本文選題：圖像配準　切入點：非剛性配準　出處：《光學精密工程》2017年09期 　論文類型：期刊論文

【摘要】：考慮傳統(tǒng)非剛性圖像配準方法無法同時滿足配準精度和配準時間要求,綜合圖像的特征和灰度信息,提出了幾種改進的非剛性圖像配準方法:基于圓形描述子特征的非剛性配準方法(Circle Descriptor Feature,CDF),基于動態(tài)驅動力Demons的非剛性配準方法(Dynamic Driving Force Demons,DDFD),和基于圖像特征和光流場的非剛性配準方法。CDF方法通過提取圖像的特征點,采用圓形描述子代替?zhèn)鹘y(tǒng)方法的正方形描述子來保證圖像的旋轉不變性,提高配準速度;DDFD方法通過引入驅動力系數(shù)動態(tài)改變驅動力,有效地解決了傳統(tǒng)方法配準時間和配準精度低的問題;基于圖像特征和光流場的非剛性配準方法則首先提取浮動圖像和參考圖像的特征點,然后利用提取的特征點進行粗配準(特征級配準),再采用基于光流場的方法進行精細配準(像素級配準),最終實現(xiàn)配準精度和配準時間的兼顧。對checkboard測試圖像、自然圖像、腦部MR圖像、肝部CT圖像進行了實驗測試,結果表明,本文方法在配準時間、配準精度及對大形變圖像的適應性方面均優(yōu)于傳統(tǒng)尺度不變特征轉換(SIFT)、加速魯棒特征(SURF)、Demons、Active Demons和全變差正則項-L~1范數(shù)項(TV-L~1)等方法。
[Abstract]:Considering that the traditional non-rigid image registration method can not meet the registration accuracy and registration time requirements at the same time, the image features and gray level information are synthesized. Several improved non-rigid image registration methods are proposed: circle Descriptor feature based non-rigid registration method based on circular descriptor feature, dynamic Driving Force Demonsd FDF based on dynamic driving force Demons, and image feature and optical flow field. The non-rigid registration method. CDF method extracts the feature points of the image. The circular descriptor is used to replace the square descriptor of the traditional method to ensure the rotation invariance of the image, and the dynamic driving force is changed by introducing the driving force coefficient to improve the registration speed. It effectively solves the problems of low registration time and registration accuracy in traditional methods, and the non-rigid registration method based on image feature and optical flow field firstly extracts the feature points of floating image and reference image. Then the extracted feature points are used for rough registration (feature gradation), and then fine registration (pixel gradation) based on optical flow field is used to achieve both registration accuracy and registration time. Mr images of brain and CT images of liver were tested experimentally. The results showed that the registration time of this method, The registration accuracy and adaptability to large deformation images are better than those of the traditional scale invariant feature conversion (SIFT), and the methods of accelerating robust features such as robust Demons and TV-L ~ (1)) are also discussed in this paper, and the results are as follows: (1) the accuracy of registration and the adaptability to large deformation images are better than those of the traditional scale-invariant feature conversion (SIFT).
【作者單位】： 山東大學(威海)機電與信息工程學院;
【基金】：國家自然科學基金資助項目(No.81671848,No.81371635) 山東省重點研發(fā)計劃資助項目(No:2016GGX101017)
【分類號】：TP391.41

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