system can be used to locate medical images in large databases. In this paper we propose a CBIR system which describes the methodology for retrieving digital human brain magnetic resonance images (MRI) based on textural features and the Adaptive neuro-fuzzy inference system (ANFIS) learning to retrieve similar images from database in two categories: normal and tumoral. A fuzzy classifier has been used, because of the uncertainty in the results of classifier and capacity of learning. ANFIS is a good candidate for our categorization problem. Our proposed CBIR system can locate a query image in the category of normal or tumoral images in the online retrieval part. Finally, using a relevance feedback, we improve the effectiveness of our retrieval system. 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. We present and compare 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. Active potentials ; ANFIS ; CBIR system ; Digital humans ; Fuzzy classifiers ; Human brain ; Human intervention ; Image features ; Image retrieval efficiency ; Imaging technology ; Index image ; Large database ; Learning schemes ; Magnetic resonance image ; Medical images ; Query images ; Relevance feedback ; Research fields ; Retrieval systems ; Similar image ; Textural feature ; Brain ; Content based retrieval ; Feature extraction ; Feedback ; Fuzzy systems ; Image texture ; Magnetic resonance imaging ; Search engines ; Adaptive neuro fuzzy inference system ; Clinical effectiveness ; Clinical feature ; Comparative effectiveness ; Content based image retrieval ; Controlled study ; Data base ; Data extraction ; Decision support system ; Feedback system ; Fuzzy system ; Human ; Image retrieval ; Medical decision making ; Mental capacity ; Methodology ; Neuroimaging ; Nuclear magnetic resonance imaging ; Tumor calcinosis ; Visual discrimination
  8. Source: Biomedical Engineering - Applications, Basis and Communications ; Volume 24, Issue 1 , 2012 , Pages 27-36 ; 10162372 (ISSN)
  9. URL: http://www.worldscientific.com/doi/abs/10.4015/S1016237212002925