Open AccessGraph cuts segmentation based on multi‐dimensional features for the right ventricle in cardiac MRI
Author(s) -
Lu Xuesong,
Ma Shuang,
Zhao Danqing,
Liu Liman
Publication year - 2017
Publication title -
the journal of engineering
Language(s) - English
Resource type - Journals
ISSN - 2051-3305
DOI - 10.1049/joe.2016.0288
Subject(s) - segmentation , hausdorff distance , artificial intelligence , pattern recognition (psychology) , ventricle , histogram , computer vision , computer science , graph , image segmentation , mathematics , medicine , image (mathematics) , combinatorics , cardiology
The segmentation of the right ventricle in cardiac magnetic resonance imaging is a difficult task due to the blurred border and variable shape. To overcome these problems, an approach using graph cuts (GC) segmentation based on multi‐dimensional local features is proposed. The features are reduced by a principal component analysis, which are incorporated into the t‐link and the n‐link. Experiments are performed on 240 MR images from 16 patients. The segmentation results of the proposed algorithm are obtained using Gabor and histogram of oriented gradient (HOG) features, respectively. For Dice metric, the median value of using Gabor and HOG increases significantly compared to original GC from 0.8012 to 0.8174 and 0.8306 at end‐diastole (ED), from 0.6555 to 0.7021 and 0.7124 at end‐systole (ES). For Hausdorff distance, the median value of using Gabor and HOG decreases significantly compared to original GC from 12.88 to 12.33 and 11.82 mm at ED, from 17.20 to 12.75 and 13.41 mm at ES. The proposed method outperforms original GC depending on image intensity only. The segmentation errors of using HOG are slightly less than those of using Gabor.