STACS: new active contour scheme for cardiac MR image segmentation.
The paper presents a novel stochastic active contour scheme (STACS) for automatic image segmentation designed to overcome some of the unique challenges in cardiac MR images such as problems with low contrast, papillary muscles, and turbulent blood flow. STACS minimizes an energy functional that combines stochastic region-based and edge-based information with shape priors of the heart and local properties of the contour. The minimization algorithm solves, by the level set method, the Euler-Lagrange equation that describes the contour evolution. STACS includes an annealing schedule that balances dynamically the weight of the different terms in the energy functional. Three particularly attractive features of STACS are: 1) ability to segment images with low texture contrast by modeling stochastically the image textures; 2) robustness to initial contour and noise because of the utilization of both edge and region-based information; 3) ability to segment the heart from the chest wall and the undesired papillary muscles due to inclusion of heart shape priors. Application of STACS to a set of 48 real cardiac MR images shows that it can successfully segment the heart from its surroundings such as the chest wall and the heart structures (the left and right ventricles and the epicardium.) We compare STACS' automatically generated contours with manually-traced contours, or the "gold standard," using both area and edge similarity measures. This assessment demonstrates very good and consistent segmentation performance of STACS.
Pluempitiwiriyawej
Chamchai
C
Department of Electrical Engineering, Chulalongkorn University, Pratumwan, Bangkok, Thailand 10330. Charnchai.P@eng.chula.ac.th
Moura
José M F
JM
Fellow
Wu
Yi-Jen Lin
YJ
Ho
Chien
C
D000465:Algorithms; D000818:Animals; D001185:Artificial Intelligence; D003198:Computer Simulation; D006352:Heart Ventricles / Q000033:anatomy & histology*; D007089:Image Enhancement / Q000379:methods; D007090:Image Interpretation, Computer-Assisted / Q000379:methods*; D021621:Imaging, Three-Dimensional / Q000379:methods*; D019028:Magnetic Resonance Imaging, Cine / Q000379:methods*; D008954:Models, Biological; D015233:Models, Statistical; D010363:Pattern Recognition, Automated / Q000379:methods*; D051381:Rats; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity; D013382:Subtraction Technique