FCSE at Medical Tasks of ImageCLEF 2013

This paper presents the details of the participation of FCSE (Faculty of Computer Science and Engineering) research team in Image- CLEF 2013 medical tasks (modality classication, ad-hoc image retrieval and case-based retrieval). For the modality classication task we used SIFT descriptors and tf idf weights of the surrounding text (image caption and paper title) as features. SVMs with 2 kernel and one-vs- all strategy were used as classiers. For the ad-hoc image retrieval task and case-based retrieval we adopted a strategy which uses a combination of word-space and concept-space approaches. The word-space approach uses the Terrier IR search engine to index and retrieve the text associ- ated with the images/cases. The concept-space approach uses Metamap to map the text data into a set of UMLS (Unied Medical Language System) concepts, which are later indexed and retrieved by the Terrier IR search engine. The results from the word-space and concept-space retrieval are fused using linear combination. For the compound gure separation task, we used unsupervised algorithm based on breadth-rst search strategy using only visual information from the medical images. The selected algorithms were tuned and tested on the data from Im- ageCLEF 2012 medical task and based on the selected parameters we submitted the new experiments for ImageCLEF 2013 medical task. We achieved very good overall performance: the best run for the modality classication ranked 2nd in the overall score, the best run for the ad-hoc