北京生物醫(yī)學工程

基于Curvelet變換的肺結(jié)節(jié)CT圖像良惡性分類研究

Classification of Malignant and Benign Pulmonary Nodules in CT Image Based on Curvelet Transformation

作者：吳海豐劉韞寧孫濤李霞郭秀花賀文

單位：首都醫(yī)科大學公共衛(wèi)生與家庭醫(yī)學學院（北京100069）

關(guān)鍵詞： Curvelet變換；紋理特征；BP神經(jīng)網(wǎng)絡；受試者工作特征曲線

分類號：

出版年·卷·期（頁碼）：2011·30·5（471-473）

摘要：

目的早期肺癌患者的CT圖像表現(xiàn)為結(jié)節(jié)狀（在肺野內(nèi)直徑≤3cm的病灶），需要與結(jié)核球等良性病
變鑒別開, 以提高患者的5年生存率。方法本文基于Curvelet變換提取能量、熵、灰度均值及灰度標準
差四種紋理特征值，按7∶3比例將樣本分為訓練集與驗證集。使用BP(back propagation)神經(jīng)網(wǎng)絡作為
分類器。每一種紋理參數(shù)測試集的神經(jīng)網(wǎng)絡仿真值結(jié)合病理診斷結(jié)果繪制受試者工作特征曲線
（receiver operator characteristic curve, ROC曲線），根據(jù)ROC下面積得到最優(yōu)的幾種紋理參數(shù)用
于良惡性分類，并將分類結(jié)果與病理診斷結(jié)果進行比較。結(jié)果四種紋理參數(shù)構(gòu)建的BP網(wǎng)絡均具有診斷價
值，每種紋理參數(shù)診斷價值各不相同，其中熵與灰度標準差的診斷價值優(yōu)于能量與灰度均值，并且通過
組合多種紋理參數(shù)可以提高診斷準確性。結(jié)論使用熵與灰度標準差兩種紋理特征值構(gòu)建BP神經(jīng)網(wǎng)絡能達
到最好的分類效果，在一定程度上有利于肺癌的早期診斷。

Objective To raise the 5-year survival rate,the CT detected pulmonary
nodules,which size is defined smaller than 30mm, is needed to be distinguished between
benign or malignantones. Methods Curvelet transformation was introduced in this paper and
four texture features, including energy, entropy, gray scale mean and gray scale
standardized deviation, were calculated. The samples were divided into 2 parts, 70% in test
set, and the 30% in validation set. A back propagation(BP) artificial neutral network was
used as the classifier. The testing set of each texture feature obtained a ROC (receiver
operator characteristic curve) by using its simulation result of the BP artificial neutral
network and pathological diagnosis. The optimal texture features were chosen to predict the
characteristic of small solitary pulmonary nodules in the CT images compared with other
texture features, it was more proper to use the entropy and standard deviation as
parameters to establish the prediction model. Results The BP artificial neutral network
established by parameters entropy and standard deviation provided the best discrimination
of the benign and the malignant small solitary pulmonary nodules. Conclusions We can
profit from the diagnosis of early stage carcinoma of the lung to some extent with the BP
artificial neutral network, which utilizes the entropy and standard deviation as
parameters.

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