Article

Case retrieval in medical databases by fusing heterogeneous information

Department of Image et Traitement de l'Information, Institut Telecom/Telecom Bretagne, F-29200 Brest, France.
IEEE transactions on medical imaging 01/2011; 30(1):108-18. DOI: 10.1109/TMI.2010.2063711
Source: PubMed

ABSTRACT A novel content-based heterogeneous information retrieval framework, particularly well suited to browse medical databases and support new generation computer aided diagnosis (CADx) systems, is presented in this paper. It was designed to retrieve possibly incomplete documents, consisting of several images and semantic information, from a database; more complex data types such as videos can also be included in the framework. The proposed retrieval method relies on image processing, in order to characterize each individual image in a document by their digital content, and information fusion. Once the available images in a query document are characterized, a degree of match, between the query document and each reference document stored in the database, is defined for each attribute (an image feature or a metadata). A Bayesian network is used to recover missing information if need be. Finally, two novel information fusion methods are proposed to combine these degrees of match, in order to rank the reference documents by decreasing relevance for the query. In the first method, the degrees of match are fused by the Bayesian network itself. In the second method, they are fused by the Dezert-Smarandache theory: the second approach lets us model our confidence in each source of information (i.e., each attribute) and take it into account in the fusion process for a better retrieval performance. The proposed methods were applied to two heterogeneous medical databases, a diabetic retinopathy database and a mammography screening database, for computer aided diagnosis. Precisions at five of 0.809 ± 0.158 and 0.821 ± 0.177, respectively, were obtained for these two databases, which is very promising.

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    • "Considering related diseases have similar solutions, medical case retrieval is suggested by heterogeneous information fusion [4]. In addition, support vector machine (SVM)-based frameworks are also popular in medical image retrieval system during image filtering and dynamic features fusion [5]. "
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    • "Several studies have shown that combining CBIR with natural language processing (NLP) of medical unstructured text (e.g., anamnesis, diagnosis) associated with the images and hosted in the EMR may significantly improve query completion [80,81]. Case retrieval based on both image and contextual information has been used, e.g., by Quellec et al., who developed a framework for the retrieval of cases in medical databases [82]. Results from ImageCLEF show that combining textual and visual information is important for effective retrieval [74]. "
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