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
Title of host publicationArtificial Neural Networks in Pattern Recognition. ANNPR 2010
PublisherSpringer
Pages231-240
Number of pages10
ISBN (Electronic)9783642121593
ISBN (Print)9783642121586
DOIs
Publication statusPublished - 2010
Event4th IAPR TC3 Workshop on Artificial Neural Networks in Pattern Recognition 2010 - Cairo, Egypt
Duration: 11 Apr 201013 Apr 2010

Publication series

NameLecture Notes in Computer Science
Volume5998
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Conference

Conference4th IAPR TC3 Workshop on Artificial Neural Networks in Pattern Recognition 2010
Abbreviated titleANNPR 2010
CountryEgypt
CityCairo
Period11/04/1013/04/10

ASJC Scopus subject areas

  • Theoretical Computer Science
  • Computer Science(all)

Fingerprint Dive into the research topics of 'Different Regions Identification in Composite Strain-Encoded (C-SENC) Images Using Machine Learning Techniques'. Together they form a unique fingerprint.

  • Cite this

    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. ANNPR 2010 (pp. 231-240). (Lecture Notes in Computer Science; Vol. 5998). Springer. https://doi.org/10.1007/978-3-642-12159-3_21