A Novel Active Contour Model for MRI Brain Segmentation used in Radiotherapy Treatment Planning
Keywords:
image segmentation; active contour model; intensity inhomogeneity; energy function; brain MRIAbstract
Introduction: Brain image segmentation is one of the most important clinical tools used in radiology and radiotherapy. But accurate segmentation is a very difficult task because these images mostly contain noise, inhomogeneities, and sometimes aberrations. The purpose of this study was to introduce a novel, locally statistical active contour model (ACM) for magnetic resonance image segmentation in the presence of intense inhomogeneity with the ability to determine the position of contour and energy diagram.
Methods: A Gaussian distribution model with different means and variances was used for inhomogeneity, and a moving window was used to map the original image into another domain in which the intensity distributions of inhomogeneous objects were still Gaussian but were better separated. The means of the Gaussian distributions in the transformed domain can be adaptively estimated by multiplying a bias field by the original signal within the window. Then, a statistical energy function is defined for each local region. Also, to evaluate the performance of our method, experiments were conducted on MR images of the brain for segment tumors or normal tissue as visualization and energy functions.
Results: In the proposed method, we were able to determine the size and position of the initial contour and to count iterations to have a better segmentation. The energy function for 20 to 430 iterations was calculated. The energy function was reduced by about 5and 7% after 70 and 430 iterations, respectively. These results showed that, with increasing iterations, the energy function decreased, but it decreased faster during the early iterations, after which it decreased slowly. Also, this method enables us to stop the segmentation based on the threshold that we define for the energy equation.
Conclusion: An active contour model based on the energy function is a useful tool for medical image segmentation. The proposed method combined the information about neighboring pixels that belonged to the same class, thereby making it strong to separate the desired objects from the background.
References
Bovik A. Handbook of image and video processing. Second edition. Elsevier Academic Press, 2005.
Tian D, Fan L. A brain MR images segmentation method based on SOM neural network. Bioinformatics
and Biomedical Engineering. 2007; 686-9.doi:10.1109/ICBBE.2007.179.
Llado X, Oliver A, Cabezas M, Freixenet J, Vilanova J.C, Quiles A, et al. Segmentation of multiple
sclerosis lesions in brain MRI: a review of automated approaches. Information Sciences. 2012; 186(1):
-185.doi: 10.1016/j.ins.2011.10.011.
Devi, C.N, Chandrasekharan A, Sundararaman V.K, Alex Z.C. Neonatal brain MRI segmentation: A
review. Journal of computers in biology and medicine. 2015; 64(1): 163-78. doi:
1016/j.compbiomed.2015.06.016, PMID: 26189155
Ji Z, Liu J, Cao G, Sun Q, Chen Q. Robust spatially constrained fuzzy c-means algorithm for brain MR
image segmentation. Pattern Recognition. 2014; 47(7): 2454-66. doi: 10.1016/j.patcog.2014.01.017.
Peng, B, Zhang L, Zhang D. Automatic image segmentation by dynamic region merging. IEEE Trans.
Image Process 2011; 20(12): 3592-605. doi: 10.1109/TIP.2011.2157512.
Talu, M.F. ORACM: Online region-based active contour model. Expert Systems with Applications. 2013;
(16): 6233-40. doi: 10.1016/j.eswa.2013.05.056.
Minati L, Grisoli M, Seth A.K, Critchley H.D. Decision-making under risk: a graph-based network analysis
using functional MRI. Neuroimage. 2012; 60(4): 2191-205. doi: 10.1016/j.neuroimage.2012.02.048,
PMID: 22387471
Lai Z, Qu X, Liu Y, Guo D, Ye J, Zhan Z, et al. Image Reconstruction of Compressed Sensing MRI Using
Graph-based Redundant Wavelet Transform. Medical Image Analysis. 2015; 1-27. doi:
1016/j.media.2015.05.012.
Peng B, Zhang L, Zhang D, Yang J. Image segmentation by iterated region merging with localized graph
cuts. Pattern Recognition. 2011; 44(10): 2527–38. doi: 10.1016/j.patcog.2011.03.024
Egger J, Colen RR, Freisleben B, Nimsky C. Manual refinement system for graph-based segmentation
results in the medical domain. Journal of Medical Systems. 2012; 36(5): 2829-39. doi: 10.1007/s10916-
-9761-7, PMCID: PMC3691109
Kass M, Witkin A, Terzopoulos D. Snakes: active contour models. International Journal of Computer
Vision. 1987; 1(4): 321–31. doi: 10.1007/BF00133570.
Moreno JC, Prasath VBS, Proença H, Palaniappan K. Fast and globally convex multiphase active contours
for brain MRI segmentation. Computer Vision and Image Understanding. 2014; 125: 237-50. doi:
1016/j.cviu.2014.04.010.
