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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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Abstract

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

Item Type: Article
Uncontrolled Keywords: Science & Technology, Technology, Engineering, Biomedical, Engineering
Related URLs:
Divisions: Faculty of Engineering and Physical Sciences > Electronic Engineering > Centre for Communication Systems Research
Depositing User: Melanie Hughes
Date Deposited: 21 Sep 2010 09:27
Last Modified: 23 Sep 2013 18:37
URI: http://epubs.surrey.ac.uk/id/eprint/2317

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