北京生物醫(yī)學工程

基于直方圖均衡化的伽馬校正和K-means聚類的舌象苔質(zhì)分離方法

Separation method of tongue coating and body of tongue image based on histogram equalization and gamma correction and K-means clustering

作者：韓立博胡廣芹張新峰馮利李泉旺蔡軼珩

單位：北京工業(yè)大學信息學部（北京 100124）<p>中國醫(yī)學科學院腫瘤醫(yī)院（北京 100021）</p><p>北京中醫(yī)藥大學東方醫(yī)院（北京 100078）</p>

關鍵詞：腫瘤；舌苔舌質(zhì)分離；直方圖均衡化；伽馬校正； K-means聚類； a*通道

分類號：R318.04

出版年·卷·期（頁碼）：2019·38·1（1-6）

摘要：

目的舌苔舌質(zhì)分離對后續(xù)腫瘤患者舌象的客觀化辨證具有重要的意義。常用的算法是基于顏色空間通道舌圖像的k-means聚類算法。CIEL*a*b*顏色空間的a*通道舌圖像相較于其他顏色空間通道的舌圖像分割結(jié)果穩(wěn)定，常用來后續(xù)的分割。但對于部分舌圖像而言，a*通道舌圖像的舌苔、舌質(zhì)雖然具有一定的區(qū)分度，但二者的區(qū)分度并不是十分明顯，影響后續(xù)的分割結(jié)果。因此，本文提出一種基于直方圖均衡化的伽馬校正和K-means聚類的舌苔舌質(zhì)分離方法。方法采用200幅腫瘤患者的舌圖像作為實驗樣本。首先將舌圖像從RGB顏色空間轉(zhuǎn)換到CIEL*a*b*顏色空間，對a*通道舌圖像進行直方圖均衡化增強以及伽馬校正，然后利用k-means聚類方法對增強后的舌圖像舌苔舌質(zhì)分離。得到直方圖均衡化以及伽馬校正后的a*通道舌圖像和分割后的舌苔、舌質(zhì)圖像。為了驗證算法的可行性，請5位專業(yè)中醫(yī)醫(yī)生對200例腫瘤患者的舌圖像舌苔、舌質(zhì)分割效果進行辨析。結(jié)果進行直方圖均衡化以及伽馬校正后的a*通道舌圖像舌苔、舌質(zhì)分割結(jié)果明顯強于未經(jīng)處理的a*通道舌圖像分割結(jié)果。經(jīng)辨析，分割合格率為97%。結(jié)論該方法可以很好地實現(xiàn)舌苔舌質(zhì)分離，具有一定的應用價值。

Objective Tongue coating and body separation has an important significance to the subsequent objective syndrome differentiation of tongue images of tumor patients. The common algorithms are K-means clustering algorithm based on tongue image of color space channels.The a* channel tongue image in CIEL*a*b* color space is stable compared with other color space channels in tongue segmentation.It is often used for subsequent segmentation.For some tongue images, tongue coating and body of the a* channel have a certain degree of differentiation , but the degree of discrimination between the two is not very obvious,which affects the subsequent segmentation results. Therefore, we designed a separation method of tongue coating and body based on histogram equalization and gamma correction and K-means clustering. Methods 200 tongue images of tumour patients were used as experimental samples. Firstly, the tongue image was transformed from RGB color space to CIEL*a*b* color space.The tongue image of the a* channel was enhanced by histogram equalization and gamma correction. Secondly, tongue coating and body of enhanced tongue image was separated by the K-means clustering method. The histogram equalization and the gamma corrected a* channel tongue image and the segmented tongue coating and tongue body images were obtained.In order to verify the feasibility of the algorithm, five professional Chinese medicine doctors were asked to discriminate results of tongue coating and tongue body segmentation on 200 patients with tumors. Results The tongue coating and body segmentation results of the a* channel tongue image after histogram equalization and gamma correction are obviously better than unprocessed a* channel tongue image segmentation results.After analysis, the qualified rate of segmentation is 97%.Conclusions This method can achieve good separation of tongue coating and body and has a certain application value.

參考文獻：

[1]吳萬垠.中醫(yī)腫瘤診療中的診斷、辨病、辨證與辨癥[J].中國中西醫(yī)結(jié)合雜志,2018,38(2):156-158.

[2]高清河,剛晶,王和禹,劉海英.舌診圖像分割和特征提取的方法研究與應用[J].中國中醫(yī)藥現(xiàn)代遠程教育,2017,15(13):147-149.

Gao QH, Gang J, Wang HY, et al. Research and application of tongue image segmentation and feature extraction method in traditional Chinese medicine[J]. Chinese Medicine Modern Distance Education of China, ,2017,15(13):147-149.

[3]Kawanabe T, Kamarudin N D, Ooi C Y, et al. Quantification of tongue colour using machine learning in Kampo medicine[J]. European Journal of Integrative Medicine, 2016,8(6):932-941.

