本文選題：CT圖像 + 放射組學��； 參考：《河北大學》2017年碩士論文

【摘要】：眾所周知,癌癥是目前全球發(fā)病率和死亡率最高的疾病之一。通過活體檢查的方式對腫瘤的良惡性進行診斷,不僅需要對患者做入侵手術,而且不利于醫(yī)生對腫瘤異質性的觀察。放射組學通過對放射影像中獲取的可描述性特征的量化和分析,將腫瘤表型和療效評估對應起來,為醫(yī)生對肺癌患者進行臨床上的診斷提供了良好的依據(jù)。近年來,深度學習方法被廣泛應用于醫(yī)學圖像處理領域。利用原始放射影像進行深度學習雖然可以獲得良好的識別效果,但由于其封閉的學習方式,無法得知圖像特征與分類結果間的關系,而采用傳統(tǒng)的淺層機器學習方法通常具有一定的局限性。本文主要主要研究工作如下:(1)采用傳統(tǒng)的統(tǒng)計方法從肺部CT圖像的感興趣區(qū)域中提取了幾何特征、紋理特征和直方圖特征,共143維特征向量作為原始特征集,用于結節(jié)的良惡性分類。(2)受基因組學在基因選擇中的啟發(fā),在Relief特征選擇算法中引入遞歸特征排除策略,形成RFE-Relief特征選擇算法,克服了Relief算法中不能去除冗余特征的缺點,最終得到包含有46維特征向量且與結節(jié)良惡性相關性強的低維特征子集。(3)構建了一個由三層受限玻爾茲曼機構成的深度置信網絡模型,并在頂層加入Softmax分類器,將由46維特征向量構成的特征子集作為DBN的輸入,通過對RBM和Softmax的逐層訓練和微調實現(xiàn)對結節(jié)的良惡性分類。實驗結果表明,最終的分類精確度為93.8%。通過特征選擇,不僅降低了算法的運行時間和效率,還提高了分類的精確度,可以輔助臨床醫(yī)生進行肺癌診斷。本文最后還對DBN模型在結節(jié)良惡性分類中的隱含層節(jié)點數(shù)和隱含層數(shù)進行了討論,結果顯示本文構建的DBN模型可以較好的完成結節(jié)良惡性的分類任務。
[Abstract]:It is well known that cancer is one of the highest morbidity and mortality in the world. The diagnosis of benign and malignant tumors by living examination not only requires invasive surgery, but also is not conducive to doctors' observation of tumor heterogeneity. By quantifying and analyzing the descriptive features obtained from radiographic images, radiology corresponds the tumor phenotype to the evaluation of curative effect, which provides a good basis for the clinical diagnosis of lung cancer patients. In recent years, depth learning has been widely used in the field of medical image processing. Although the deep learning of the original radiographic image can obtain good recognition effect, because of its closed learning mode, it is impossible to know the relationship between the image features and the classification results. However, the traditional shallow machine learning method usually has some limitations. The main work of this paper is as follows: (1) the geometric feature, texture feature and histogram feature are extracted from the region of interest of lung CT image by traditional statistical method. A total of 143 dimensional feature vector is used as the original feature set. Inspired by genomics in gene selection, recursive feature exclusion strategy is introduced into Relief feature selection algorithm to form RFE-Relief feature selection algorithm, which overcomes the disadvantage that redundant features can not be removed in Relief algorithm. Finally, a low dimensional feature subset containing 46 dimensional feature vectors with strong correlation with benign and malignant nodules is obtained. A depth confidence network model based on a three layer constrained Boltzmann mechanism is constructed, and a Softmax classifier is added to the top layer. A feature subset composed of 46 dimensional feature vectors is used as the input of DBN. The classification of benign and malignant nodules is realized by training and fine-tuning the RBM and Softmax layer by layer. The experimental results show that the final classification accuracy is 93. 8%. Feature selection not only reduces the running time and efficiency of the algorithm, but also improves the accuracy of classification, which can assist clinicians in the diagnosis of lung cancer. Finally, the number of hidden nodes and the number of hidden layers of DBN model in the classification of benign and malignant nodules are discussed. The results show that the DBN model constructed in this paper can accomplish the classification of benign and malignant nodules.
【學位授予單位】：河北大學
【學位級別】：碩士
【學位授予年份】：2017
【分類號】：R734.2;TP391.41

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