Lung CT Image Segmentation Based on Combined Multi-Scales Watershed Method and Region Growing Method

(*) Corresponding author

Authors' affiliations

DOI's assignment:
the author of the article can submit here a request for assignment of a DOI number to this resource!
Cost of the service: euros 10,00 (for a DOI)


In the field of computer assisted diagnostic, Image segmentation is one of the most important preprocessing steps. In this paper, the watershed algorithm segmented image by choosing different merging parameters, original image will be divided into different scales of image, High-scale image and low-scale image. High-scale image is part of the pulmonary parenchyma without the main vessel in it, low-scale image used to aid region growing tore move the branching vessels from the pulmonary parenchyma. Selected seed point located inside the branch vessels and determined the region growing threshold, with the aid of low-scale image region growing can segment the branch vessel regions .Removed the regions in the high-scale image to get the final segmentation results. In this paper, a large number of CT lung data segmentation experiment. Proven, good segmentation results are achieved.
Copyright © 2013 Praise Worthy Prize - All rights reserved.


Image Segmentation; Lung Segmentation; Multi Scales; Region Growing; Watershed

Full Text:



L. Yao, T. Jie. Medical image segmentation techniques, PR&AI, Vol. 15, n. 2, pp. 224-232, 2002.

L. M. Ping, B. S. Su. Medical image segmentation algorithm research, Ph.D. Thesis, Dept. Computer Engineering, South China Normal University,GuangZhou,China,2010.

Xu, G., Xie, S., Yin, Y., Zhou, M., Zhang, S., A fast edge detection algorithm based on cellular neural networks for road images, (2012) International Review on Computers and Software (IRECOS), 7 (1), pp. 426-431.

L. Yan, F. X.Ping,L.Gang. A New Algorithm of Image Threshold Segmentation, Computer emulation, Vol. 23, n. 6, pp. 196-197, 2006.

J. P. Fan, G. H. Zeng, B . Mathurin, et al. Seeded region growing: an extensive and comparative study, Pattern Recognition Letters, Vol. 26, n. 2005, pp. 1139–1156, 2005.

L. J. Feng, L. Hai, P. Z. Geng. Adaptive Region Growing Algorithm in Medical Images Segmentation, JOURNAL OFCOMPUTER—AIDEDDESIGN&COMPUTERGRAPHICS, Vol. 17, n. 10, pp. 2168-2073, 2005.

Zhao, Y., Xu, X., Wang, B., Bai, X., Two-dimensional fuzzy entropy image segmentation based on adaptive CPSO algorithm, (2012) International Review on Computers and Software (IRECOS), 7 (4), pp. 1767-1772.

C. C. Kang, W.J. Wang, C.H. Kang. Image segmentation with complicated background by using seeded region growing,Int. J. Electron. Commun, Vol. 66, n. 2012, pp. 767-771, 2012.

S. Beucher, C. Lantuejoul. Use of watersheds in contour detection, International workshop on image process in :Real-time Edge and Motion detection/estimation, pp,17-21,1979.

J. BTM, Roerdink, M. Arnold. The Watershed Transform: Definitions, Algorithms and Parallelization Strategies, Fundamenta Informaticae, Vol. 41, n. 2001, pp. 187-228, 2001.

M. Frucci, G. Ramella, G. S. D. Baja. Using resolution pyramids for watershed image segmentation, Image and Vision Computing, Vol. 25, n. 2007, pp. 1021-1031, 2007.

H. Ghassan, X. X. Li. Watershed segmentation using prior shape and appearance knowledge, Image and Vision Computing, Vol. 27, n. 2009, pp. 59-68, 2009.

C. R. Jung. Combining wavelets and watersheds for robust multi-scale image segmentation, Image and Vision Computing, Vol. 25, n. 2007, pp. 24-33, 2007.

B. Alberto, D. Ivan, A. Silvano, et al. An automatic method for colon segmentation in CT colonography, Computerized Medical Imaging and Graphics, Vol. 33, n. 2009, pp. 325-331, 2009.

P. F. Ping, B. S. Su, Z. B. Qing. Segmentation of liver based on adaptive region growing, Computer Engineering and Applications, Vol. 46, n. 33, pp. 198 – 200, 2010.

Z. X. Rong, H. Tatsuro, H. Takeshi, et al. Automatic segmentation and recognition of anatomical lung structures from high-resolution chest CT images, Computerized Medical Imaging and Graphics, Vol. 30, n. 2006, pp. 299-313, 2006.

P. J. Tao, R. Justus, A. Y. Chin, et al. Adaptive border marching algorithm: Automatic lung segmentation on chest CT images, Computerized Medical Imaging and Graphics, Vol. 32, n. 2008, pp. 452-462, 2008.

Y. Yeny, H. Helen, B.S. Joon, et al. Correction of lung boundary using t he gradient and intensity distribution, Computers in Biology a nd Medicine, Vol. 39, n. 2009, pp. 239-250, 2009.


  • There are currently no refbacks.

Please send any question about this web site to
Copyright © 2005-2024 Praise Worthy Prize