[4]杜建強,盧炎生.一種中醫(yī)舌象的舌質(zhì)舌苔分離方法[J].計算機應用研究,2009,26(07):2762-2764.

Du JQ, Lu YS. Separation algorithm of tongue body and tongue coating[J]. Application Research of Computers, 2009, 26 (7):2762-2764.

[5]Wei CC, Wang CH, Huang S W. Using threshold method to separate the edge, coating and body of tongue in automatic tongue diagnosis[C]. The 6th International Conference on Networked Computing and Advanced Information Management, Seoul, 2010, pp. 653-656.

[6]方衡.基于改進的最大類間方差的舌色和舌苔分離技術[J].電腦知識與技術,2008,3(4):711-713.

Fang H. Based on advenced otsu methord separating the tongue coating and the tongue image[J]. Computer Knowledge & Technology, 2008，3(4):711-713

[7]許家佗,屠立平,張志楓,等.基于圖像區(qū)域分割方法的舌質(zhì)與舌苔識別[J].上海中醫(yī)藥大學學報,2009,23(03):42-45.

Xu JT, Tu LP, Zhang ZF,et al. Identification of tongue body and fur based on color image region separation[J]. Acta Universitatis Traditionis Medicalis Sinensis Pharmacologiaeque Shanghai, 2009,23(03):42-45.

[8]Kamarudin N D, Ooi C Y, Kawanabe T, et al. A fast and effective segmentation algorithm with automatic removal of ineffective features on tongue images[J]. Jurnal Teknologi ,2016, 78(8):153-163.

[9]李兆龍,蘇育挺.一種基于聚類的舌苔舌質(zhì)分離方法[J].南開大學學報(自然科學版),2017,50(04):52-56+62.

Li ZL, Su YT. A kind of method for separation between tongue coating and nature based on clustering[J]. Acta Scientiarum Naturalium Universitatis Nankaiensis,2017,50(04):52-56+62.

[10]郭宙,楊學智,司銀楚,等.基于K-均值聚類的常用色彩空間舌質(zhì)舌苔分割研究[J].北京中醫(yī)藥大學學報,2009,32(12):819-821+871.

Guo Z, Yang XZ, Si YC, et al. Segmentation of tongue body and fur based on common color spaces of Kmeans clustering[J]. Journal of Beijing University of Traditional Chinese Medicine, 2009,32(12):819-821+871.

[11]王學民,呂元婷,王瑞云,等.基于雙光源的舌質(zhì)舌苔分離方法研究[J].納米技術與精密工程,2016,14(06):434-439.

Wang XM, Lv YT , Wang RY, et al. Research on separation method of tongue body and coating based on double light sources[J]. Nanotechnology & Precision Engineering, 2016,14(06):434-439.

[12]楊飚,楊芩.Lab顏色空間和形態(tài)學處理相結(jié)合的雙行車牌定位方法[J].科學技術與工程,2014,14(26):108-110+130.

Yang B, Yang Q. Two-line license plate detection method based on CIELab space and morphological processing[J]. Science Technology & Engineering, 2014,14(26):108-110+130.

[13]陳守剛.基于直方圖均衡化的彩色圖像增強研究[J].重慶三峽學院學報,2011,27(03):74-77.

Chen SG. Color image enhancement based on histogram equalization[J]. Journal of Chongqing Three Gorges University,2011,27(03):74-77.

[14]張錚 ,徐超，任淑霞，等.數(shù)字圖像處理與機器視覺:Visual C++與Matlab實現(xiàn)[M].北京：人民郵電出版社，2015.8.

[15]崔艷軍,張文峰,李建欣，等.條紋投影三維測量的Gamma畸變校正方法[J].光學學報,2015,35(1):161-170.

Cui YJ, Zhang WF, Li JX, et al. A method of gamma correction in fringe projection measurement[J]. Acta Optica Sinica,2015,35(1):161-170.

[16] Shi CZ, Wang YN, Xiao BH, et al. OTSU guided adaptive binarization of CAPTCHA image using gamma correction[C]. 2016 23rd International Conference on Pattern Recognition (ICPR), Cancun, 2016, pp. 3962-3967.

[17]周飛,賈振紅,楊杰，等.基于剪切波域改進Gamma校正的醫(yī)學圖像增強算法[J].光電子·激光,2017,28(05):566-572.

Zhou F, Jia H, Yang J, et al. Medical image enhancement method based on improved Gamma correction in Shearlet domain[J]. Guangdianzi Jiguang/journal of Optoelectronics Laser, 2017, 28(05):566-572.

[18]吳迪,劉偉峰,胡勝,等.基于Lab空間的K均值聚類彩色圖像分割[J].電子科技,2017,30(10):29-32.

Wu D , Liu WF,Hu S, et al. Color image segmentation using k-mean clustering based on lab space[J]. Electronic Science & Technology, 2017,30(10):29-32.

[19]Kumar B, Maheshkar SV, Bist AS. Image segmentation using enhanced K-means clustering with divide and conquer approach[J]. International Journal of Engineering Sciences & Research Technology, 2014, 3(7):188-196

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