AN AUTOMATIC SEGMENTATION OF LIVER COLUME THAT CONTAINED DISCONNECTED REGION WITH DYNAMIC LOCAL NEIGHBOURHOOD REGION SIZES
Abstract
Segmentation of liver images containing disconnected regions has always been an overlooked problem. Previous works on liver segmentation either ignore this problem or use manual initialization when facing these disconnected regions. Therefore, in this paper we propose a liver level set (LLS) algorithm which is able to segment disconnected regions automatically. The LLS algorithm is based on level set framework together with hybrid energy minimization as the stopping function. By using the LLS algorithm in a looping manner, we allow the current liver boundary to inherit the topological changes from previous images in a 2.5D environment. We also conduct an experiment to obtain an average factor for dynamic localization region sizes based on liver anatomy to improve the segmentation accuracy. These dynamic localization region sizes ensure a more accurate segmentation when compared with manual segmentation. Our experiment gives a respective segmentation result with dice similarity coefficient (DSC) percentage of 87.5%. Plus, our LLS algorithm is able to segment all connected and disconnected liver region automatically and accurately.References
Areste R., Yongyi Y. and Jiang H (2005). An Image Enhancement Procedure for 3D Visualization of Liver CT Data. In Image Analysis and Interpretation, Proceedings of IEEE Southwest, Denver.
Bae K.T., Giger M.L., Chen C.T. and Kahn C.E. Jr. (1993). Automatic segmentation of liver structure in CT images. Journal of Medical Physics. 20(1):71-78.
https://doi.org/10.1118/1.597064
Gao L., Heath, D.G. and Fishman, E.K. (1998). Abdominal image segmentation using three-dimensional deformable models. Journal of Investigative Radiology. 33 (6):348-355.
https://doi.org/10.1097/00004424-199806000-00006
Gao L., Heath D.G., Kyszyk B.S. and Gishman E.K. (1996). Automatic liver segmentation technique for three-dimensional visualization of CT data. Journal of Radiology. 201: 359-364.
https://doi.org/10.1148/radiology.201.2.8888223
Kass M., Witkin A. and Terzopoulos D. (1988). Snakes: active contour models. Journal of Computer Vision.1(4): 321-331.
https://doi.org/10.1007/BF00133570
Kobashi M. and Shapiro L. (1995). Knowledge-based organ identification from CT images. Journal of Pattern Recognition. 28(4):475-491.
https://doi.org/10.1016/0031-3203(94)00124-5
Lamecker H., Lange T. and Seebass M. (2004). Segmentation of the liver using a 3D statistical shape model; ZIBReport 04-09, Berlin.
Lankton S., Delphine N., Anthony Y., and Allen T. (2007) Hybrid Geodesic Region-Based Curve Evolutions for Image Segmentation. In Proceedings of SPIE Medical Imaging.
https://doi.org/10.1117/12.709700
Lee J., Kim N., Seo J.B., W H.J., Shin Y.M. and Shin Y.G. (2007). Efficient liver segmentation exploiting level set speed images with 2.5D shape propagation. In: 3D Segmentation in the Clinic: A grand Challenge, pp. 189-196.
Lim S.J., Jeong Y.Y., Lee C.W. and Ho Y.S (2004) Automatic segmentation of the liver in CT images using the watershed algorithm based on morphological filtering. In Medical Imaging, Proceedings of SPIE.
https://doi.org/10.1117/12.533586
Lim S.J., Jeong Y.Y. and Ho Y.S. (2006). Automatic liver segmentation for volume measurement in CT images. Journal of Visual Communication and Image Representation. 17(4): 860-875.
https://doi.org/10.1016/j.jvcir.2005.07.001
Lim S.J., Jeong Y.Y. and Ho Y.S. (2005) Segmentation of the liver using the deformable contour method on ct images, In Medical Imaging, Proceedings of SPIE.
https://doi.org/10.1007/11581772_50
Liu F., Zhao B, and Kijewski P.K. (2005). Liver segmentation for CT images using gvf snake. Journal of Medical Physic. 32(12): 3699-3706.
https://doi.org/10.1118/1.2132573
Pan S., and Dawant B. M. (2001) Automatic 3D egmentation of the liver from abdominal CT images: a level set approach. Proceeding of SPIE, vol. 4322 pp. 128-138.
https://doi.org/10.1117/12.431019
Pham D.L., Xu, C. and Prince, J.L. (2000). A survey of current methods in medical image segmentation. Journal of Annual Review of Biomedical Engineering. 2:315-337.
https://doi.org/10.1146/annurev.bioeng.2.1.315
Shimizu A., Ohno R., Ikegam, T., Kobatake H., Nawano S. and Smutek D. (2007). Multi-organ segmentation in three dimensional abdominal CT images. Journal of Computer Assisted Radiology and Surgery. 1(7):76-78.
Sulaiman P.S., Rahmat R.W., Mahmod R. and Rashid A.H. (2008) A Liver Level Set (LLS) Algorithm for Extracting Liver's Volume Containing Disconnected Regions Automatically. In IJCSNC Vol. 8 No. 12 pp. 246-252.
Heimann T, van Ginneken B, Styner MA, Arzhaeva Y, Aurich V, Bauer C, Beck A, Becker C, Beichel R, Bekes G, Bello F, Binnig G, Bischof H, Bornik A, Cashman PM, Chi Y, Cordova A, Dawant BM, Fidrich M, Furst JD, Furukawa D, Grenacher L, Hornegger J, Kainmüller D, Kitney RI, Kobatake H, Lamecker H, Lange T, Lee J, Lennon B, Li R, Li S, Meinzer HP, Nemeth G, Raicu DS, Rau AM, van Rikxoort EM, Rousson M, Rusko L, Saddi KA, Schmidt G, Seghers D, Shimizu A, Slagmolen P, Sorantin E, Soza G, Susomboon R, Waite JM, Wimmer A, Wolf I. (2009) Comparison and Evaluation of Methods for Liver Segmentation from CT datasets, IEEE Transactions on Medical Imaging, volume 28, number 8, pp. 1251-1265.
https://doi.org/10.1109/TMI.2009.2013851
Zhou X., Kitagawa T., Okuo K., Fujita H., Yokoyama R., Kanematsu M., and Hoshi H. (2005). Construction of a probabilistic atlas for automated liver segmentation in non-contrast torso CT images. Proceedings of CARS.
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