發(fā)布時(shí)間：2018-03-11 12:20

  本文選題：醫(yī)學(xué)圖像處理　切入點(diǎn)：機(jī)器學(xué)習(xí)　出處：《浙江大學(xué)》2017年碩士論文　論文類型：學(xué)位論文

【摘要】：自動(dòng)化醫(yī)學(xué)圖像的病變定位是計(jì)算機(jī)醫(yī)療輔助的一個(gè)關(guān)鍵技術(shù)。目前的方法中,需要醫(yī)生通過人工的方式對醫(yī)學(xué)圖形進(jìn)行診斷,毫無疑問,這需要巨大的人工成本。在現(xiàn)有的醫(yī)學(xué)數(shù)據(jù)庫中,存在著海量的數(shù)據(jù)。而計(jì)算機(jī)的輔助可以幫助醫(yī)生減少工作量,提高診斷的效率,這個(gè)課題一直以來都被作為一個(gè)重要的課題進(jìn)行探討。許多圖像相關(guān)的分割算法以及匹配模型被提出來,比如基于圖像灰度的算法,基于圖像梯度的算法,基于模板比配模型等等。但由于醫(yī)學(xué)圖像的復(fù)雜性,事實(shí)上,整個(gè)流程仍然需要醫(yī)生以交互式的方式對圖像進(jìn)行分割和定位,不能夠?qū)崿F(xiàn)大規(guī)模的醫(yī)學(xué)圖像處理。這篇論文針對肺部醫(yī)學(xué)CT圖像序列的輪廓畸變診斷建立了一套全自動(dòng)的診斷算法。這套算法主要包含輪廓掩膜提取算法和輪廓畸變判斷算法。實(shí)際產(chǎn)生病變的肺結(jié)構(gòu)中通常會(huì)存在許多病癥,輪廓畸變只是其中的一部分病癥,但無論要對何種病癥進(jìn)行診斷,都需要首先獲得肺輪廓的掩膜。這就是說輪廓掩膜提取算法為所有的診斷算法提供了前提性的輸入。輪廓畸變判斷算法基于提取算法的輸入,對輪廓建立子輪廓的特征向量。在訓(xùn)練數(shù)據(jù)中,通過交互完成對輪廓異常區(qū)域的信息標(biāo)注。通過機(jī)器學(xué)習(xí)獲得最后的診斷模型,對判定為異常的輪廓的異常區(qū)域進(jìn)行局部的高亮標(biāo)注。
[Abstract]:The localization of pathological changes in automatic medical images is a key technology of computer assisted medical treatment. In the present method, doctors are required to diagnose medical graphics manually, and there is no doubt that, There is a huge amount of data in existing medical databases, and computer support can help doctors reduce their workload and improve diagnostic efficiency. Many image segmentation algorithms and matching models have been proposed, such as image grayscale based algorithms, image gradient-based algorithms, image gradient-based algorithms. But because of the complexity of medical images, in fact, the whole process still needs doctors to segment and locate the images in an interactive way. Large scale medical image processing can not be realized. This paper establishes a set of automatic diagnosis algorithms for contour distortion diagnosis of lung medical CT image sequence. This set of algorithms mainly includes contour mask extraction algorithm and wheel. An algorithm for judging the profile distortion. There are usually many diseases in the lung structure that actually causes the disease. Contour distortion is just part of it, but whatever the diagnosis, It is necessary to obtain the mask of the lung contour first. This means that the contour mask extraction algorithm provides a leading input for all diagnostic algorithms. The contour distortion judgment algorithm is based on the input of the extraction algorithm. In the training data, the information of the contour anomaly region is annotated by interaction. The final diagnosis model is obtained by machine learning. Local highlighting of abnormal areas identified as abnormal contours is carried out.
【學(xué)位授予單位】：浙江大學(xué)
【學(xué)位級別】：碩士
【學(xué)位授予年份】：2017
【分類號】：R563;TP391.41

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本文編號：1598140