本文選題：非剛性配準(zhǔn) + LBFGS算法�。� 參考：《太原理工大學(xué)》2017年碩士論文

【摘要】：圖像配準(zhǔn)技術(shù)發(fā)展到今天也不過三十多年的歷史,由于計(jì)算機(jī)硬件與醫(yī)學(xué)成像技術(shù)突飛猛進(jìn)的發(fā)展,不斷地涌現(xiàn)出許多信息各異的醫(yī)學(xué)圖像,為臨床診斷提供了豐富的資料。由于各種醫(yī)療儀器的成像原理不同,導(dǎo)致提供的醫(yī)學(xué)信息也就大不相同,然而單模態(tài)圖像為醫(yī)生的臨床診斷提供的都是零散片面的信息,要想得到更全面完整且互補(bǔ)的圖像信息,就必須將攜帶各種不同類型信息的多種模態(tài)圖像融合在一起,以便醫(yī)生做出更準(zhǔn)確可靠的診斷。當(dāng)前國內(nèi)外在剛性配準(zhǔn)領(lǐng)域的技術(shù)已經(jīng)成熟,但事實(shí)上人體器官組織通常都存在各種復(fù)雜的非線性形變,只有非剛性配準(zhǔn)才能滿足這方面要求。因此,醫(yī)學(xué)圖像非剛性配準(zhǔn)在臨床治療上尤為關(guān)鍵,具有十分重要的現(xiàn)實(shí)應(yīng)用意義,必將成為未來醫(yī)學(xué)數(shù)字技術(shù)的熱點(diǎn)。本文主要研究非剛性圖像配準(zhǔn)關(guān)鍵技術(shù)的兩大模塊,一是反映兩幅圖像之間空間關(guān)系的變換模型,一是度量兩幅圖像是否達(dá)到完全配準(zhǔn)的相似性測(cè)度。具體的研究內(nèi)容有以下幾個(gè)方面:(1)研究了配準(zhǔn)的整個(gè)框架、流程及具體步驟,詳細(xì)歸納并整理總結(jié)了圖像配準(zhǔn)中的空間變換、灰度插值、相似性測(cè)度和優(yōu)化算法四大關(guān)鍵技術(shù),重點(diǎn)闡述了樣條函數(shù)模型、LBFGS優(yōu)化算法的搜索原理以及基于空間變換和物理模型的非剛性圖像配準(zhǔn)方法。(2)針對(duì)存在非線性形變的圖像,采用B樣條變換可以很好地?cái)M合圖像間的不規(guī)則形變,直觀上看可以得到較佳的配準(zhǔn)效果;針對(duì)B樣條控制網(wǎng)格間距的選取具有隨機(jī)性,無法很好地權(quán)衡配準(zhǔn)精度與效率的問題,提出了基于多層次B樣條變換模型的醫(yī)學(xué)圖像非剛性配準(zhǔn)方法;針對(duì)多層次B樣條的均勻形變場(chǎng)無法很好地模擬圖像局部區(qū)域大形變的問題,提出了基于局部區(qū)域多層次B樣條變換模型的非剛性配準(zhǔn)方法。(3)互信息的測(cè)度在配準(zhǔn)前不用進(jìn)行任何特征提取、檢測(cè)、分割等預(yù)處理操作,僅需計(jì)算兩幅圖像各自信息熵和聯(lián)合信息熵,確實(shí)簡單方便易行,但也正因如此忽略了圖像本身存在的任何空間信息,影響了配準(zhǔn)結(jié)果的精度與魯棒性,因此本文引進(jìn)一種把圖像本身信息置于很高地位的局部互信息概念,詳細(xì)地分析了局部互信息的原理、計(jì)算方法與步驟,并提出了基于局部互信息的圖像配準(zhǔn)方法,進(jìn)行了對(duì)比仿真實(shí)驗(yàn),結(jié)果表明,提出的方案有效的提高了配準(zhǔn)的精確性和魯棒性,但也存在時(shí)間代價(jià)大的缺陷。(4)進(jìn)行基于B樣條和局部互信息的前列腺圖像非剛性配準(zhǔn)的應(yīng)用研究,從總體上闡述使用的配準(zhǔn)算法及配準(zhǔn)過程的步驟;然后,介紹配準(zhǔn)使用的關(guān)鍵主函數(shù)框架以及配準(zhǔn)用到的圖形用戶界面;最后,對(duì)比試驗(yàn)結(jié)果,提出的方案對(duì)存在非線性無規(guī)則形變的醫(yī)學(xué)圖像有很好的配準(zhǔn)效果。
[Abstract]:Image registration technology has been developed for more than 30 years. Because of the rapid development of computer hardware and medical imaging technology, many medical images with different information have been emerging, which provides abundant information for clinical diagnosis.Because of the different imaging principles of various medical instruments, the medical information provided is very different. However, the single mode image provides the doctor's clinical diagnosis with scattered and one-sided information.In order to obtain more complete and complementary image information, it is necessary to fuse a variety of modal images with different types of information, so that doctors can make more accurate and reliable diagnosis.At present, the technology of rigid registration has been mature at home and abroad, but in fact, human organs and tissues usually have a variety of complex nonlinear deformation, only non-rigid registration can meet this requirement.Therefore, non-rigid registration of medical images is very important in clinical treatment and has very important practical significance. It will become a hot spot of medical digital technology in the future.This paper mainly studies two modules of the key technology of non-rigid image registration, one is the transformation model which reflects the spatial relationship between the two images, the other is to measure whether the two images achieve the similarity measure of complete registration.The main contents of this paper are as follows: (1) the whole frame, flow and concrete steps of registration are studied, and the four key techniques of image registration, such as spatial transformation, gray interpolation, similarity measure and optimization algorithm, are summarized and summarized in detail.The search principle of the spline function model LBFGS optimization algorithm and the non-rigid image registration method based on spatial transformation and physical model.The irregular deformation between images can be well fitted by B-spline transform, and the registration effect can be obtained intuitively, and the selection of B-spline control mesh spacing is random.This paper presents a non-rigid medical image registration method based on multi-level B-spline transform model, which is unable to balance the accuracy and efficiency of registration.In view of the problem that the uniform deformation field of multilevel B-spline can not well simulate the large deformation in the local region of the image,A non-rigid registration method based on local region multi-level B-spline transform model is proposed. The measure of mutual information does not need any pre-processing operations such as feature extraction, detection and segmentation before registration.It is simple and convenient to calculate the information entropy and joint information entropy of the two images, but because of this, any spatial information that exists in the image itself is ignored, which affects the accuracy and robustness of the registration results.Therefore, this paper introduces a concept of local mutual information, which places the image itself in a very high position, analyzes in detail the principle, calculation methods and steps of local mutual information, and proposes an image registration method based on local mutual information.The simulation results show that the proposed scheme can effectively improve the accuracy and robustness of registration.However, there is also a time-cost defect. (4) to study the application of non-rigid registration of prostate image based on B-spline and local mutual information, and to describe the registration algorithm and the steps of registration process.The key principal function framework and the graphical user interface used in registration are introduced. Finally, compared with the experimental results, the proposed scheme has a good registration effect for medical images with nonlinear irregular deformation.
【學(xué)位授予單位】：太原理工大學(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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