基于多期CT圖像的常見肝臟疾病計(jì)算機(jī)輔助診斷系統(tǒng)
發(fā)布時(shí)間：2019-01-12 16:09
【摘要】：隨著社會(huì)發(fā)展,世界衛(wèi)生組織等權(quán)威機(jī)構(gòu)均觀測到了肝臟疾病發(fā)病率的不斷上升,其中肝癌已經(jīng)成為致死率最高的肝臟疾病之一。除治療手段方面的原因外,早期肝癌病理指標(biāo)的不明顯,亦會(huì)造成患者確診與治療的延遲。當(dāng)前,肝癌的確診依然很大程度上依賴于穿刺活檢技術(shù),其實(shí)施難度大,且患者體驗(yàn)、術(shù)后恢復(fù)等均存在一定的問題。于是非介入式的計(jì)算機(jī)輔助診斷在肝臟疾病的檢測、發(fā)現(xiàn)、確診中有著非常廣闊的前景和重要的意義。 本文致力于設(shè)計(jì)一個(gè)常見肝臟疾病的計(jì)算機(jī)自動(dòng)輔助診斷系統(tǒng),該系統(tǒng)根據(jù)數(shù)據(jù)處理流程可依次劃分為：感興趣區(qū)域(Regin Of Interest, ROI)提取模塊、特征提取和特征選擇模塊、分類器模塊。由于常見肝臟疾病在CT圖像中的表現(xiàn)相似度較高,這給基于計(jì)算機(jī)的肝臟輔助診斷系統(tǒng)設(shè)計(jì)帶來了一定的難度,因此本文使用多期腹部CT掃描數(shù)據(jù)作為系統(tǒng)的輸入。系統(tǒng)首先結(jié)合了水平集方法和區(qū)域生長法提出病灶部分作為ROI；隨后提取了基于灰度直方圖、基于灰度共生矩陣的肝臟紋理統(tǒng)計(jì)特征和基于多期肝臟CT圖的時(shí)序特征作為特征向量,并經(jīng)過主成分分析法進(jìn)行特征選擇；最后將降維優(yōu)化后的特征向量輸入分類器模塊,選取支持向量機(jī)作為分類器算法,將系統(tǒng)設(shè)計(jì)為三層級(jí)聯(lián)的二分分類器,分別得到正常和非正常、肝囊腫和其它、肝血管瘤和肝癌的診斷準(zhǔn)確率,并結(jié)合醫(yī)學(xué)診斷的特殊性給出了接受者操作特性曲線值,兩者共同作為判別系統(tǒng)性能的參考。經(jīng)過實(shí)驗(yàn)數(shù)據(jù)驗(yàn)證,系統(tǒng)運(yùn)行穩(wěn)定且達(dá)到了較高的診斷準(zhǔn)確率。其中對(duì)于最重要的判別指標(biāo)——正常和非正常肝臟的分類準(zhǔn)確率達(dá)到了99.49%,證明了本文方法的可靠和有效性。 結(jié)合本文工作以及實(shí)際臨床需求,未來肝臟疾病輔助診斷系統(tǒng)需要在肝臟分割和病灶提取方面進(jìn)一步改進(jìn),以提高系統(tǒng)的診斷準(zhǔn)確率。
[Abstract]:With the development of society, the World Health Organization and other authoritative organizations have observed the rising incidence of liver diseases, among which liver cancer has become one of the most fatal liver diseases. In addition to the treatment methods, the early pathological indicators of liver cancer are not obvious, but also lead to the delay of diagnosis and treatment. At present, the diagnosis of liver cancer still depends largely on the puncture biopsy technique, which is difficult to implement, and there are some problems in patient experience, postoperative recovery and so on. Therefore, non-interventional computer-aided diagnosis has broad prospects and important significance in the detection of liver diseases. This paper is devoted to the design of a computer-aided diagnosis system for common liver diseases. According to the data processing process, the system can be divided into three modules: region of interest (Regin Of Interest, ROI) extraction module, feature extraction and feature selection module. Classifier module. Because of the high performance similarity of common liver diseases in CT images, it is difficult to design a computer-based liver aided diagnosis system, so we use multi-phase abdominal CT scan data as the input of the system. Firstly, the level set method and the region growth method are combined to propose the lesion part as ROI;. Secondly, the feature vectors of liver texture statistics based on gray histogram, gray level co-occurrence matrix and temporal feature based on multi-phase liver CT are extracted as feature vectors, and the feature selection is carried out by principal component analysis (PCA). Finally, the optimized feature vector is input into the classifier module, and the support vector machine is selected as the classifier algorithm. The system is designed as a three-layer cascade binary classifier to obtain normal and abnormal, liver cyst and others, respectively. The diagnostic accuracy of hepatic hemangioma and liver cancer was analyzed, and the operating characteristic curve of the recipient was given in combination with the particularity of medical diagnosis, which could be used as a reference to judge the performance of the system. The experimental data show that the system runs stably and achieves high diagnostic accuracy. The classification accuracy of normal and abnormal liver is 99.49, which proves the reliability and validity of this method. In order to improve the accuracy of diagnosis, the future liver disease diagnosis system needs to be further improved in liver segmentation and focus extraction.
【學(xué)位授予單位】：廈門大學(xué)
【學(xué)位級(jí)別】：碩士
【學(xué)位授予年份】：2014
【分類號(hào)】：R575;TP391.41
本文編號(hào)：2407965
[Abstract]:With the development of society, the World Health Organization and other authoritative organizations have observed the rising incidence of liver diseases, among which liver cancer has become one of the most fatal liver diseases. In addition to the treatment methods, the early pathological indicators of liver cancer are not obvious, but also lead to the delay of diagnosis and treatment. At present, the diagnosis of liver cancer still depends largely on the puncture biopsy technique, which is difficult to implement, and there are some problems in patient experience, postoperative recovery and so on. Therefore, non-interventional computer-aided diagnosis has broad prospects and important significance in the detection of liver diseases. This paper is devoted to the design of a computer-aided diagnosis system for common liver diseases. According to the data processing process, the system can be divided into three modules: region of interest (Regin Of Interest, ROI) extraction module, feature extraction and feature selection module. Classifier module. Because of the high performance similarity of common liver diseases in CT images, it is difficult to design a computer-based liver aided diagnosis system, so we use multi-phase abdominal CT scan data as the input of the system. Firstly, the level set method and the region growth method are combined to propose the lesion part as ROI;. Secondly, the feature vectors of liver texture statistics based on gray histogram, gray level co-occurrence matrix and temporal feature based on multi-phase liver CT are extracted as feature vectors, and the feature selection is carried out by principal component analysis (PCA). Finally, the optimized feature vector is input into the classifier module, and the support vector machine is selected as the classifier algorithm. The system is designed as a three-layer cascade binary classifier to obtain normal and abnormal, liver cyst and others, respectively. The diagnostic accuracy of hepatic hemangioma and liver cancer was analyzed, and the operating characteristic curve of the recipient was given in combination with the particularity of medical diagnosis, which could be used as a reference to judge the performance of the system. The experimental data show that the system runs stably and achieves high diagnostic accuracy. The classification accuracy of normal and abnormal liver is 99.49, which proves the reliability and validity of this method. In order to improve the accuracy of diagnosis, the future liver disease diagnosis system needs to be further improved in liver segmentation and focus extraction.
【學(xué)位授予單位】：廈門大學(xué)
【學(xué)位級(jí)別】：碩士
【學(xué)位授予年份】：2014
【分類號(hào)】：R575;TP391.41
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