FCSE at Medical Tasks of ImageCLEF 2013
Ivan Kitanovski, Ivica Dimitrovski, Suzana Loškovska · 2013
Abstract. 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 classification, ad-hoc image retrieval and case-based retrieval). For the modality classification task we used SIFT descriptors and tf − idf weights of the surrounding text (image caption and paper title) as features. SVMs with χ2 kernel and one-vs-all strategy were used as classifiers. 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 (Unified 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 figure separation task, we used unsupervised algorithm based on breadth-first 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 classification ranked 2nd in the overall score, the best run for the ad-hoc image retrieval ranked 3rd.