北京生物醫(yī)學(xué)工程

新疆哈薩克族食管癌圖像特征提取及分型方法的探討

Feature extraction and typing method of esophageal cancer images for Xinjiang Kazakh nationality

作者：茹仙古麗·艾爾西丁木拉提·哈米提嚴(yán)傳波姚娟排孜麗耶·尤山塔依娜迪亞·阿卜杜迪克依木

單位：新疆醫(yī)科大學(xué)基礎(chǔ)醫(yī)學(xué)院（烏魯木齊830011）新疆醫(yī)科大學(xué)醫(yī)學(xué)工程技術(shù)學(xué)院（烏魯木齊830011）新疆醫(yī)科大學(xué)第一附屬醫(yī)院放射科（烏魯木齊830054）

關(guān)鍵詞：食管癌；灰度-梯度共生矩陣；灰度共生矩陣；特征提取；圖像分類

分類號(hào)：R318.04

出版年·卷·期（頁(yè)碼）：2019·38·3（257-262）

摘要：

目的利用支持向量機(jī)（support vector machine ,SVM）對(duì)新疆哈薩克族X線食管造影圖像進(jìn)行特征提取及分型研究,為食管癌影像學(xué)診斷提供參考。方法隨機(jī)選取正常食管和蕈傘型食管癌X線造影圖像各200幅，運(yùn)用灰度-梯度共生矩陣法和灰度共生矩陣法提取圖像的特征，在SVM類型設(shè)置上選擇C-SVC，選擇多項(xiàng)式核函數(shù)，通過(guò)調(diào)整C-SVC分類器的參數(shù)進(jìn)行實(shí)驗(yàn)。結(jié)果共計(jì)提取23維特征，利用單一特征算法進(jìn)行分類，灰度-梯度共生矩陣法分類準(zhǔn)確率為72.75％，灰度共生矩陣法分類準(zhǔn)確率為85.25％，而混合紋理特征的分類準(zhǔn)確率為86.25％。結(jié)論將紋理特征與SVM相結(jié)合對(duì)正常食管與蕈傘型食管癌X線造影圖像進(jìn)行特征提取及分析，具有較高的分類識(shí)別率，混合特征把圖像紋理和灰度特征有效結(jié)合，提高了特征的分類能力，為食管癌的計(jì)算機(jī)輔助診斷系統(tǒng)的開(kāi)發(fā)奠定了基礎(chǔ)。

Objective To study the feature extraction and typing for X- ray images of Xinjiang Kazakh esophageal cancer based on support vector machine（SVM）.Methods We randomly selected 200 pieces normal esophagus X-ray image and mushroom esophageal carcinoma X-ray image. Gray gradient co-occurrence matrix and gray level co-occurrence matrix texture features were applied to extract the image features.And the feature classification ability was evaluated by C-SVC classifier.Conducting the experiment repeatedly by adjusting the C-SVC classifier. Results Twenty-three features were extracted by gray gradient co-occurrence matrix and gray level co-occurrence matrix.The experimental results showed that using single feature classification，the accuracy rate of gray gradient co-occurrence matrix and gray level co-occurrence matrix classification reached to 72.75％ and 85.25％,respectively.The accuracy rate of the comprehensive of gray gradient co-occurrence matrix and gray level co-occurrence matrix was 86.25%.It was more suitable for the classification of normal esophagus and mushroom esophageal carcinoma. Conclusions The characteristics of the normal esophagus and the mushroom esophageal carcinoma are extracted and analyzed with the SVM, which have high classification recognition rate，and laid the foundation for the development of computer-aided diagnosis system for esophageal cancer.

參考文獻(xiàn)：

[1]劉泓，王海霞.窄帶成像放大技術(shù)與超聲微探頭結(jié)合在診斷早期食管癌的應(yīng)用[J].中國(guó)藥物與臨床,2017,17(2):206-207.

Liu H,WangHX.Application of narrowband imaging amplification technique combined with ultrasound microprobe in the diagnosis of early esophageal carcinoma[J].Chinese Remedies & Clinics,2017,17(2):206-207.

[2] 張朋，王少康，孫桂菊．食物、營(yíng)養(yǎng)與食管癌關(guān)系的研究進(jìn)展[J]．腫瘤?,2011,31(8):768-771．

Zhang P, Wang SK, Sun GJ. Research progress in association of food and nutrition with esophageal cancer [J].Tumor,2011,31(8):768-771．

[3] 楊芳，木拉提·哈米提，嚴(yán)傳波，等.PCA和SVM在新疆哈薩克族食管癌圖像分類中的研究與應(yīng)用[J]．科技通報(bào)，2016，32(3):53-57．

Yang F, Murat H, Yan CB,etal.Classification of Esophageal Cancer Images for Xinjiang Kazak Nationality[J].science and Technology Bulletin,2016，32(3):53-57．

[4] 郭文佳，賈小莉，王喜艷.哈薩克族食管癌核心家系標(biāo)本庫(kù)的建立[J].新疆醫(yī)科大學(xué)學(xué)報(bào)，2015，38(3):266-268.

Guo WJ, Jia XL, Wang Xiyan.The establishment of nuclear family of Kazak`s esophageal cancer [J]. Journal of Xinjiang Medical University,2015，38(3):266-268.

[5]Tixier F, Le Res CCt, HattM,et al. Intratumor heterogeneity characterized by textural features on baseline 18F-FDG PET images predicts response to concomitantradiochemotherapyin esophagealcancer[J].Nucl Med,2010,52(3):369-378.

