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Mammogram image retrieval via sparse representation
, Article 2011 1st Middle East Conference on Biomedical Engineering, MECBME 2011, Sharjah, 21 February 2011 through 24 February 2011 ; 2011 , Pages 63-66 ; 9781424470006 (ISBN) ; Ghaffari, A ; Fatemizadeh, E ; Sharif University of Technology
2011
Abstract
In recent years there has been a great effort to enhance the computer-aided diagnosis systems, since proven similar pathologies, in the past, plays an important role in diagnosis of the current cases, content based medical image retrieval has been emerged. In this work we have designed a decision making machine in which utilizes sparse representation technique to preserve semantic category relevance among the retrieved images and the query image, this machine comprises optimized wavelets (adapted using lifting scheme) to extract appropriate visual features in order to grasp visual content of the images, afterwards by using some classical methods, Raw data vectors become applicable for sparse...
An interactive cbir system based on anfis learning scheme for human brain magnetic resonance images retrieval
, Article Biomedical Engineering - Applications, Basis and Communications ; Volume 24, Issue 1 , 2012 , Pages 27-36 ; 10162372 (ISSN) ; Fatemizadeh, E ; Badie, K ; Sharif University of Technology
2012
Abstract
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...