naa a22 4500 510807267 CHVBK 20180411083424.0 cr unu---uuuuu 180411e20130501xx s 000 0 eng 10.1007/s11548-012-0787-1 doi (NATIONALLICENCE)springer-10.1007/s11548-012-0787-1 4D statistical shape modeling of the left ventricle in cardiac MR images [Elektronische Daten] [Shahrooz Faghih Roohi, Reza Aghaeizadeh Zoroofi] Purpose: Statistical shape models have shown improved reliability and consistency in cardiac image segmentation. They incorporate a sufficient amount of a priori knowledge from the training datasets and solve some major problems such as noise and image artifacts or partial volume effect. In this paper, we construct a 4D statistical model of the left ventricle using human cardiac short-axis MR images. Methods: Kernel PCA is utilized to explore the nonlinear variation of a population. The distribution of the landmarks is divided into the inter- and intra-subject subspaces. We compare the result of Kernel PCA with linear PCA and ICA for each of these subspaces. The initial atlas in natural coordinate system is built for the end-diastolic frame. The landmarks extracted from it are propagated to all frames of all datasets. We apply the 4D KPCA-based ASM for segmentation of all phases of a cardiac cycle and compare it with the conventional ASM. Results: The proposed statistical model is evaluated by calculating the compactness capacity, specificity and generalization ability measures. We investigate the behavior of the nonlinear model for different values of the kernel parameter. The results show that the model built by KPCA is less compact than PCA but more compact than ICA. Although for a constant number of modes the reconstruction error is a little higher for the KPCA-based statistical model, it produces a statistical model with substantially better specificity than PCA- and ICA-based models. Conclusion: Quantitative analysis of the results demonstrates that our method improves the segmentation accuracy. CARS, 2012 Cardiac models nationallicence 4D statistical shape models nationallicence Kernel PCA nationallicence Cardiac segmentation nationallicence Faghih Roohi Shahrooz University of Tehran, Tehran, Iran aut Aghaeizadeh Zoroofi Reza University of Tehran, Tehran, Iran aut International Journal of Computer Assisted Radiology and Surgery Springer-Verlag 8/3(2013-05-01), 335-351 1861-6410 8:3<335 2013 8 11548 https://doi.org/10.1007/s11548-012-0787-1 text/html Onlinezugriff via DOI 1 research-article jats NATIONALLICENCE 856 40 https://doi.org/10.1007/s11548-012-0787-1 text/html Onlinezugriff via DOI NATIONALLICENCE 700 1- Faghih Roohi Shahrooz University of Tehran, Tehran, Iran aut NATIONALLICENCE 700 1- Aghaeizadeh Zoroofi Reza University of Tehran, Tehran, Iran aut NATIONALLICENCE 773 0- International Journal of Computer Assisted Radiology and Surgery Springer-Verlag 8/3(2013-05-01), 335-351 1861-6410 8:3<335 2013 8 11548 Metadata rights reserved Springer special CC-BY-NC licence nationallicence BK010053 XK010053 XK010000 NATIONALLICENCE NATIONALLICENCE NL-springer