Clinical content detection for medical image retrieval
Chen, L, Tang, HL and Wells, I (2005) Clinical content detection for medical image retrieval 2005 27th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, Vols 1-7 . 6441 - 6444.
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Content-based image retrieval (CBIR) is the most widely used method for searching large-scale medical image collections; however this approach is not suitable for high-level applications as human experts are accustomed to manage medical images based on their clinical features rather than primitive features. Automatic detection of clinical features in a large-scale image database and realization of image retrieval by clinical content are still open issues. This paper presents a Markov random field (MRF) based model for clinical content detection. Multiple classifiers are applied to recognize a wide range of clinical features in a large-scale histological image database, and they are further combined to generate more reliable and robust estimation. Spatial contexts will cooperate with local estimations in the MRF based model to make a decision based on global consistency. The detected clinical features will provide a basis for image retrieval. Experiments have been carried out in a large-scale histological image database with promising results
|Uncontrolled Keywords:||Science & Technology, Technology, Engineering, Biomedical, Engineering|
|Divisions:||Faculty of Engineering and Physical Sciences > Electronic Engineering > Centre for Communication Systems Research|
|Deposited By:||Melanie Hughes|
|Deposited On:||21 Sep 2010 10:27|
|Last Modified:||16 Feb 2013 16:03|
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