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

Abdallah G. Motaal, Neamat El-Gayar, Nael Fakhry 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 publicationLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Pages231-240
Number of pages10
Volume5998 LNAI
DOIs
StatePublished - 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)03029743
ISSN (Electronic)16113349

Other

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

Fingerprint

Learning systems
Machine Learning
Composite
Tissue
Composite materials
Decision making
Imaging techniques
Encoding
Decision Making
Imaging
Magnetic resonance imaging
Muscle
Contractility
Identification (control systems)
Myocardial Infarction
Viability
Cardiac
Computer simulation
High Accuracy
Enhancement

ASJC Scopus subject areas

  • Computer Science(all)
  • Theoretical Computer Science

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 Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 5998 LNAI, 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

Different regions identification in composite strain-encoded (C-SENC) images using machine learning techniques. / Motaal, Abdallah G.; El-Gayar, Neamat; Osman, Nael Fakhry.

Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). Vol. 5998 LNAI 2010. p. 231-240 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 5998 LNAI).

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

Motaal, AG, El-Gayar, N & Osman, NF 2010, Different regions identification in composite strain-encoded (C-SENC) images using machine learning techniques. in Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). vol. 5998 LNAI, Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 5998 LNAI, pp. 231-240, 4th IAPR TC3 Workshop on Artificial Neural Networks in Pattern Recognition, ANNPR 2010, Cairo, Egypt, 4/11/10. https://doi.org/10.1007/978-3-642-12159-3_21
Motaal AG, El-Gayar N, Osman NF. Different regions identification in composite strain-encoded (C-SENC) images using machine learning techniques. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). Vol. 5998 LNAI. 2010. p. 231-240. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)). https://doi.org/10.1007/978-3-642-12159-3_21
Motaal, Abdallah G. ; El-Gayar, Neamat ; Osman, Nael Fakhry. / Different regions identification in composite strain-encoded (C-SENC) images using machine learning techniques. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). Vol. 5998 LNAI 2010. pp. 231-240 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).
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