@inproceedings{dbd37739987e40888726373b13556c2d,
title = "Automated cardiac-tissue identification in composite strain-encoded (C-SECN) images using fuzzy K-means and bayesian classifier",
abstract = "Composite Strain Encoding (C-SENC) is an MRI acquisition technique for simultaneous acquisition of cardiac tissue viability and contractility images. It combines the use of black-blood delayed-enhancement imaging to identify the infracted (dead) tissue inside the heart wall muscle and the ability to image myocardial deformation (MI) from the strain-encoding (SENC) imaging technique. In this work, we propose an automatic image processing technique to identify the different heart tissues. This provides physicians with a better clinical decision-making tool in patients with myocardial infarction. The technique is based on using Bayesian classifier to identify the background regions in the C-SENC images, and fuzzy clustering technique to identify the different types of the heart tissues. The proposed method is tested using numerical simulations of the heart C-SENC images with MI and real images of patients. The results show that the proposed technique is able to identify the different components of the image with a high accuracy.",
keywords = "Bayesian classifier, Cardiac magnetic resonance, Composite senc, Delayed enhancement, Fuzzy k-means clustering, SENC, Strain encoding",
author = "Motaal, {Abdallah G.} and Neamat El-Gayar and Osman, {Nael F.}",
year = "2010",
month = sep,
day = "6",
doi = "10.1109/ICBBE.2010.5517766",
language = "English (US)",
isbn = "9781424447138",
series = "2010 4th International Conference on Bioinformatics and Biomedical Engineering, iCBBE 2010",
booktitle = "2010 4th International Conference on Bioinformatics and Biomedical Engineering, iCBBE 2010",
note = "4th International Conference on Bioinformatics and Biomedical Engineering, iCBBE 2010 ; Conference date: 18-06-2010 Through 20-06-2010",
}