@inproceedings{7a486d36a4744bb0b1d041285b278ab4,
title = "Application of fuzzy image concept to medical images matching",
abstract = "The main aim of this research is presenting an automated image matching methodology being used in the field of medicine for inter- and intraobjectional image matching. This paper shows a different approach avoiding the standard procedures associated with performing four main steps of the registration process: feature detection, feature matching, mapping function design and image transformation with resampling, and replacing them with the fuzzy image concept combined with the use of similarity measures. This methodology has been implemented in MATLAB and tested on clinical T1- and T2-weighted magnetic resonance imaging (MRI) slices of the knee joint in coronal and sagittal plane.",
keywords = "Cruciate ligament, Entropy measure of fuzziness, Knee joint, Medical image matching, Similarity measures",
author = "Piotr Zarychta",
note = "Publisher Copyright: {\textcopyright} 2019, Springer International Publishing AG, part of Springer Nature.; 6th International Conference on Information Technology in Biomedicine, ITIB 2018 ; Conference date: 18-06-2018 Through 20-06-2018",
year = "2019",
doi = "10.1007/978-3-319-91211-0\_3",
language = "English",
isbn = "9783319912103",
series = "Advances in Intelligent Systems and Computing",
publisher = "Springer Verlag",
pages = "27--38",
editor = "Ewa Pietka and Pawel Badura and Jacek Kawa and Wojciech Wieclawek",
booktitle = "Information Technology in Biomedicine - Proceedings 6th International Conference, ITIB{\textquoteright}2018",
address = "Germany",
}