[6]王迪，陳庭毅，陳嘉彬.圖像特征提取算法研究[J].電腦知識(shí)與技術(shù)，2017,15（13）：149-150,155.

Wang D, Chen TY, Chen JB. Research of image feature extraction algorithm [J]. computer knowledge and technology.2017,15（13）：149-150,155.

[7]張學(xué)工 . 關(guān)于統(tǒng)計(jì)學(xué)習(xí)理論與支持向量機(jī)［J］. 自動(dòng)化學(xué)報(bào)， 2000，26（1）：32-42.

Zhang XG.introduction to statistical learning theory and support vector machines[J].acta automatica sinica.2000，26（1）：32-42.

[8]褚洪波.支持向量機(jī)理論與算法研究[J].和田師范專科學(xué)校學(xué)報(bào),2012,31(5):104-106.

Zhu HB. Support vector machine theory and algorithm research [J]. Journal of Hotan Teachers College,2012,31(5):104-106.

[9]劉宏兵,柳春華.用于手寫(xiě)數(shù)字識(shí)別的增量式模糊支持向量機(jī)[J].信陽(yáng)師范學(xué)院學(xué)報(bào)（自然科學(xué)版）,2014,27(3):421-424.

Liu HB, Liu CH. Incremental Fuzzy Support Vector Machines for Handwritten Digit Recognition [J]. Journal of Xinyang Normal University (natural science edition),2014,27(3):421-424.

[10]李軍,高立群,蔡翔.基于支持向量機(jī)的消極性文本識(shí)別研究[J].電腦知識(shí)與技術(shù),2017,13(6):209-211.

Li J,GaoLQ,Cai X. Research on negative text recognition based on support vector machine[J].Computer knowledge and technology,2017,13(6):209-211.

[11]談蓉蓉.基于支持向量機(jī)分類的圖像識(shí)別研究[J].安徽農(nóng)業(yè)科學(xué) ,2010,38(26):14756-14757.

Tan RR. Image Recognition Research Based on Support Vector Machine Classification[J].AnhuiAgricultural Sciences,2010,38(26):14756-14757.

[12]俞春強(qiáng),付睿,陳艷等.基于信息隱藏的圖像感興趣區(qū)域保護(hù)算法[J].廣西師范大學(xué)學(xué)報(bào)，2016,34（2）：67-73.

Yu CQ, Fu R, Chen Y, et al. Image region of interest protection algorithm based on information hiding [J]. Journal of Guangxi Normal University,2016,34（2）：67-73.

[13]華顯立，許貴陽(yáng).數(shù)字圖像中值濾波技術(shù)研究[J].電子設(shè)計(jì)工程，2014,22（11）：191-193.

Hua XL, Xu GY. Study on digital image median filtering technique [J]. Electronic Design Engineering,2014,22（11）：191-193.

[14] 吳艷，朱明遠(yuǎn)，周富強(qiáng).基于直方圖均衡化的渠道冰凌密度計(jì)算[J].人民長(zhǎng)江，2017,48（14）：69-72.

Wu Y, Zhu MY, Zhou FQ. Calculation of canal ice density based on histogram equalization [J]. people's Yangtze River,2017,48（14）：69-72.

[15] 肖達(dá)，王潤(rùn)民，等.基于Gabor變換和灰度梯度共生矩陣的超聲無(wú)損測(cè)溫研究[J].傳感技術(shù)學(xué)報(bào)，2017,30（11）：1684-1688.

Xiao D, Wang RM, et al. A Noninvasive Temperature Measurement Based on Gabor Transform and Gray Level Gradient Co-Occurrence Matrix Using Ultrasound [J]. Journal of sensing technology,2017,30（11）：1684-1688.

[16]Haralick RM. Statistical and structural approaches to texture[J]. Proceedings of the IEEE，1979，67(5):786-840.

[17]Haralick RM，Shanmugam K，DisteninI.Textural features for image classification[J]. IEEE Trans Syst Man Cybern，1973，3(6): 610-21.

[18]歐國(guó)振，孫林慧，薛海雙.基于重組超矢量的GMM-SVM說(shuō)話人辨認(rèn)系統(tǒng)[J].計(jì)算機(jī)技術(shù)與發(fā)展，2017,27（7）：51-56.

Ou GZ, Sun LH, Xue HS. GMM-SVM Speaker Identification System with Recombination of GMM Super Vector[J]. Computer technology and development,2017,27(7)：51-56.

[19]Vapnik VN. The nature of statistical learning theory [M].New York: Springer -Verlag，1995.

[20]田芳，周孝信，于之虹.基于支持向量機(jī)綜合分類模型和關(guān)鍵樣本集的電力系統(tǒng)暫態(tài)穩(wěn)定評(píng)估[J].電力系統(tǒng)保護(hù)與控制,2017,45(22):1-8.

Tian F, Zhou XX, in the rainbow.Power system transient stability assessment based on comprehensive SVM classification model and key sample set[J]. power system protection and control,2017,45(22):1-8.

[21]杜娟,鄔麟,陳柏林.食管癌的主要發(fā)病因素及其機(jī)制[J].中國(guó)醫(yī)藥導(dǎo)報(bào),2017,14(7):40-44.

Du J,WuL,Chen BL.Inducing factors of esophagus cancer and the underlying mechanisms[J].China Medical Herald,2017,14(7):40-44.

服務(wù)與反饋：

【文章下載】【加入收藏】

提示：您還未登錄，請(qǐng)登錄！點(diǎn)此登錄

51黑料吃瓜在线观看,51黑料官网|51黑料捷克街头搭讪_51黑料入口最新视频