Robust retinal image registration using expectation maximisation with mutual information

Retinal images (RI) are widely used to diagnose a variety of eye conditions and diseases such as myopia and diabetic retinopathy. They are inherently characterised by having nonuniform illumination and low-contrast homogeneous regions which represent a unique set of challenges for retinal image registration (RIR). This paper investigates using the expectation maximization for principal component analysis based mutual information (EMPCA-MI) algorithm in RIR. It combines spatial features with mutual information to efficiently achieve improved registration performance. Experimental results for mono-modal RI datasets verify that EMPCA-MI together with Powell-Brent optimization affords superior robustness in comparison with existing RIR methods, including the geometrical features method.

[1]  William H. Press,et al.  Numerical Recipes 3rd Edition: The Art of Scientific Computing , 2007 .

[2]  References , 1971 .

[3]  Guoliang Fan,et al.  Hybrid retinal image registration , 2006, IEEE Transactions on Information Technology in Biomedicine.

[4]  Paul L. Rosin,et al.  A Robust Solution to Multi-modal Image Registration by Combining Mutual Information with Multi-scale Derivatives , 2009, MICCAI.

[5]  Chia-Ling Tsai,et al.  The Edge-Driven Dual-Bootstrap Iterative Closest Point Algorithm for Registration of Multimodal Fluorescein Angiogram Sequence , 2010, IEEE Transactions on Medical Imaging.

[6]  Charles V. Stewart,et al.  Retinal Vessel Centerline Extraction Using Multiscale Matched Filters, Confidence and Edge Measures , 2006, IEEE Transactions on Medical Imaging.

[7]  V.R.S Mani,et al.  Survey of Medical Image Registration , 2013 .

[8]  Lisa M. Brown,et al.  A survey of image registration techniques , 1992, CSUR.

[9]  Li Yuan-yuan.  A SURVEY OF MEDICAL IMAGE REGISTRATION , 2006 .

[10]  Jan Flusser,et al.  Image registration methods: a survey , 2003, Image Vis. Comput..

[11]  Conor Heneghan,et al.  Registration of digital retinal images using landmark correspondence by expectation maximization , 2004, Image Vis. Comput..

[12]  Jiri Jan,et al.  Registration of multimodal images of retina , 2002, Proceedings of the Second Joint 24th Annual Conference and the Annual Fall Meeting of the Biomedical Engineering Society] [Engineering in Medicine and Biology.

[13]  Guy Marchal,et al.  Multimodality image registration by maximization of mutual information , 1997, IEEE Transactions on Medical Imaging.

[14]  Balraj Naren,et al.  Medical Image Registration , 2022 .

[15]  J. Jan,et al.  Registration of bimodal retinal images - improving modifications , 2004, The 26th Annual International Conference of the IEEE Engineering in Medicine and Biology Society.

[16]  Parminder Singh Reel,et al.  Efficient image registration using fast principal component analysis , 2012, 2012 19th IEEE International Conference on Image Processing.

[17]  Paul L. Rosin,et al.  Incorporating neighbourhood feature derivatives with Mutual Information to improve accuracy of multi-modal image registration , 2008 .

[18]  Max A. Viergever,et al.  Ridge-based vessel segmentation in color images of the retina , 2004, IEEE Transactions on Medical Imaging.

[19]  Yulong Shen,et al.  Registration and fusion of retinal images-an evaluation study , 2003, IEEE Transactions on Medical Imaging.

[20]  L. Zangwill,et al.  Using optical imaging summary data to detect glaucoma. , 2001, Ophthalmology.

[21]  L. Dooley,et al.  A new mutual information based similarity measure for medical image registration , 2012 .

[22]  Huiqi Li,et al.  Automated feature extraction in color retinal images by a model based approach , 2004, IEEE Transactions on Biomedical Engineering.

[23]  Sabalan Daneshvar,et al.  Retinal Image Registration Using Geometrical Features , 2013, Journal of Digital Imaging.

[24]  Juan Xu,et al.  Optic disk feature extraction via modified deformable model technique for glaucoma analysis , 2007, Pattern Recognit..

[25]  Chia-Ling Tsai,et al.  The dual-bootstrap iterative closest point algorithm with application to retinal image registration , 2003, IEEE Transactions on Medical Imaging.

[26]  Carlo Tomasi,et al.  Image Similarity Using Mutual Information of Regions , 2004, ECCV.