發(fā)布時(shí)間：2018-06-29 04:07

  本文選題：肝臟灰度分離 + 腹部CT圖像��； 參考：《浙江大學(xué)》2017年碩士論文

【摘要】：隨著近幾年來(lái)肝臟切除技術(shù)的飛速發(fā)展和人們生活水平的提高,個(gè)體化治療是目前醫(yī)療的發(fā)展趨勢(shì),但是從肝臟二維醫(yī)學(xué)影像到三維模型的重建仍是一個(gè)未能完全解決的問題。圖像分割算法和三維軟件建模為解決這一問題提供了有效的途徑,其過程包括:肝臟原始數(shù)據(jù)處理、肝臟特征序列生成、肝臟模型分層重構(gòu)以及肝臟實(shí)體模型打印。本文針對(duì)人體肝臟三維建模關(guān)鍵技術(shù)展開深入研究,主要成果如下:研究了現(xiàn)有肝臟特征提取算法無(wú)法完全提取肝臟邊緣的理論根源,提出灰度分離的肝臟分割算法,加上對(duì)現(xiàn)有算法的整合和改進(jìn),成功地解決了肝臟邊緣特征提取不明顯以及無(wú)法只保留肝臟部分而不顯示其它組織的問題,從而得到了肝臟特征序列,使得肝臟三維重建過程簡(jiǎn)化、建模時(shí)間極大的縮減以及肝臟模型準(zhǔn)確性得到了進(jìn)一步的保證。在肝臟分割算法的研究過程中,利用水平集算法提取出肝臟輪廓特征,然后通過灰度分離算法對(duì)輪廓內(nèi)的區(qū)域進(jìn)行處理,最后針對(duì)區(qū)域增長(zhǎng)算法需要手動(dòng)選取種子點(diǎn)的弊端,對(duì)其進(jìn)行了改進(jìn),從而成功用改進(jìn)后的區(qū)域增長(zhǎng)算法自動(dòng)將灰度分離后的圖像變成僅含有肝臟特征而不含其它組織的圖像,使算法的效率和精度得到了提升。針對(duì)肝臟三維建模需要手動(dòng)修改蒙板、耗時(shí)巨大及精度無(wú)法保證等問題,分析了傳統(tǒng)建模方法的不足,結(jié)合圖像分割算法利用處理后的肝臟特征序列進(jìn)行三維重建的系統(tǒng),借助于Matlab提取并生成肝臟特征序列,導(dǎo)入Mimics后無(wú)需進(jìn)行閾值分割和蒙板手動(dòng)修改,選擇所有灰度值不為0的區(qū)域即可直接計(jì)算出三維模型,最后可將三維模型打印出肝臟實(shí)體,從而解決了肝臟三維建模的效率和精度等問題。根據(jù)上述研究?jī)?nèi)容,以甲、乙兩病人的腹部CT圖像進(jìn)行實(shí)驗(yàn)驗(yàn)證。首先,灰度分離算法處理效果良好,解決了肝臟邊緣特征無(wú)法自動(dòng)分割及分割效果不佳的問題;其次,先生成肝臟特征序列再進(jìn)行分層重構(gòu)的方法經(jīng)試驗(yàn)使得肝臟三維模型重建的時(shí)間大大縮短;最后,通過肝臟實(shí)體模型3D打印完成了整個(gè)實(shí)驗(yàn)最終驗(yàn)證。
[Abstract]:With the rapid development of hepatectomy technology and the improvement of people's living standard in recent years, individualized treatment is the development trend of medical treatment at present, but the reconstruction from two-dimensional medical image of liver to three-dimensional model is still a problem that can not be solved completely. Image segmentation algorithm and 3D software modeling provide an effective way to solve this problem. The process includes: liver raw data processing, liver feature sequence generation, liver model hierarchical reconstruction and liver entity model printing. In this paper, the key technologies of 3D modeling of human liver are deeply studied. The main results are as follows: firstly, the theoretical roots of the existing liver feature extraction algorithms that can not completely extract the liver edges are studied, and a liver segmentation algorithm based on gray-scale separation is proposed. Combined with the integration and improvement of the existing algorithms, the problem of liver edge feature extraction is solved successfully, and the liver feature sequence is obtained, which can not only retain the liver part without displaying other tissues. The 3D liver reconstruction process is simplified, the modeling time is greatly reduced and the accuracy of the liver model is further guaranteed. In the research process of liver segmentation algorithm, the level set algorithm is used to extract the liver contour features, and then the gray level separation algorithm is used to process the region in the contour. Finally, aiming at the drawbacks of the region growth algorithm, we need to manually select the seed points. The improved region growth algorithm is successfully used to automatically transform the gray-scale image into an image with only liver features and no other tissues, which improves the efficiency and accuracy of the algorithm. In order to solve the problems such as the need to modify the mask manually, the time consuming and the precision can not be guaranteed, the deficiency of the traditional modeling method is analyzed, and the 3D reconstruction system based on the processed liver feature sequence is combined with the image segmentation algorithm. With the help of Matlab, the liver feature sequence is extracted and generated. After importing mimics, the 3D model can be directly calculated by selecting all regions with gray value not equal to zero, and the 3D model can be printed out to the liver entity without the need of threshold segmentation and manual modification of the mask. Thus, the efficiency and accuracy of liver 3D modeling are solved. According to the above study, abdominal CT images of patients A and B were used for experimental verification. Firstly, the gray level separation algorithm has good processing effect, which solves the problems that the liver edge feature can not be segmented automatically and the segmentation effect is not good. The method of stratified reconstruction of liver characteristic sequence is used to shorten the time of liver 3D model reconstruction. Finally, the whole experiment is verified by 3D printing of liver entity model.
【學(xué)位授予單位】：浙江大學(xué)
【學(xué)位級(jí)別】：碩士
【學(xué)位授予年份】：2017
【分類號(hào)】：R816.5;TP391.41

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