@inproceedings{3e26160a56eb45039b82a222e4531014,
title = "Texture-based continuous probabilistic framework for robust medical image representation and classification",
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.",
keywords = "Classification, Gaussian mixture models, Medical imaging, Textural features",
author = "Dror Lederman",
note = "Publisher Copyright: {\textcopyright} Springer International Publishing Switzerland 2015; 6th European Conference of the International Federation for Medical and Biological Engineering, MBEC 2014 ; Conference date: 07-09-2014 Through 11-09-2014",
year = "2015",
month = jan,
day = "1",
doi = "10.1007/978-3-319-11128-5\_49",
language = "English",
series = "IFMBE Proceedings",
publisher = "Springer Verlag",
pages = "196--199",
editor = "Igor Lackovic and Darko Vasic",
booktitle = "6th European Conference of the International Federation for Medical and Biological Engineering - MBEC 2014",
address = "Germany",
}