發(fā)布時(shí)間：2018-06-01 20:39

  本文選題：新疆哈薩克族 + 食管癌　； 參考：《新疆醫(yī)科大學(xué)》2017年碩士論文

【摘要】：目的:本研究根據(jù)新疆哈薩克族食管X射線圖像的特點(diǎn),探討計(jì)算機(jī)輔助診斷技術(shù)和算法在新疆哈薩克族食管癌圖像中的應(yīng)用,驗(yàn)證算法的可行性及合理性。從而為基于醫(yī)學(xué)X射線圖像的計(jì)算機(jī)輔助診斷技術(shù)輔助醫(yī)生進(jìn)行病變類型圖像的判別,降低醫(yī)生的工作量,提高診斷質(zhì)量。方法:利用MATLAB圖像處理軟件,對(duì)食管X射線圖像進(jìn)行預(yù)處理包括選取感興趣區(qū)域、中值濾波和直方圖均衡化;其次,將預(yù)處理后的圖像選用閾值分割法進(jìn)行分割;利用灰度共生矩陣、Hu不變矩特征、灰度直方圖和小波變換算法對(duì)分割后的圖像提取特征值;采用兩種特征選擇算法,基于獨(dú)立樣本T檢驗(yàn)和主成分分析相結(jié)合對(duì)正常食管和病理食管圖像的特征值進(jìn)行優(yōu)化選擇,基于單因素方差分析和主成分分析結(jié)合的特征選擇算法對(duì)蕈傘型、潰瘍型和浸潤(rùn)型食管癌圖像進(jìn)行特征篩選,消除不同特征間的冗余信息;根據(jù)上述兩種特征選擇算法,采用BP神經(jīng)網(wǎng)絡(luò)分別設(shè)計(jì)兩個(gè)分類器對(duì)哈薩克族食管X射線圖像進(jìn)行分類識(shí)別,采用分類準(zhǔn)確率和Kappa值作為分類器性能的評(píng)價(jià)指標(biāo)。結(jié)果:(1)經(jīng)過預(yù)處理后圖像的質(zhì)量得到了提高,圖像的清晰度明顯增強(qiáng),且獲得的圖像邊緣細(xì)節(jié)都較清晰;(2)本文使用閾值分割法對(duì)預(yù)處理后的圖像進(jìn)行分割,可以分割出完整的、清晰的、不失真的病灶區(qū)域,該方法保留了圖像的完整性;(3)通過四種特征提取算法對(duì)分割后的正常圖像和病理圖像進(jìn)行特征的提取,共計(jì)提取了29個(gè)特征值;(4)第一種特征選擇算法,即使用獨(dú)立樣本T檢驗(yàn)結(jié)合主成分分析特征選擇算法對(duì)正常食管和病理食管進(jìn)行特征篩選時(shí),首先使用獨(dú)立樣本T檢驗(yàn)篩選出19個(gè)特征值,然后在此基礎(chǔ)上對(duì)特征值做主成分分析,選擇前5個(gè)累積貢獻(xiàn)率達(dá)到88.085%的主成分;另一種方法為采用單因素方差分析和主成分分析結(jié)合的特征選擇算法對(duì)蕈傘型、潰瘍型和浸潤(rùn)型食管癌圖像進(jìn)行特征的篩選時(shí),首先利用單因素方差分析選擇了24個(gè)特征,然后使用主成分分析對(duì)這些特征值進(jìn)行計(jì)算,所以求得主成分的個(gè)數(shù)為5,且累積貢獻(xiàn)率達(dá)到了87.537%;(5)使用BP神經(jīng)網(wǎng)絡(luò)對(duì)正常食管和病理食管進(jìn)行分類時(shí),獨(dú)立樣本T檢驗(yàn)結(jié)合主成分分析特征選擇算法在隱含層節(jié)點(diǎn)數(shù)為7時(shí)對(duì)圖像的分類結(jié)果高于其它四種不同特征提取算法、綜合特征及獨(dú)立樣本T檢驗(yàn)篩選出的特征值的分類結(jié)果,即正常和病理食管圖像的平均分類準(zhǔn)確分別為97.130%、98.620%,分類效果較好;使用BP神經(jīng)網(wǎng)絡(luò)結(jié)合單因素方差分析與主成分分析特征選擇算法對(duì)三種食管癌圖像進(jìn)行分類時(shí),所得蕈傘型、潰瘍型和浸潤(rùn)型食管癌的分類準(zhǔn)確率分別為98.000%、96.000%、98.500%,與其它三種方法比較,分類結(jié)果較為精確;結(jié)論:本研究選取的預(yù)處理和閾值分割算法不僅去除了噪聲使圖像質(zhì)量得到改善,而且也保留了完整的目標(biāo)區(qū)域。其次,對(duì)處理后的圖像使用不同特征提取算法得到的特征值采用特征選擇算法篩選出了分類能力較強(qiáng)的特征值。最后,利用BP神經(jīng)網(wǎng)絡(luò)分類器對(duì)食管圖像進(jìn)行分類,且取得了較高的分類準(zhǔn)確率。本文提出的特征選擇算法結(jié)合BP神經(jīng)網(wǎng)絡(luò)算法是合理可行的,這為哈薩克族地區(qū)的放射科醫(yī)生提供有價(jià)值的參考意見,提高診斷質(zhì)量,為開發(fā)面向放射科的新疆哈薩克族食管癌計(jì)算機(jī)輔助診斷系統(tǒng)奠定了基礎(chǔ)。
[Abstract]:Objective: To study the application of computer aided diagnosis technology and algorithm in the Xinjiang Kazak esophageal cancer image in Xinjiang based on the characteristics of the Kazak's X ray image of the Kazak nationality in Xinjiang, and to verify the feasibility and rationality of the algorithm. Thus, the computer aided diagnosis technology based on the medical X ray image is used to assist the doctor to carry out the disease type image To reduce the workload of the doctors and improve the quality of diagnosis. Methods: using MATLAB image processing software, the preprocessing of the X ray images of the esophagus includes selected regions of interest, median filtering and histogram equalization; secondly, the pre processed images are segmented by threshold segmentation, and the gray level symbiotic matrix and Hu invariant moments are used. The gray histogram and wavelet transform algorithm extracts the eigenvalues of the segmented images. Two feature selection algorithms are used to select the eigenvalues of the normal esophagus and the pathological esophagus, based on the combination of independent sample T test and principal component analysis, and the feature selection algorithm based on the combination of single factor variance analysis and principal component analysis. The images of fungoid, ulcerative and infiltrating esophageal cancer were screened to eliminate the redundant information between different features. According