Different regions identification in composite strain-encoded (C-SENC) images using machine learning techniques

Abdallah G. Motaal, Neamat El-Gayar, Nael F. Osman

Research output: Chapter in Book/Report/Conference proceedingConference contribution

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.

Original languageEnglish (US)
Title of host publicationArtificial Neural Networks in Pattern Recognition - 4th IAPR TC3 Workshop, ANNPR 2010, Proceedings
Pages231-240
Number of pages10
DOIs
StatePublished - May 21 2010
Event4th IAPR TC3 Workshop on Artificial Neural Networks in Pattern Recognition, ANNPR 2010 - Cairo, Egypt
Duration: Apr 11 2010Apr 13 2010

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume5998 LNAI
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Other

Other4th IAPR TC3 Workshop on Artificial Neural Networks in Pattern Recognition, ANNPR 2010
CountryEgypt
CityCairo
Period4/11/104/13/10

ASJC Scopus subject areas

  • Theoretical Computer Science
  • Computer Science(all)

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    Motaal, A. G., El-Gayar, N., & Osman, N. F. (2010). Different regions identification in composite strain-encoded (C-SENC) images using machine learning techniques. In Artificial Neural Networks in Pattern Recognition - 4th IAPR TC3 Workshop, ANNPR 2010, Proceedings (pp. 231-240). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 5998 LNAI). https://doi.org/10.1007/978-3-642-12159-3_21