@inproceedings{bb124dc49850426884a28f148e83b753,
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 = "Motaal, {Abdallah G.} and Neamat El-Gayar and Osman, {Nael F.}",
year = "2010",
doi = "10.1007/978-3-642-12159-3_21",
language = "English",
isbn = "9783642121586",
series = "Lecture Notes in Computer Science",
publisher = "Springer",
pages = "231--240",
booktitle = "Artificial Neural Networks in Pattern Recognition. ANNPR 2010",
note = "4th IAPR TC3 Workshop on Artificial Neural Networks in Pattern Recognition 2010, ANNPR 2010 ; Conference date: 11-04-2010 Through 13-04-2010",
}