Non-parametric iterative model constraint graph min-cut for automatic kidney segmentation

Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

Volumn 6363 LNCS, Issue PART 3, 2010, Pages 73-80

  Freiman, M ^a   Kronman, A ^a   Esses, S J ^b,c   Joskowicz, L ^a   Sosna, J ^c  

^a HEBREW UNIVERSITY OF JERUSALEM   (Israel)

^b MOUNT SINAI SCHOOL OF MEDICINE   (United States)

^c HADASSAH HEBREW UNIVERSITY MEDICAL CENTER   (Israel)

Author keywords

[No Author keywords available]

Indexed keywords

CONTRAST AGENT; CT IMAGE; DATA SETS; ENERGY FUNCTIONALS; EXPECTATION-MAXIMIZATION APPROACHES; IMAGE INFORMATION; INTENSITY MODELS; ITERATIVE MODEL; KIDNEY SEGMENTATION; LATENT MODELS; LATENT VARIABLE; MANUAL SEGMENTATION; MARKOV RANDOM FIELDS; MAXIMUM A POSTERIORI ESTIMATION; MIN-CUT; MIN-CUT ALGORITHM; MODEL-DRIVEN; NON-PARAMETRIC; NON-PARAMETRIC MODEL; REGISTRATION ERROR; SYMMETRIC SURFACES;

CHANNEL ESTIMATION; COMPUTERIZED TOMOGRAPHY; MEDICAL COMPUTING; MEDICAL IMAGING; MODELS; OPTIMIZATION;

IMAGE SEGMENTATION;

ALGORITHM; ARTICLE; AUDIOVISUAL EQUIPMENT; AUTOMATED PATTERN RECOGNITION; COMPUTER ASSISTED DIAGNOSIS; COMPUTER ASSISTED TOMOGRAPHY; HUMAN; IMAGE QUALITY; KIDNEY; METHODOLOGY; RADIOGRAPHY; REPRODUCIBILITY; SENSITIVITY AND SPECIFICITY;

ALGORITHMS; HUMANS; KIDNEY; MODELS, ANATOMIC; PATTERN RECOGNITION, AUTOMATED; RADIOGRAPHIC IMAGE ENHANCEMENT; RADIOGRAPHIC IMAGE INTERPRETATION, COMPUTER-ASSISTED; REPRODUCIBILITY OF RESULTS; SENSITIVITY AND SPECIFICITY; TOMOGRAPHY, X-RAY COMPUTED;

EID: 84860730570     PISSN: 03029743     EISSN: 16113349     Source Type: Book Series    
DOI: 10.1007/978-3-642-15711-0_10     Document Type: Conference Paper

References (17)

1. Herts, B.R., et al.: Estimating glomerular filtration rate in kidney donors: a model constructed with renal volume measurements from donor CT scans. Radiology 252(1), 109-116 (2009)
2. Rao, M., et al.: Comparison of human and automatic segmentations of kidneys from CT images. Int. J. Rad. Onc. Bio. Phys. 61(3), 954-960 (2005)
3. Hidas, G., et al.: Estimating relative renal function from relative parenchymal volume-a feasibility study. J. Endourol. 22(11), 2527-2530 (2008)
4. Boykov, Y., Funka-Lea, G.: Graph Cuts and Efficient N-D Image Segmentation. Int. J. of Comp. Vis. 70(2), 109-131 (2006) (Pubitemid 44127252)
5. Shim, H., et al.: Semiautomated segmentation of kidney from high-resolution multidetector computed tomography images using a graph-cuts technique. J. Comput. Assist. Tomogr. 33(6), 893-901 (2009)
6. Slabaugh, G., Unal, G.: Graph cuts segmentation using an elliptical shape prior. In: IEEE ICIP 2005, vol. 2, pp. 1222-1225 (2005)
7. Freedman, D., Zhang, T.: Interactive graph cut based segmentation with shape priors. In: IEEE CVPR 2005, vol. 1, pp. 755-762 (2005)
8. Ali, A., Farag, A., El-Baz, A.: Graph Cuts Framework for Kidney Segmentation with Prior Shape Constraints. In: Ayache, N., Ourselin, S., Maeder, A. (eds.) MICCAI 2007, Part I. LNCS, vol. 4791, pp. 384-392. Springer, Heidelberg (2007)
9. Freiman, M., et al.: Vessels-Cut: a graph based approach to carotid arteries patient-specific modeling. In: Magnenat-Thalmann, N. (ed.) Modelling the Physiological Human. LNCS, vol. 5903, pp. 1-12. Springer, Heidelberg (2009)
10. Kumar, M., Torr, P., Zisserman, A.: OBJ CUT. In: IEEE CVPR 2005, pp. 18-25 (2005)
11. Malcolm, J., Rathi, Y., Tannenbaum, A.: Graph Cut Segmentation with Nonlinear Shape Priors. In: IEEE ICIP 2007, pp. 365-368 (2007)
12. Sabuncu, M.R., et al.: Supervised Nonparametric Image Parcellation. In: Yang, G.-Z., Hawkes, D., Rueckert, D., Noble, A., Taylor, C. (eds.) MICCAI 2009. LNCS, vol. 5762, pp. 1075-1083. Springer, Heidelberg (2009)
13. Wimmer, A., Soza, G., Hornegger, J.: A Generic Probabilistic Active Shape Model for Organ Segmentation. In: Yang, G.-Z., Hawkes, D., Rueckert, D., Noble, A., Taylor, C. (eds.) MICCAI 2009. LNCS, vol. 5762, pp. 26-33. Springer, Heidelberg (2009)
14. Klein, S., et al.: elastix: a toolbox for intensity based medical image registration. IEEE Trans. Med. Imaging 29(1), 196-205 (2010), http://elastix.isi.uu.nl/
15. Barak, A., Shiloh, A.: The MOSIX2 Management System for Linux Clusters, Multi-Clusters and Clouds, http://www.mosix.org/pub/MOSIX2-wp.pdf
16. Commowick, O., Malandain, G.: Efficient Selection of the Most Similar Image in a Database for Critical Structures Segmentation. In: Ayache, N., Ourselin, S., Maeder, A. (eds.) MICCAI 2007, Part II. LNCS, vol. 4792, pp. 203-210. Springer, Heidelberg (2007)
17. Warfield, S., Zou, K., Wells, W.: Simultaneous truth and performance level estimation (STAPLE): an algorithm for the validation of image segmentation. IEEE Trans. Med. Imaging. 23(7), 903-921 (2004)