Delayed-enhancement magnetic resonance imaging (DE-MRI) is an effective technique for imaging left ventricular (LV) infarct. Existing techniques for LV infarct segmentation are primarily threshold-based making them prone to high user variability. In this work, we propose a segmentation algorithm that can learn from training images and segment based on this training model. This is implemented as a Markov random field (MRF) based energy formulation solved using graph-cuts. A good agreement was found with the Full-Width-at-Half-Maximum (FWHM) technique.
|Title of host publication||Statistical Atlases and Computational Models of the Heart. Imaging and Modelling Challenges|
|Subtitle of host publication||STACOM 2012|
|Editors||Oscar Camara, Tommaso Mansi, Mihaela Pop, Kawal Rhode, Maxime Sermesant, Alistair Young|
|Place of Publication||Berlin, Heidelberg|
|Number of pages||9|
|Publication status||Published - Apr 2013|
|Name||Lecture Notes in Computer Science|