Visual information retrieval
Visualizing Topic Maps for e-Learning
ICALT '05 Proceedings of the Fifth IEEE International Conference on Advanced Learning Technologies
Using contexts to personalize educational topic maps
AH'06 Proceedings of the 4th international conference on Adaptive Hypermedia and Adaptive Web-Based Systems
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The paper presents original query modalities on a multimedia database that stores medical images and the associated information, for educational goal. So, a modern and efficient system for professional accomplishment is offered to the medical superior education (including residents, young specialists, family doctors and medical assistants). Specialists can update the medical image database with images acquired from different patients in the diagnosis and treatment process. A series of alphanumerical information: diagnosis, treatment and patient evolution can be added for each image. The database can be browsed, simply text-based queried or content-based queried using colour and texture characteristics automatically extracted from medical images at their loading in the database. An original element is the presence of a topic map based on a part of MeSH thesaurus, the part that includes the medical diagnosis names. The student can navigate through topic map depending on its interest subject, having in this way big advantages. He does not have to be familiar with the logic of the database, he will learn about the semantic context, in which a collection and its single items are embedded and he may find useful items he would not have expected to find in the beginning. Also, semantic queries against the multimedia database can be automatically launched with the help of the topic map. All these access paths can be combined for retrieving the interest information. Using content-based visual query with other access methods on a teaching image database allows students to see images and associated information from database in a simple and direct manner. This method stimulates learning, by comparing similar cases along with their particularities, or by comparing cases that are visually similar, but with different diagnoses.