title: Endocardial border detection in cardiac magnetic resonance images using level set method.
abstract: Segmentation of the left ventricle in MRI images is a task with important diagnostic power. Currently, the evaluation of cardiac function involves the global measurement of volumes and ejection fraction. This evaluation requires the segmentation of the left ventricle contour. In this paper, we propose a new method for automatic detection of the endocardial border in cardiac magnetic resonance images, by using a level set segmentation-based approach. To initialize this level set segmentation algorithm, we propose to threshold the original image and to use the binary image obtained as initial mask for the level set segmentation method. For the localization of the left ventricular cavity, used to pose the initial binary mask, we propose an automatic approach to detect this spatial position by the evaluation of a metric indicating object's roundness. The segmentation process starts by the initialization of the level set algorithm and ended up through a level set segmentation. The validation process is achieved by comparing the segmentation results, obtained by the automated proposed segmentation process, to manual contours traced by tow experts. The database used was containing one automated and two manual segmentations for each sequence of images. This comparison showed good results with an overall average similarity area of 97.89%.
authors.affiliation: Biomedical Engineering Laboratory, University of Tlemcen Algeria, Tlemcen, Algeria. ammar.mohammed4@gmail.com
authors.forename: Mohammed
authors.initials: M
authors.lastname: Ammar
authors.forename: Saïd
authors.initials: S
authors.lastname: Mahmoudi
authors.forename: Mohammed Amine
authors.initials: MA
authors.lastname: Chikh
authors.forename: Amine
authors.initials: A
authors.lastname: Abbou
mesh_terms: D000465:Algorithms; D004699:Endocardium / Q000033:anatomy & histology*; D006352:Heart Ventricles / Q000033:anatomy & histology*; D006801:Humans; D007089:Image Enhancement / Q000379:methods*; D008279:Magnetic Resonance Imaging / Q000379:methods*; D010363:Pattern Recognition, Automated / Q000379:methods*