基于改进<i>k</i>均值与高斯混合模型的宫颈图像分割

doi:10.3969/j.issn.0258-8021.2018.02.002

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Abstract In order to segment the cervix region from colposcope images in the intelligent cervix cancer screening system, a kind of method based on modified k means algorithm and Gaussian mixture model was developed in this paper. Firstly, the data set to be classified was constructed according to the representative color of the cervix region and the distance to the image center point. Secondly, by recalculating the representative color of the cervix region, a dynamic regulation which enable the data set to be classified keep updating with the iteration was added in the k means algorithm, thus the k means algorithm can be applied to target images that obtained under different light environment. Lastly, the segmentation result was obtained by Gaussian mixture model that initialized by the result of the modified k means algorithm. 75 sets of cervical images that photographed under different conditions were used in the experiments. The results on these data showed that developed method gained a mean accuracy of 65.1%, which is 5.5%,5.8% and 8.5% higher comparing with the k means initialized Gaussian mixture model algorithm, fuzzy C means algorithm and basic Gaussian mixture model algorithm separately. It also gained a standard deviation of 11.5%, which is 5.6% lower comparing with level set algorithm. The results in these experiments proved the effectivity of the developed method in the cervix region segmentation from colposcope images.

Key words： cervix cancer screening cervical image segmentation k means algorithm Gaussian mixture model

Received: 05 May 2017

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R318

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	Articles by authors
	Liu Jun
	Yu Tingting
	Shi Huijuan

Cite this article:

Liu Jun,Yu Tingting,Shi Huijuan. Cervical Image Segmentation Based on Modified k Means Algorithm and Gaussian Mixture Model[J]. Chinese Journal of Biomedical Engineering, 2018, 37(2): 138-145.

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http://cjbme.csbme.org/EN/10.3969/j.issn.0258-8021.2018.02.002 OR http://cjbme.csbme.org/EN/Y2018/V37/I2/138

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