本文關(guān)鍵詞：低劑量胸腔CT肺部影像的肺結(jié)節(jié)計(jì)算機(jī)輔助診斷方法研究　出處：《西南交通大學(xué)》2017年碩士論文　論文類型：學(xué)位論文

  更多相關(guān)文章： CT 肺結(jié)節(jié) 加權(quán)中值濾波 分割 檢測

【摘要】：肺癌目前被稱為了全世界的頭號(hào)癌癥,并且發(fā)病率一直在上升,在我國表現(xiàn)的尤為顯著。各項(xiàng)研究表明,早期的診斷和治療,能夠提升肺癌患者的治愈率,對(duì)愈后的恢復(fù)也有重要作用。近幾年來,隨著各種醫(yī)學(xué)成像設(shè)備的普遍使用,醫(yī)學(xué)設(shè)備成像的各種先進(jìn)技術(shù)也越來越受到醫(yī)學(xué)工作者的重視。肺結(jié)節(jié)的定位和定性有賴于這些技術(shù)的應(yīng)用。為此,胸腔CT影像的肺結(jié)節(jié)計(jì)算機(jī)輔助檢測識(shí)別系統(tǒng)的研究引起了越來越多科研工作者和醫(yī)生的重視。由于醫(yī)學(xué)影像本身就存在灰度值不均勻、個(gè)體差異、偽影、噪聲、邊緣模糊等因素,所以有關(guān)醫(yī)學(xué)影像處理的算法,要達(dá)到較高的靈敏度和精確度具有很大的難度。本文研究對(duì)象基于低劑量胸腔CT圖像,在CT圖像預(yù)處理、肺實(shí)質(zhì)分割、疑似肺結(jié)節(jié)提取、肺結(jié)節(jié)檢測這幾個(gè)方面,進(jìn)行了實(shí)驗(yàn)和研究。提出了基于低劑量胸腔CT肺部影像的結(jié)節(jié)檢測方法:首先,對(duì)原始數(shù)據(jù)進(jìn)行轉(zhuǎn)格式換,并針對(duì)CT圖像成像過程中所攜帶的噪聲問題,引進(jìn)了快速自適應(yīng)加權(quán)中值濾波器(Faster Weighted Median Filter,FWMF)進(jìn)行預(yù)處理;其次,為縮小目標(biāo)范圍及原始數(shù)據(jù)存在異常圖片而導(dǎo)致難以基于單張切片處理得到較好分割結(jié)果的問題,提出使用自動(dòng)區(qū)域生長法對(duì)多張連續(xù)預(yù)處理后的CT幀切片序列進(jìn)行肺實(shí)質(zhì)分割;接著,對(duì)預(yù)處理后的CT幀序列利用形態(tài)學(xué)方法進(jìn)行疑似結(jié)節(jié)區(qū)域的分割;最后,根據(jù)醫(yī)生提供的真陽性結(jié)節(jié)的空間位置和規(guī)律,提出了基于肺邊緣距離的最小距離算法,對(duì)肺實(shí)質(zhì)中的疑似病變區(qū)域進(jìn)行篩選和檢測,最終實(shí)現(xiàn)對(duì)肺結(jié)節(jié)進(jìn)行分類,以及疑似結(jié)節(jié)的初步定位,從而提高肺癌早期診斷的準(zhǔn)確率,降低假陽性率,提高閱片診斷效率和減輕放射科醫(yī)生的工作量。
[Abstract]:Lung cancer is currently known as the world's number one cancer, and the incidence has been rising, especially in China. Studies show that early diagnosis and treatment can improve the cure rate of lung cancer patients. In recent years, with the widespread use of various medical imaging equipment. Various advanced techniques of medical equipment imaging have been paid more and more attention by medical workers. The location and characterization of pulmonary nodules depend on the application of these techniques. More and more researchers and doctors pay more and more attention to the computer aided detection and recognition system of pulmonary nodules in chest CT image. Because of the uneven gray value individual difference and artifact in medical image itself. Because of the noise, edge blur and so on, it is very difficult to achieve high sensitivity and accuracy in the medical image processing algorithm. The object of this study is based on low dose chest CT images. In the aspects of CT image preprocessing, lung parenchyma segmentation, suspected pulmonary nodule extraction and pulmonary nodule detection, we have carried out experiments and studies. The original data is changed to format, and the noise problem in CT image imaging is analyzed. The fast adaptive weighted median filter (Faster Weighted Median filter) is introduced for preprocessing. Secondly, in order to narrow down the target range and the existence of abnormal images in the original data, it is difficult to get better segmentation results based on single slice processing. An automatic region growth method is proposed to segment the lung parenchyma of CT frame slices after continuous preprocessing. Then, the preprocessed CT frame sequence is segmented by morphological method. Finally, according to the spatial position and rule of true positive nodules provided by doctors, a minimum distance algorithm based on lung edge distance is proposed to screen and detect suspected lesion areas in lung parenchyma. Finally, the classification of pulmonary nodules and the initial localization of suspected nodules are realized, so as to improve the accuracy of early diagnosis of lung cancer, reduce the false positive rate, improve the efficiency of X-ray diagnosis and lighten the workload of radiologists.
【學(xué)位授予單位】：西南交通大學(xué)
【學(xué)位級(jí)別】：碩士
【學(xué)位授予年份】：2017
【分類號(hào)】：R734.2;TP391.41

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