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

  • Authors:

  • Abdallah G. Motaal;Neamat El-Gayar;Nael F. Osman

  • Affiliations:

  • Center for Informatics Sciences, Nile University, Egypt;Center for Informatics Sciences, Nile University, Egypt;Center for Informatics Sciences, Nile University, Egypt

  • Venue:
  • ANNPR'10 Proceedings of the 4th IAPR TC3 conference on Artificial Neural Networks in Pattern Recognition
  • Year:
  • 2010

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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.