Lankton S, Tannenbaum A. Localizing region-based active contours.. IEEE Trans. Image Process 2008;
(11): 2029–39. doi: 10.1109/TIP.2008.2004611.
Zhu S, Yuille A. Region competition: unifying snakes, region growing, and Bayes/MDL for multiband
image segmentation.. IEEE Trans. Pattern Anal. Mach. Intell. 1996; 18(9): 884–900. doi:
1109/34.537343.
Zhang K, Zhang L, Zhang S. A variational multiphase level set approach to simultaneous segmentation and
bias correction. IEEE International Conference on Image Processing. 2010; 4105–08. doi:
1109/ICIP.2010.5651554.
Li C, Huang R, Ding Z, Gatenby C, Metaxas D, Gore JC. A level set method for image segmentation in the
presence of intensity inhomogeneities with application to MRI.. IEEE Trans. Image Process. 2011; 20(7):
–16. doi: 10.1109/TIP.2011.2146190.
Chan T, Vese L. Active contours without edges.. IEEE Trans. Image Process. 2001; 10(2): 266–77. doi:
1109/83.902291.
Paul T.U, Bandhyopadhyay SK. Segmentation of Brain Tumor from Brain MRI Images Reintroducing K–
Means with advanced Dual Localization Method. International Journal of Engineering Research and
Applications. 2012; 2(3): 226-31.
Zhang K, Zhang L, Lam KM, Zhang D. A locally statistical active contour model for image segmentation
with intensity inhomogeneity. Computer Vision and Pattern Recognition. 2013. arXiv preprint.
arXiv:1305.7053.
Gibou F, Fedkiw R, Eric FE. A fast hybrid k-means level set algorithm for segmentation. In 4th Annual
Hawaii International Conference on Statistics and Mathematics. 2005; 281-291. Honolulu, Hawaii.
Mumford D, Shah J. Optimal approximation by piecewise smooth function and associated variational
problems. Communications on Pure Applied Mathematics. 1989; 42(5): 577–685. doi:
1002/cpa.3160420503.
Vese L, Chan T. A multiphase level set framework for image segmentation using the mumford and shah
model. International Journal of Computer Vision. 2002; 50(3): 271–293. doi: 10.1023/A:1020874308076.
Wang Y, Lei T. Statistical analysis of MR imaging and its applications in image modeling. IEEE
International Conference of Image Processing and Neural Networks. 1994; 1: 866–70. doi:
1109/ICIP.1994.413438.
Brox T. From pixels to regions: partial differential equations in image analysis, Ph.D Thesis, Saarland
University, Germany, 2005.
Duda R, Hart P., Stork D, Pattern classification. John Wiley & Sons Inc press, 2nd edition, 2001.
Knutsson H, Westin C. Normalized and differential convolution: methods for interpolation and filtering of
incomplete and uncertain data. IEEE Conference on Computer Vision and Pattern Recognition. 1993; 515–
doi: 10.1109/CVPR.1993.341081.
Zhang K, Song H, Zhang L. Active contours driven by local image fitting energy. Pattern Recognition.
; 43(4): 1199–206. doi: 10.1016/j.patcog.2009.10.010.
Gonzalez R, Woods R, Digital image processing. Third edition, Prentice Hall, 2008.
Tsai A, Yezzi J A , Willsky AS, Curve evolution implementation of the Mumford-Shah functional for
image segmentation, denoising, interpolation, and magnification.. IEEE Trans. Image Process. 2001; 10(8):
-1186. doi: 10.1109/83.935033.
Darolti C, Mertins A, Bodensteiner C, Hofmann UG. Local region descriptors for active contours
evolution.. IEEE Trans. Image Process. 2008; 17(12): 2275-88. doi: 10.1109/TIP.2008.2006443.
Li C, Chiu-Yen K, Gore JC, Ding Z. Minimization of region-scalable fitting energy for image
segmentation.. IEEE Trans. Image Process. 2008; 17(10): 1940-9. doi: 10.1109/TIP.2008.2002304.
Li C, Chiu-Yen K, Gore JC, Ding Z. Implicit active contours driven by local binary fitting energy. IEEE
Conference on Computer Vision and Pattern Recognition. 2007; 1-7. doi: 10.1109/CVPR.2007.383014.
Brox T, Cremers D. On local region models and a statistical interpretation of the piecewise smooth
Mumford-Shah functional. International journal of computer vision. 2009; 84(2): 184-93. doi:
1007/s11263-008-0153-5.
Bishop CM. Pattern Recognition and Machine Learning. Springer-Verlag, New York, 2006.
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