Cervical Image Segmentation Based on Modified k Means Algorithm and Gaussian Mixture Model
Liu Jun1*, Yu Tingting2, Shi Huijuan2
1Key Laboratory of Jiangxi Province for Image Processing and Pattern Recognition, Nanchang Hangkong University, Nanchang 330063, China 2College of Information Engineering, Nanchang Hangkong University, Nanchang 330063
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.
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.
[1] Chen W, Zheng R, Baade PD, et al. Cancer statistics in China, 2015 [J]. Ca A Cancer Journal for Clinicians, 2016,66(2): 115-132. [2] Pfaendler KS, Wenzel L, Mechanic MB, et al. Cervical cancer survivorship: Long-term quality of life and social support [J]. Clinical Therapeutics, 2015,37(1): 39-48. [3] Torre LA, Siegel RL, Ward EM, et al. Global cancer incidence and mortality rates and trends--an update [J]. Cancer Epidemiology Biomarkers & Prevention, 2015,25(1): 16-27. [4] Goyal S, Tandon P, Bhutani N, et al. To study the role of visual inspection of cervix with acetic acid (via) in cervical cancer screening [J]. International Journal of Reproduction Contraception Obstetrics & Gynecology, 2014,24(1): 684-687. [5] Thekkek N, Richards-Kortum R. Optical imaging for cervical cancer detection: Solutions for a continuing global problem [J]. Nature Reviews Cancer, 2008,8(9): 725-731. [6] Ferris DG. Cervical cancer screening [J]. Journal of Midwifery & Women's Health, 2015,60(2): 229-230. [7] 岑坚敏,钱德英,陈观娣,等. 肉眼醋酸试验在宫颈癌前病变筛查中的价值研究 [J]. 实用医学杂志, 2010, 26 (5): 794-796. [8] 钱翠凤,徐爱娣. 醋酸试验和碘试验在社区妇女宫颈癌筛查中的价值 [J]. 中国妇产科临床杂志,2012, 13 (1): 50-51. [9] Park SY, Sargent D, Lieberman R, et al. Domain-specific image analysis for cervical neoplasia detection based on conditional random fields [J]. IEEE Transactions on Medical Imaging, 2011, 30(3): 867-878. [10] Alush A, Greenspan H, Goldberger J. Automated and interactive lesion detection and segmentation in uterine cervix images [J]. IEEE Transactions on Medical Imaging, 2010, 29(2): 488-501. [11] Jusman Y, Ng SC, Abu Osman NA. Intelligent screening systems for cervical cancer [J]. The Scientific World Journal, 2014,2014(2): 1-15. [12] Rajasekaran R, Aruna PR, Koteeswaran D, et al. Steady-state and time-resolved fluorescence spectroscopic characterization of urine of healthy subjects and cervical cancer patients [J]. Journal of Biomedical Optics, 2014,19(3): 37003-37012. [13] 徐晓敏,刘祖良,王坤东. 宫颈癌荧光图像色彩特征分析与病变判别研究 [J]. 生物医学工程学杂志,2011, 28 (2): 268-272. [14] 张建萍,刘希玉. 基于聚类分析的k-means算法研究及应用 [J]. 计算机应用研究,2007, 24 (5): 166-168. [15] 聂生东,陈瑛,顾顺德,等. 磁共振颅脑图像快速模糊聚类分割算法的研究 [J]. 中国生物医学工程学报,2001, 20(2): 104-109. [16] Zheng Y, Jeon B, Xu D, et al. Image segmentation by generalized hierarchical fuzzy c-means algorithm [J]. Journal of Intelligent & Fuzzy Systems, 2015, 28(2): 4024-4028. [17] Liu HT, Sheu TWH, Chang HH. Automatic segmentation of brain mr images using an adaptive balloon snake model with fuzzy classification [J]. Medical & Biological Engineering & Computing, 2013, 51(10): 1091-1104. [18] Wang L, Pan C. Robust level set image segmentation via a local correntropy-based k-means clustering [J]. Pattern Recognition, 2014. 47(5): 1917-1925. [19] Li W, Venkataraman S, Gustafsson U, et al. Using acetowhite opacity index for detecting cervical intraepithelial neoplasia [J]. Journal of Biomedical Optics, 2009, 14(1): 014020-014030. [20] Greenspan H, Gordon S, Zimmerman G, et al. Automatic detection of anatomical landmarks in uterine cervix images [J]. IEEE Transactions on Medical Imaging, 2009, 28(3): 454-468. [21] Nguyen TM, Wu QMJ. Fast and robust spatially constrained gaussian mixture model for image segmentation [J]. IEEE Transactions on Circuits & Systems for Video Technology, 2013, 23(4): 621-635. [22] 向日华,王润生. 一种基于高斯混合模型的距离图像分割算法 [J]. 软件学报,2003, 14 (7): 1250-1257. [23] Li C, Huang R, Ding Z, et al. A level set method for image segmentation in the presence of intensity inhomogeneities with application to mri [J]. IEEE Transactions on Image Processing, 2011, 20(7): 2007-2016.
