本文選題：圖像配準(zhǔn) + 大形變圖像��； 參考：《華東師范大學(xué)》2017年碩士論文

【摘要】：大形變圖像配準(zhǔn)在計(jì)算機(jī)圖像處理尤其醫(yī)學(xué)圖像處理中有重要的研究?jī)r(jià)值和應(yīng)用意義。由于在配準(zhǔn)過(guò)程中形變量較大,傳統(tǒng)的Demons算法僅僅利用圖像的梯度信息驅(qū)使像素朝梯度下降的方向擴(kuò)散,使圖像發(fā)生形變,導(dǎo)致在圖像平坦或者邊緣處梯度信息缺失的情況下很難完成配準(zhǔn)。因此,本文提出了基于驅(qū)動(dòng)力的Log-Demons算法,首先通過(guò)提取圖像的結(jié)構(gòu)張量作為約束,使配準(zhǔn)過(guò)程保持更多的結(jié)構(gòu)信息,其次利用基于描述子匹配獲得的驅(qū)動(dòng)力提高圖像在大形變情況下的配準(zhǔn)精度,避免算法配準(zhǔn)陷入局部最優(yōu),最終構(gòu)建了基于Log-Demons算法的大形變圖像高精度配準(zhǔn)模型。本文的主要工作包括:1)提出基于結(jié)構(gòu)張量的Log-Demons算法:圖像的結(jié)構(gòu)張量能提取更多的局部結(jié)構(gòu)信息,并且對(duì)外部光照變化不敏感。通過(guò)張量守恒準(zhǔn)則將其融合進(jìn)Log-Demons算法中來(lái)約束像素點(diǎn)的擴(kuò)散,使其能保持良好的局部結(jié)構(gòu)并獲得更精確的形變場(chǎng)。2)提出基于驅(qū)動(dòng)力的Log-Demons算法:為了能實(shí)現(xiàn)大形變圖像高精度配準(zhǔn),本文提出將邊緣點(diǎn)匹配算法獲得的矢量位移作為驅(qū)動(dòng)力,拉動(dòng)圖像邊緣周?chē)袼攸c(diǎn)的運(yùn)動(dòng),使其朝正確的方向擴(kuò)散,解決了大形變情況下圖像邊緣梯度相同導(dǎo)致像素點(diǎn)隨機(jī)擴(kuò)散的問(wèn)題,使其能夠適應(yīng)大形變圖像配準(zhǔn)。3)提出基于MROGH描述子匹配獲得驅(qū)動(dòng)力的算法:為了獲得更準(zhǔn)確的驅(qū)動(dòng)力,并適應(yīng)大形變帶來(lái)的大角度變化,本文提出采用MROGH構(gòu)建描述子并對(duì)其進(jìn)行一對(duì)一的精確匹配以獲得匹配點(diǎn)的位移向量作為驅(qū)動(dòng)力。4)提出基于特征驅(qū)動(dòng)的Log-Demons融合算法:將驅(qū)動(dòng)力作為常量單獨(dú)計(jì)算,在迭代配準(zhǔn)的過(guò)程中以指數(shù)減少的方式與Log-Demons的更新形變場(chǎng)相融合,使其在形變開(kāi)始時(shí)給予較大的影響力,形變減小時(shí)由Log-Demons自身的驅(qū)動(dòng)力完成配準(zhǔn),既加快配準(zhǔn)過(guò)程又減少了特征點(diǎn)誤匹配所帶來(lái)的影響。在更新形變場(chǎng)計(jì)算過(guò)程中,本文提出在李群中利用群性質(zhì)融合驅(qū)動(dòng)力和Log-Demons本身的更新形變場(chǎng),使融合后的形變場(chǎng)也能保持微分同胚。通過(guò)算法(1)-(4),最終構(gòu)建了基于驅(qū)動(dòng)力的Log-Demons算法模型,實(shí)現(xiàn)了大形變圖像配準(zhǔn)。本文方法在模擬形變圖像、真實(shí)場(chǎng)景大位移圖像以及腦圖像上進(jìn)行了測(cè)試,并與其它方法(如Log-Demons、SpectralLog-Demons、LDDMM)進(jìn)行了比較,實(shí)驗(yàn)表明本文方法都取得了較好的效果。本文方法不僅能較好地完成大形變圖像配準(zhǔn),而且計(jì)算得到的形變場(chǎng)具有更好的局部結(jié)構(gòu)和精度。
[Abstract]:Large deformation image registration has important research value and application significance in computer image processing, especially in medical image processing. Because of the large amount of deformation in the registration process, the traditional Demons algorithm only uses the gradient information of the image to drive the pixel to the direction of gradient descent, which causes the image to deform. It is difficult to complete registration when the image is flat or the gradient information at the edge is missing. Therefore, a driving force based Log-Demons algorithm is proposed in this paper. Firstly, by extracting the structure Zhang Liang of the image as the constraint, the registration process can keep more structure information. Secondly, the driving force based on descriptor matching is used to improve the image registration accuracy in the case of large deformation, and avoid the local optimal registration. Finally, a high-precision registration model of large deformation image based on Log-Demons algorithm is constructed. The main work of this paper includes: 1) A Log-Demons algorithm based on structural Zhang Liang is proposed: the structure tensor of the image can extract more local structure information and is not sensitive to the external illumination change. The Zhang Liang conservation criterion is used to integrate it into the Log-Demons algorithm to constrain the spread of pixels. The Log-Demons algorithm based on driving force is proposed. In order to achieve high precision registration of large deformation image, the vector displacement obtained by edge point matching algorithm is used as the driving force in this paper. The motion of pixels around the edge of the image is pulled to spread in the right direction, which solves the problem of random diffusion of pixels caused by the same gradient of the edge of the image under the condition of large deformation. So that it can adapt to large deformation image registration. 3) an algorithm for obtaining driving force based on MROGH descriptor matching is proposed: in order to obtain more accurate driving force and adapt to large angle change caused by large deformation, In this paper, we propose a one-to-one exact matching of the descriptor using MROGH to obtain the displacement vector of the matching point as the driving force. 4) A feature-based Log-Demons fusion algorithm is proposed: the driving force is calculated separately as a constant. In the process of iterative registration, the updating deformation field of Log-Demons is merged in the way of exponential reduction, so that it can give great influence at the beginning of deformation. When the deformation decreases, the registration is completed by the driving force of Log-Demons itself. It not only speeds up the registration process but also reduces the effect of feature point mismatch. In the course of calculating the updated deformation field, it is proposed that the fusion of the driving force of group property and the updated deformation field of Log-Demons itself can keep the differential homeomorphism in Li Qun. Finally, the Log-Demons algorithm model based on driving force is constructed, and the large deformation image registration is realized. The present method is tested on simulated deformation images, real scene large displacement images and brain images, and compared with other methods such as Log-Demonsn SpectralLog-Demonsln LDDMMM. The experimental results show that the proposed method achieves good results. In this paper, not only the large deformation image registration can be completed, but also the calculated deformation field has better local structure and accuracy.
【學(xué)位授予單位】：華東師范大學(xué)
【學(xué)位級(jí)別】：碩士
【學(xué)位授予年份】：2017
【分類(lèi)號(hào)】：TP391.41

【參考文獻(xiàn)】

相關(guān)期刊論文 前2條

1 張桂梅;曹紅洋;儲(chǔ)s，

本文編號(hào)：1841355