A level set image segmentation method based on a cloud model as the priori contour
A novel image segmentation method combining a cloud model and a level set (CM-LS) is proposed in this article. At present, the cloud model can only obtain the rough segmentation result of an image, but the level set method is sensitive to the initial contour. The core idea of this method is to use the rough segmentation result of cloud model as the initial contour of the level set and then obtain the final result by the contour evolution. In this method, the cloud model is used to decompose the boundary of the image, which reduces the occurrence probability and occurrence degree of the instability problem caused by artificial intervention; at the same time, the convergence of the level set function is accelerated, and the initializing operation of the level set function that uses the cloud model algorithm can also effectively reduce the noise sensitivity of the function itself. Compared with the conventional level set method, the proposed method is general and accurate. The experimental data set in this article includes natural images of the Berkeley database, medical images and synthetic noise images. The experimental results show that the method is effective.
KeywordsLevel set Cloud model Image segmentation Priori contour
This work was supported in part by the Natural Science Foundation of China (61472055, U1713213, U1401252), the National Science & Technology Major Project \((2016YFC1000307-3)\) and the Chongqing Research Program of Application Foundation and Advanced Technology (cstc2014jcyjjq40001).
Compliance with ethical standards
Conflict of interest
The authors declare that they have no conflict of interest.
- 1.Ma, Z., Tavares, J.M.R.S., Natal Jorge, R.M.: A review on the current segmentation algorithms for medical images. In: International Conference on Imaging Theory & Applications, pp. 135–140 (2009)Google Scholar
- 3.Vasconcelos Maria, J.M., Tavares, J.M.R.S.: Methods to automatically build point distribution models for objects like hand palms and faces represented in images. Comput. Model. Eng. Sci. 36, 213–241 (2018)Google Scholar
- 9.Shan, J., Wang, Y., Cheng, H.D.: IEEE International Conference on Image Processing, 1713-1716. IEEE, Hong Kong, China (2010)Google Scholar
- 20.Giga, Y.: A level set method for surface evolution equations. Sugaku Expos. 10, 217–241 (1997)Google Scholar
- 29.Li, C., Kao, C.Y., Gore, J.C., Ding, Z.: Implicit active contours driven by local binary fitting energy. In: IEEE Conference on Computer Vision and Pattern Recognition 2007, pp. 1–7 (2007)Google Scholar
- 30.Li, C., Wang, L., Kao, C., Ding, Z., Gore, J.: Brain MR image segmentation by minimizing scalable neighborhood intensity fitting energy: a multiphase level set approach. In: Proceedings 16th Scientific Meeting, International Society for Magnetic Resonance in Medicine 1, 556 (2008)Google Scholar
- 31.Xu, C., Li, C., Gui, C., Fox, M.D.: 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR’05)(CVPR), pp. 430–436. CVPR, San Diego (2005)Google Scholar
- 32.Li, D., Di, K., Li, D.: Knowledge representation and uncertainty reasoning in GIS based on cloud models. In: Proceedings of the 9th International Symposium on Spatial Data Handling, vol. 3, pp. 3–14 (2000)Google Scholar
- 35.Qin, K., Xu, K., Du, Y., Li, D.: Seventh International Conference on Fuzzy Systems and Knowledge Discovery, pp. 524–528. IEEE, Yantai, China (2010)Google Scholar
- 36.Martin, D.R., Fowlkes, C., Tal, D., Malik, J.: A database of human segmented natural images and its application to evaluating segmentation. In: Proceedings of the international Conference on Computer Vision, vol. 2, pp. 416–423 (2001)Google Scholar
- 39.Dietenbeck, T., Alessandrini, M., Friboulet, D., Bernard, O.: 17th IEEE International Conference on Hong Kong, pp. 665–668. IEEE, Hong Kong, China (2010)Google Scholar