to the above two feature selection algorithms, two classifiers were designed by BP neural network to classify the Kazak's esophagus X ray images, and the classification accuracy and Kappa values were used as the classifier performance evaluation. Results: (1) the quality of the image is improved after preprocessing, the image sharpness is obviously enhanced and the image edge details are clear. (2) this paper uses the threshold segmentation method to segment the pre processed image, which can separate the complete, clear and undistorted focus area, which preserves the image. Integrity; (3) four feature extraction algorithms are used to extract the characteristics of the normal and pathological images after the segmentation, and a total of 29 eigenvalues are extracted. (4) the first feature selection algorithm, the first use of the independent sample T test combined with the principal component analysis feature selection algorithm to screen the normal esophagus and the pathological esophagus. 19 eigenvalues were screened by independent sample T test, then the principal component analysis was performed on the eigenvalues, and the first 5 cumulative contribution rates of 88.085% were selected, and the other was a feature selection algorithm combined with single factor analysis of variance and principal component analysis for the image of fungoid, ulcerative and infiltrating esophageal cancer. When screening, 24 characteristics are selected by single factor analysis of variance, and then the principal component analysis is used to calculate these eigenvalues, so the number of the principal components is 5 and the cumulative contribution rate is 87.537%. (5) the independent sample T test combined with the main formation using the BP neural network for the normal esophagus and the pathological esophagus. The classification result is higher than the other four different feature extraction algorithms when the number of hidden layer nodes is 7. The classification results of the characteristic values selected by the integrated feature and the independent sample T test, that is, the average classification accuracy of the normal and pathological esophagus images is 97.130%, 98.620%, and the classification effect is better; the use of BP God is good. The classification accuracy of the three kinds of esophageal cancer was 98%, 96%, 98.500%, respectively, with the single factor variance analysis and the principal component analysis feature selection algorithm. The classification results were more accurate than those of the other three methods. Conclusion: This study selected the preprocessing and the results of the study. The threshold segmentation algorithm not only removes the noise to improve the quality of the image, but also preserves the complete target area. Secondly, the eigenvalues obtained by using different feature extraction algorithms after the processing of the processed images are selected by the feature selection algorithm. Finally, the BP neural network classifier is used for the esophagus map. It is reasonable and feasible to combine the feature selection algorithm combined with the BP neural network algorithm. This provides a valuable reference for the radiologists of the Kazak region to improve the quality of diagnosis and to develop a computer aided diagnosis of the Xinjiang Kazakh cancer in the radiology department. The system lays the foundation.
【學(xué)位授予單位】：新疆醫(yī)科大學(xué)
【學(xué)位級(jí)別】：碩士
【學(xué)位授予年份】：2017
【分類號(hào)】：R735.1

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本文編號(hào)：1965552