A new framework for automated segmentation of left ventricle wall from contrast enhanced cardiac magnetic resonance images

Ahmed Elnakib, Garth M. Beache, Georgy Gimel'farb, Ayman El-Baz

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

4 Scopus citations

Abstract

A novel automated framework for the segmentation of the left ventricle (LV) wall from contrast enhanced cardiac magnetic resonance images (CE-CMRI) is proposed. The framework consists of two main steps. First, the inner cavity of the LV is segmented from the surrounding tissues based on finding the Maximum A Posteriori (MAP) estimation of a new energy function using a graph-cuts-based optimization algorithm. The proposed energy function consists of three descriptors: 1 st-order visual appearance descriptors of the CE-CMRI, a 2D spatially rotation-variant 2 nd-order homogeneity descriptor, and a LV inner cavity shape descriptor. Second, the outer contour of the LV is segmented by generating an orthogonal wave, starting from the LV inner contour, by solving an Eikonal partial differential equation with a new speed function that combines the prior shape and current visual appearance models of the LV wall. The proposed framework was tested on in-vivo CE-CMR images and validated with manual expert delineations of left ventricle borders. Experiments and comparison results on real CE-CMR images confirm the robustness and accuracy of the proposed framework over the existing ones.

Original languageEnglish (US)
Title of host publicationICIP 2011
Subtitle of host publication2011 18th IEEE International Conference on Image Processing
Pages2289-2292
Number of pages4
DOIs
StatePublished - 2011
Event2011 18th IEEE International Conference on Image Processing, ICIP 2011 - Brussels, Belgium
Duration: Sep 11 2011Sep 14 2011

Publication series

NameProceedings - International Conference on Image Processing, ICIP
ISSN (Print)1522-4880

Other

Other2011 18th IEEE International Conference on Image Processing, ICIP 2011
Country/TerritoryBelgium
CityBrussels
Period9/11/119/14/11

All Science Journal Classification (ASJC) codes

  • Software
  • Computer Vision and Pattern Recognition
  • Signal Processing

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