本文選題：微創(chuàng)手術(shù)　切入點(diǎn)：內(nèi)窺鏡視覺SLAM　出處：《電子科技大學(xué)》2017年碩士論文　論文類型：學(xué)位論文

【摘要】：內(nèi)窺鏡視覺即時定位與地圖創(chuàng)建(Simulation Localization and Mapping,SLAM)是在動態(tài)微創(chuàng)手術(shù)環(huán)境下基于內(nèi)窺鏡實(shí)時影像同步完成對軟組織環(huán)境的三維動態(tài)重建和內(nèi)窺鏡自身運(yùn)動的估計(jì),即獲取軟組織表面特征在對應(yīng)場景中的三維空間信息,并通過內(nèi)窺鏡與場景特征的相對位置關(guān)系來確定內(nèi)窺鏡在同一空間坐標(biāo)中的位姿。微創(chuàng)手術(shù)特殊復(fù)雜的環(huán)境,如術(shù)中光照不均、軟組織流血、診療煙霧、軟組織形變、軟組織表面強(qiáng)邊緣圖像特征缺失以及濕性軟組織表面的高度鏡面反射等,對內(nèi)窺鏡視覺SLAM方法的實(shí)時性和魯棒性提出較高要求。針對以上問題,本文開展了微創(chuàng)手術(shù)中內(nèi)窺鏡視覺SLAM方法的研究,具體內(nèi)容如下:1.本文搭建了基于概率估計(jì)的內(nèi)窺鏡視覺SLAM框架,主要包括內(nèi)窺鏡及軟組織運(yùn)動建模、內(nèi)窺鏡視覺測量模型以及擴(kuò)展卡爾曼濾波算法。2.軟組織特征測量包括軟組織特征提取和特征匹配兩個部分。對于軟組織的特征提取,本文創(chuàng)新性地應(yīng)用ORB(Oriented Brief)特征提取方法,保證了提取的軟組織特征在微創(chuàng)手術(shù)復(fù)雜環(huán)境中的穩(wěn)定性;為了獲得更好的實(shí)時性,本文對內(nèi)窺鏡圖像進(jìn)行了柵格區(qū)域劃分,根據(jù)區(qū)域特征分布參數(shù),提取數(shù)目穩(wěn)定且分布均勻的軟組織特征。對于軟組織的特征匹配,提出軟組織特征匹配區(qū)域的主動搜索方法,避免全局搜索,從而大大地減小了特征匹配的計(jì)算復(fù)雜度。最后,將上述軟組織特征測量方法代入SLAM框架,并與已有特征測量方法進(jìn)行對比,實(shí)驗(yàn)結(jié)果表明,本文提出的軟組織特征測量方法具有良好的魯棒性以及廣泛適用性。3.針對擴(kuò)展卡爾曼濾波存在的缺陷,本文提出基于1點(diǎn)隨機(jī)抽樣一致性(1-point Random Sample Consensus,1-pointRANSAC)的改進(jìn)擴(kuò)展卡爾曼濾波算法,刪除誤匹配的同時補(bǔ)救了有用的軟組織特征信息,使得內(nèi)窺鏡定位更精確,并且結(jié)合實(shí)驗(yàn)對其進(jìn)行了可行性分析。最后,本文基于達(dá)芬奇手術(shù)機(jī)器人在真實(shí)微創(chuàng)手術(shù)環(huán)境下采集的內(nèi)窺鏡圖像數(shù)據(jù),對本文所提出的內(nèi)窺鏡視覺SLAM方法進(jìn)行了驗(yàn)證。實(shí)驗(yàn)結(jié)果表明,該方法可以有效應(yīng)對軟組織形變對定位和三維重建的動態(tài)干擾,軟組織特征測量的不確定性橢圓區(qū)域能夠最終收斂在估計(jì)值附近,并且該方法利用較少的軟組織特征信息就能實(shí)現(xiàn)內(nèi)窺鏡的準(zhǔn)確定位,同時能實(shí)現(xiàn)內(nèi)窺鏡序列圖片7Hz的實(shí)時處理速度。
[Abstract]:Simulation Localization and mapping (slam) of endoscope visual real-time location and map creation is to synchronize the 3D dynamic reconstruction of soft tissue environment and the estimation of endoscope motion under dynamic minimally invasive surgery environment based on real-time image of endoscope. That is, the 3D spatial information of soft tissue surface features in the corresponding scene is obtained, and the position and pose of the endoscope in the same space coordinate are determined by the relative position relationship between the endoscope and the scene feature. Such as uneven illumination, soft tissue bleeding, diagnosis and treatment smoke, soft tissue deformation, absence of strong edge image features on soft tissue surface, and high specular reflex on wet soft tissue surface, etc. High requirements for real-time and robustness of endoscope visual SLAM method are put forward. In view of the above problems, the research of endoscope visual SLAM method in minimally invasive surgery is carried out in this paper. The main contents are as follows: 1. In this paper, we set up an endoscope visual SLAM framework based on probability estimation, which mainly includes endoscope and soft tissue motion modeling. Endoscopic vision measurement model and extended Kalman filter algorithm .2. soft tissue feature measurement includes soft tissue feature extraction and feature matching. For soft tissue feature extraction, this paper innovatively applies ORB(Oriented briefing feature extraction method. The stability of the extracted soft tissue features in the complex environment of minimally invasive surgery is guaranteed. In order to obtain better real-time performance, the image of endoscope is divided into raster regions according to the regional feature distribution parameters. For soft tissue feature matching, an active search method for soft tissue feature matching region is proposed to avoid global search, thus greatly reducing the computational complexity of feature matching. The soft tissue feature measurement method mentioned above is put into SLAM frame and compared with the existing feature measurement method. The experimental results show that, The soft tissue feature measurement method proposed in this paper has good robustness and wide applicability. 3. In view of the shortcomings of extended Kalman filter, an improved extended Kalman filter algorithm based on 1-point random sampling consistency and 1-point Random Sample Consensus1-pointRANSAC is proposed. Removing mismatch and repairing useful soft tissue feature information make endoscope localization more accurate. Finally, the feasibility analysis is carried out on the basis of experiments. Based on the image data collected by Leonardo da Vinci surgical robot in a real minimally invasive operation environment, the SLAM method proposed in this paper is verified. The experimental results show that, This method can effectively deal with the dynamic interference of soft tissue deformation on localization and 3D reconstruction, and the uncertain elliptical region of soft tissue feature measurement can finally converge near the estimated value. Using less soft tissue feature information, the method can locate endoscope accurately and realize the real-time processing speed of 7Hz.
【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】：R616;TP391.41

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