Abstract
This paper presents a texture-based continuous probabilistic framework for robust image representation. According to the proposed approach, images taken at different angles are represented using several probabilistic models connected in parallel. The classification decision is made based on a maximum likelihood approach, which is insensitive to the angle at which the image was taken. The proposed approach is evaluated using a dataset of 100 images that includes three classes of anatomical structures of the upper airways. The results show that the approach can be used to efficiently and reliably represent and classify medical images acquired during various procedures.
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© 2015 Springer International Publishing Switzerland
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Lederman, D. (2015). Texture-Based Continuous Probabilistic Framework for Robust Medical Image Representation and Classification. In: Lacković, I., Vasic, D. (eds) 6th European Conference of the International Federation for Medical and Biological Engineering. IFMBE Proceedings, vol 45. Springer, Cham. https://doi.org/10.1007/978-3-319-11128-5_49
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DOI: https://doi.org/10.1007/978-3-319-11128-5_49
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-11127-8
Online ISBN: 978-3-319-11128-5
eBook Packages: EngineeringEngineering (R0)