【摘要】：現(xiàn)如今,醫(yī)學(xué)成像系統(tǒng)已成為臨床與醫(yī)學(xué)研究中不可或缺的輔助工具,隨著醫(yī)學(xué)成像技術(shù)的飛速發(fā)展,促進(jìn)了計(jì)算機(jī)輔助診斷技術(shù)在醫(yī)學(xué)研究和臨床實(shí)驗(yàn)方面需求龐大且發(fā)展迅速,而計(jì)算機(jī)輔助診斷技術(shù)的基礎(chǔ)是醫(yī)學(xué)圖像的處理,所以醫(yī)學(xué)圖像處理技術(shù)是一個(gè)計(jì)算機(jī)科學(xué)與臨床醫(yī)學(xué)多學(xué)科相互交叉的研究熱點(diǎn)領(lǐng)域之一。隨著現(xiàn)代社會人口老年化進(jìn)程不斷加快,而腦萎縮又是老年人十分常見的疾病,這導(dǎo)致了醫(yī)務(wù)人員工作負(fù)擔(dān)的不斷增加。為了解決這個(gè)問題,可充分發(fā)揮和利用現(xiàn)代計(jì)算機(jī)速度快、效率高和成本低的優(yōu)勢。因此,本文對計(jì)算機(jī)輔助診斷腦萎縮技術(shù)展開研究。在提出腦萎縮輔助診斷系統(tǒng)的基礎(chǔ)之上,先對原始腦部醫(yī)學(xué)圖像的預(yù)處理進(jìn)行研究,然后討論腦實(shí)質(zhì)的提取方法,最后重點(diǎn)展開對腦實(shí)質(zhì)分割和腦體積測量技術(shù)的研究討論。論文主要研究成果包括:(1)利用現(xiàn)代圖像去噪的技術(shù),通過實(shí)驗(yàn)總結(jié)出一套適合腦部醫(yī)學(xué)圖像的去噪流程及方法。(2)通過分析腦部醫(yī)學(xué)圖像的特性和人腦的組織結(jié)構(gòu),創(chuàng)新使用多重閾值分割算法實(shí)現(xiàn)腦實(shí)質(zhì)的提取。(3)通過分析高斯混合模型和K-means兩種經(jīng)典的聚類算法分割腦實(shí)質(zhì)存在的不足來加以改進(jìn)和融合,創(chuàng)新使用融合后的GKA算法分割腦實(shí)質(zhì)。(4)提出兩種不同的腦實(shí)質(zhì)片面積和體積測量的方案,并針對兩種不同的方案分別做出實(shí)驗(yàn)對比。在使用臨床真實(shí)的腦部醫(yī)學(xué)圖像進(jìn)行試驗(yàn)后得出結(jié)論:本文提出的關(guān)于醫(yī)學(xué)圖像去噪流程和腦實(shí)質(zhì)提取算法均獲得較理想的實(shí)驗(yàn)結(jié)果;在腦實(shí)質(zhì)分割方面,GKA方法分割的結(jié)果更是在各項(xiàng)指標(biāo)中比傳統(tǒng)的高斯混合模型和K-means聚類算法要全面領(lǐng)先;在腦實(shí)質(zhì)的片面積測量方面,由于論文提出的兩種測量方法是基于對腦實(shí)質(zhì)片面積的不同定義,從而導(dǎo)致兩種方法得出的結(jié)果存在一定的差異,但是這兩者的結(jié)果都具有一定的臨床參考價(jià)值。
[Abstract]:Nowadays, medical imaging system has become an indispensable assistant tool in clinical and medical research. With the rapid development of medical imaging technology, It has promoted the development of computer-aided diagnosis technology in medical research and clinical experiment, and the basis of computer-aided diagnosis technology is medical image processing. Therefore, medical image processing technology is one of the hot research fields of computer science and clinical medicine. With the rapid aging of population in modern society, brain atrophy is a common disease in the elderly, which leads to the increasing burden of medical workers. In order to solve this problem, we can make full use of the advantages of high speed, high efficiency and low cost of modern computer. Therefore, the computer-aided diagnosis of brain atrophy is studied in this paper. On the basis of putting forward the assistant diagnosis system of brain atrophy, the preprocessing of the original brain medical image is studied, then the extraction method of the brain parenchyma is discussed. Finally, the segmentation of the brain parenchyma and the measurement of the brain volume are emphatically discussed. The main research results are as follows: (1) by using modern image denoising technology, a set of denoising processes and methods suitable for brain medical image are summarized through experiments. (2) by analyzing the characteristics of brain medical image and the structure of human brain, The multi-threshold segmentation algorithm is used to extract the brain parenchyma. (3) by analyzing the shortcomings of Gao Si's mixed model and K-means 's two classical clustering algorithms to improve and fuse the segmentation of brain parenchyma. The fused GKA algorithm is used to segment the brain parenchyma. (4) two different methods of measuring the area and volume of the brain parenchyma are proposed and compared with each other. After using the clinical real brain medical images, the conclusion is drawn: the proposed process of medical image denoising and the algorithm of extracting brain parenchyma have achieved satisfactory experimental results; In the aspect of brain parenchyma segmentation, the result of GKA segmentation is more advanced than the traditional Gao Si mixed model and K-means clustering algorithm in every index, and in the area measurement of brain parenchyma, Because the two measurement methods are based on the different definitions of the area of the brain parenchyma, there are some differences between the two methods, but the results of the two methods have some clinical reference value.
【學(xué)位授予單位】：電子科技大學(xué)
【學(xué)位級別】：碩士
【學(xué)位授予年份】：2017
【分類號】：TP391.41;R742

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