Abstract
A novel level set based image segmentation method is proposed in this paper. After analyzing advantages and drawbacks of SBGFRLS and LSD model, we propose a way to combine local information and global information through utilizing weighted energy generated by SBGFRLS model to reduce segmentation error and accelerate curve evolution. The total energy of proposed method comprises of local energy term, length term and weighted global energy term. In our experiments, two alternative values for the coefficient of global term are 0.1 or −0.1. Experiments on images with noise and intensity inhomogeneities show that the proposed method is effective and more accurate than both SBGFRLS model and LSD model.
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Acknowledgement
This work is supported by National Natural Science Foundation of China under Grant NO. 61305051 and National High-Tech Research and Development Plan of China under Grant No. 2012AA01A306.
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Yang, C., Wu, W., Su, Y. (2014). Level Set Method Based Image Segmentation by Combining Local Information with Global Information. In: Yuan, Y., Wu, X., Lu, Y. (eds) Trustworthy Computing and Services. ISCTCS 2013. Communications in Computer and Information Science, vol 426. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-662-43908-1_43
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DOI: https://doi.org/10.1007/978-3-662-43908-1_43
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