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Journal of Imaging Informatics in Medicine

Published in:

01-02-2014

Automatic Medical X-ray Image Classification using Annotation

Authors: Mohammad Reza Zare, Ahmed Mueen, Woo Chaw Seng

Published in: Journal of Imaging Informatics in Medicine | Issue 1/2014

Abstract

The demand for automatically classification of medical X-ray images is rising faster than ever. In this paper, an approach is presented to gain high accuracy rate for those classes of medical database with high ratio of intraclass variability and interclass similarities. The classification framework was constructed via annotation using the following three techniques: annotation by binary classification, annotation by probabilistic latent semantic analysis, and annotation using top similar images. Next, final annotation was constructed by applying ranking similarity on annotated keywords made by each technique. The final annotation keywords were then divided into three levels according to the body region, specific bone structure in body region as well as imaging direction. Different weights were given to each level of the keywords; they are then used to calculate the weightage for each category of medical images based on their ground truth annotation. The weightage computed from the generated annotation of query image was compared with the weightage of each category of medical images, and then the query image would be assigned to the category with closest weightage to the query image. The average accuracy rate reported is 87.5 %.

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Title: Automatic Medical X-ray Image Classification using Annotation
Authors: Mohammad Reza Zare
Ahmed Mueen
Woo Chaw Seng
Publication date: 01-02-2014
Publisher: Springer US
Published in: Journal of Imaging Informatics in Medicine / Issue 1/2014
Print ISSN: 2948-2925
Electronic ISSN: 2948-2933
DOI: https://doi.org/10.1007/s10278-013-9637-0

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Automatic Medical X-ray Image Classification using Annotation

Abstract

Keynote webinar | Spotlight on medication adherence

Springer Medicine

Abstract

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