Subject: Image modality classification Visual image descriptors Feature fusion
Year: 2015
Type: Journal Article
Title: Improved medical image modality classification using a combination of visual and textual features
Author: Dimitrovski, Ivica
Author: Kocev, Dragi
Author: Kitanovski, Ivan
Author: Loshkovska, Suzana
Author: Džeroski, Sasho
Abstract: In this paper, we present the approach that we applied to the medical modality classification tasks at the ImageCLEF evaluation forum. More specifically, we used the modality classification databases from the ImageCLEF competitions in 2011, 2012 and 2013, described by four visual and one textual types of features, and combinations thereof. We used local binary patterns, color and edge directivity descriptors, fuzzy color and texture histogram and scale-invariantfeature transform (and its variant opponentSIFT) as visual features and the standard bag-of-words textual representation coupled with TF-IDF weighting. The results fromthe extensive experimental evaluationidentify the SIFT andopponentSIFT features as the best performing features for modality classification. Next, the low-level fusion of the visual features improves the predictive performance of the classifiers. This is because the different features are able to capture different aspects of an image, their combination offering a more complete representation of the visual content in an image. Moreover, adding textual features further increases the predictive performance. Finally, the results obtained with our approach are the best results reported on these databases so far.
Publisher: Pergamon
Relation: Computerized Medical Imaging and Graphics
Identifier: oai:repository.ukim.mk:20.500.12188/17849
Identifier: http://hdl.handle.net/20.500.12188/17849
