27 October 2006 An image segmentation method based on two-dimensional entropy and variance
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Proceedings Volume 6047, Fourth International Conference on Photonics and Imaging in Biology and Medicine; 60471B (2006) https://doi.org/10.1117/12.710900
Event: Fourth International Conference on Photonics and Imaging in Biology and Medicine, 2005, Tianjin, China
Abstract
In this paper, we present a new image segmentation algorithm based on the concept of two-dimensional Renyi's entropy along with statistical variance from the assumed data sets of object and the background to produce the appropriate threshold. So the statistic infonnation, or relative spatial distribution, or co-occurrence, of pixel grey levels, was taken into account. Experimental results show that the method we proposed performed better than one-dimensional and two-dimensional entropy-based methods with lower segmentation errors, and a reduction in the amount of noise present in the resultant images. This method can be extended to any other entropy segmentation method based on two-dimensional gray histogram and may also be useful for pattern recognition and image sequence analysis. Especially when the gray value of the object and the background overlap greatly or there is big noises in the image, the segmentation result can be drastically improved.
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Juntao Xue, Juntao Xue, Zhengguang Liu, Zhengguang Liu, Xiuge Che, Xiuge Che, } "An image segmentation method based on two-dimensional entropy and variance", Proc. SPIE 6047, Fourth International Conference on Photonics and Imaging in Biology and Medicine, 60471B (27 October 2006); doi: 10.1117/12.710900; https://doi.org/10.1117/12.710900
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