@inproceedings{20ada5ccdde5458597bcacc3e7c98100,
title = "Different regions identification in composite strain encoded (C-SENC) images using machine learning techniques",
abstract = "Different heart tissue identification is important for therapeutic decision-making in patients with myocardial infarction (MI), this provides physicians with a better clinical decision-making tool. Composite Strain Encoding (C-SENC) is an MRI acquisition technique that is used to acquire cardiac tissue viability and contractility images. It combines the use of blackblood delayed-enhancement (DE) imaging to identify the infracted (dead) tissue inside the heart muscle and the ability to image myocardial deformation from the strain-encoding (SENC) imaging technique. In this work, various machine learning techniques are applied to identify the different heart tissues and the background regions in the C-SENC images. The proposed methods are tested using numerical simulations of the heart C-SENC images and real images of patients. The results show that the applied techniques are able to identify the different components of the image with a high accuracy.",
author = "A.G. Motaal and N.F. Osman and N. El-Gayar",
year = "2010",
doi = "10.1007/978-3-642-12159-3_21",
language = "English",
isbn = "978-3-642-12158-6",
series = "Lecture Notes in Computer Science",
publisher = "Springer",
pages = "231--240",
editor = "F. Schwenker and N. El-Gayar",
booktitle = "Artificial Neural Networks in Pattern Recognition : 4th IAPR TC3 Workshop, ANNPR 2010, Cairo, Egypt, April 11-13, 2010. Proceedings",
address = "Germany",
}