本文選題：人腦MR + 多圖譜��； 參考：《寧夏大學(xué)》2017年碩士論文

【摘要】：人體大腦結(jié)構(gòu)復(fù)雜且功能各異,如海馬體、扁桃體、顳上回、小腦、腦干、尾狀核等關(guān)鍵腦結(jié)構(gòu)與多種腦部疾病息息相關(guān),對(duì)其精準(zhǔn)分割是臨床診斷中醫(yī)生進(jìn)行相關(guān)定量分析的前提,因此多圖譜分割技術(shù)成為了當(dāng)前國(guó)內(nèi)外的研究重點(diǎn)。多圖譜分割技術(shù)主要包括兩個(gè)關(guān)鍵步驟,分別為圖像配準(zhǔn)和標(biāo)記融合。將多個(gè)圖譜與目標(biāo)圖像進(jìn)行配準(zhǔn)并選擇合適的標(biāo)記融合算法對(duì)配準(zhǔn)后的圖譜進(jìn)行融合得到最終的分割結(jié)果。為了使得分割的結(jié)果更準(zhǔn)確,需要選擇合適的標(biāo)記融合算法,以便于配準(zhǔn)后的圖像在融合過(guò)程中實(shí)現(xiàn)高精度,從而對(duì)每個(gè)初始分割中的信息進(jìn)行有效的提取,使得最終的分割結(jié)果具有代表性。標(biāo)記融合方法中用的比較廣泛的有多數(shù)表決算法(Majority Voting,MV)[1]、STAPLE算法[2](Simultaneous Truth and Performance Level Estimation)和COLLATE算法[3](Consensus Level,Labeler accuracy and Truth Estimation)等。MV沒(méi)有考慮到各個(gè)分割圖像的差異性,STAPLE算法沒(méi)有利用圖像的先驗(yàn)信息。為了獲得更高的分割精度,本文首先對(duì)腦部MR圖像進(jìn)行預(yù)處理,包括顱骨剔除、濾波、灰度歸一化以及直方圖匹配等處理并對(duì)多個(gè)組織進(jìn)行配準(zhǔn),然后對(duì)基于配準(zhǔn)的多圖譜融合算法進(jìn)行深入的研究并進(jìn)行改進(jìn),主要內(nèi)容如下:(1)圍繞人腦MR圖像,研究分析了當(dāng)前使用比較廣泛的MV融合算法和STAPLE融合算法,并使用這兩種方法對(duì)配準(zhǔn)后的腦部圖像的多個(gè)組織進(jìn)行融合,同時(shí)選擇與金標(biāo)準(zhǔn)的相似性測(cè)度作為融合結(jié)果的評(píng)價(jià)標(biāo)準(zhǔn),將這兩種方法融合的結(jié)果與最優(yōu)單圖譜分割結(jié)果進(jìn)行比較。(2)在MV融合算法的基礎(chǔ)上提出一種新的加權(quán)改進(jìn)融合算法(Weight-Voting),利用圖譜和目標(biāo)圖像之間的相似性測(cè)度作為圖像融合的權(quán)重,并分別對(duì)多個(gè)配準(zhǔn)后的腦部組織進(jìn)行融合,并將本文算法分別與最優(yōu)單圖譜、MV、STAPLE融合算法進(jìn)行了比較。實(shí)驗(yàn)結(jié)果表明,多圖譜分割方法分割精度要高于最優(yōu)單圖譜分割方法,本文提出的新融合改進(jìn)算法性能優(yōu)于最優(yōu)單圖譜、MV以及STAPLE融合算法,驗(yàn)證了本文提出的算法在醫(yī)學(xué)圖像分割方面的有效性和準(zhǔn)確性。
[Abstract]:The complex and diverse structure of the human brain, such as the hippocampus, tonsils, superior temporal gyrus, cerebellum, brain stem, caudate nucleus, and other key brain structures are closely related to a variety of brain diseases. Accurate segmentation is the premise of quantitative analysis for doctors in clinical diagnosis, so multi-spectrum segmentation technology has become the focus of research at home and abroad. Multi-spectrum segmentation includes two key steps: image registration and label fusion. Finally, the final segmentation results are obtained by matching multiple maps with target images and selecting the appropriate label fusion algorithm. In order to make the segmentation result more accurate, it is necessary to select the appropriate label fusion algorithm, so that the registration image can achieve high accuracy in the fusion process, so that the information in each initial segmentation can be extracted effectively. The final segmentation results are representative. The majority voting algorithm (Majority VotingMV) [1] is a simple truth and performance level estimation algorithm [2] and a consensus level estimation algorithm [3]. MV does not take into account the difference of each segmented image and the prior information of the image. In order to achieve higher segmentation accuracy, the brain Mr image is preprocessed, including skull removal, filtering, gray normalization and histogram matching, and registration of multiple tissues is carried out. Then the multi-map fusion algorithm based on registration is deeply studied and improved. The main contents are as follows: 1) focusing on the human brain Mr image, the current widely used MV fusion algorithm and STAPLE fusion algorithm are studied and analyzed. The two methods are used to fuse multiple tissues of the brain image after registration, and the similarity measure with the gold standard is chosen as the evaluation criterion of the fusion results. Comparing the results of these two methods with the results of optimal single map segmentation, we propose a new weighted improved fusion algorithm, Weight-Votingn, based on the MV fusion algorithm. The similarity measure between the map and the target image is used as the measure of the similarity between the map and the target image. The weight of image fusion, The fusion of multiple brain tissues after registration was performed, and the proposed algorithm was compared with the optimal single map MVS-STAPLE fusion algorithm. The experimental results show that the segmentation accuracy of the multi-map segmentation method is higher than that of the optimal single-map segmentation method, and the performance of the improved fusion algorithm proposed in this paper is better than that of the optimal single-map MV and STAPLE fusion algorithms. The validity and accuracy of the proposed algorithm in medical image segmentation are verified.
【學(xué)位授予單位】：寧夏大學(xué)
【學(xué)位級(jí)別】：碩士
【學(xué)位授予年份】：2017
【分類號(hào)】：TP391.41

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