Different Regions Identification in Composite Strain-Encoded (C-SENC) Images Using Machine Learning Techniques

  • Abdallah G. Motaal
  • Neamat El-Gayar
  • Nael F. Osman
Part of the Lecture Notes in Computer Science book series (LNCS, volume 5998)

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.

Keywords

Support Vector Machine Radial Basis Function Confusion Matrix Region Identification Slice Profile 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2010

Authors and Affiliations

  • Abdallah G. Motaal
    • 1
  • Neamat El-Gayar
    • 1
    • 2
  • Nael F. Osman
    • 1
    • 3
  1. 1.Center for Informatics SciencesNile UniversityEgypt
  2. 2.Faculty of Computers and InformationCairo UniversityGizaEgypt
  3. 3.Radiology Department, School of MedicineJohns Hopkins UniversityUSA

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