Image Registration Algorithm for Sequence Pathology Slices Of Pulmonary Nodule

Registration of pathological section images is an important part of three-dimensional reconstruction of sections. In this paper, a registration method was proposed to solve the problem of mismatching of pathological section images of pulmonary nodules. Firstly, rough matching is performed, and the feature points are extracted according to the scale-invariant feature transform (SIFT) algorithm. Then fast sparse coding (FSC) is used for fine matching to eliminate mismatched pairs. The algorithm presented in this paper is applied to the registration between sequence sections of pulmonary nodules. The experimental results show that the algorithm can effectively find more matching point pairs, accurately remove the false matching point pairs, and significantly improve the registration accuracy.

[1]  Xing Zhang,et al.  Salient Feature Region: A New Method for Retinal Image Registration , 2011, IEEE Transactions on Information Technology in Biomedicine.

[2]  Nasser Kehtarnavaz,et al.  Validation of Non-Rigid Registration Between Functional and Anatomical Magnetic Resonance Brain Images , 2008, IEEE Transactions on Biomedical Engineering.

[3]  Horst Karl Hahn,et al.  Registration of histological whole slide images guided by vessel structures , 2013, Journal of pathology informatics.

[4]  Nathan S. Netanyahu,et al.  An Efficient SIFT-Based Mode-Seeking Algorithm for Sub-Pixel Registration of Remotely Sensed Images , 2015, IEEE Geoscience and Remote Sensing Letters.

[5]  Maoguo Gong,et al.  A Novel Coarse-to-Fine Scheme for Automatic Image Registration Based on SIFT and Mutual Information , 2014, IEEE Transactions on Geoscience and Remote Sensing.

[6]  Rémy Prost,et al.  Robust Alignment of Prostate Histology Slices With Quantified Accuracy , 2013, IEEE Transactions on Biomedical Engineering.

[7]  Oleg Lobachev,et al.  Feature‐based multi‐resolution registration of immunostained serial sections , 2017, Medical Image Anal..

[8]  Maoguo Gong,et al.  Remote Sensing Image Registration With Modified SIFT and Enhanced Feature Matching , 2017, IEEE Geoscience and Remote Sensing Letters.

[9]  马文萍 A Novel Point-Matching Algorithm Based on Fast Sample Consensus for Image Registration , 2014 .

[10]  Matthijs C. Dorst Distinctive Image Features from Scale-Invariant Keypoints , 2011 .

[11]  Jane You,et al.  Non-rigid medical image registration using image field in Demons algorithm , 2019, Pattern Recognit. Lett..