Retinal Image Registration Using Geometrical Features

In this study, we have introduced an accurate retinal images registration method using affine moment invariants (AMI’s) which are the shape descriptors. First, some closed-boundary regions are extracted in both reference and sensed images. Then, AMI’s are computed for each of those regions. The centers of gravity of three pairs of regions which have the minimum of distances are selected as the control points. The region matching is performed by the distance measurements of AMI’s. The evaluation of region matching is performed by comparing the angles of three triangles which are built on these three-point pairs in reference and sensed images. The parameters of affine transform can be computed using these three pairs of control points. The proposed algorithm is applied on the valid DRIVE database. In general (for the case, each sensed image is produced by rotating, scaling, and translating the reference image with different angles, scale factors, and translation factors), the success rate and accuracy is 95 and 96 %, respectively.

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

[2]  Hideki Kuga,et al.  A computer method of understanding ocular fundus images , 1982, Pattern Recognit..

[3]  Christine Pohl,et al.  Multisensor image fusion in remote sensing: concepts, methods and applications , 1998 .

[4]  A. V. Cideciyan,et al.  Registration of ocular fundus images: an algorithm using cross-correlation of triple invariant image descriptors , 1995 .

[5]  Jan Flusser,et al.  A moment-based approach to registration of images with affine geometric distortion , 1994, IEEE Trans. Geosci. Remote. Sens..

[6]  Puteh Saad,et al.  Object Detection using Circular Hough Transform , 2005 .

[7]  Pascale Massin,et al.  Automatic detection of microaneurysms in color fundus images , 2007, Medical Image Anal..

[8]  S. Maitra.  Moment invariants , 1979, Proceedings of the IEEE.

[9]  R. Kolář,et al.  Automatic Rigid Registration and Analysis of Colour Fundus Image in Patients with Diabetic Retinopathy , 2009 .

[10]  Kostas Delibasis,et al.  Automatic model-based tracing algorithm for vessel segmentation and diameter estimation , 2010, Comput. Methods Programs Biomed..

[11]  Ian J. Deary,et al.  Retinal image analysis: concepts, applications and potential , 2006 .

[12]  D. Hill,et al.  Medical image registration , 2001, Physics in medicine and biology.

[13]  Sabalan Daneshvar,et al.  MRI and PET image fusion by combining IHS and retina-inspired models , 2010, Inf. Fusion.

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

[15]  Thitiporn Chanwimaluang.  Advanced retinal imaging: Feature extraction, 2-D registration, and 3-D reconstruction , 2006 .

[16]  Jan Flusser,et al.  Image Registration: A Survey and Recent Advances , 2005 .

[17]  C. Sinthanayothin,et al.  Automated localisation of the optic disc, fovea, and retinal blood vessels from digital colour fundus images , 1999, The British journal of ophthalmology.

[18]  Amelia Simó,et al.  Bayesian detection of the fovea in eye fundus angiographies , 1999, Pattern Recognit. Lett..

[19]  Daneshvar Sabalan.  HASSAN GHASSEMIAN. MRI AND PET IMAGE FUSION BY COMBINING IHS AND RETINA-INSPIRED MODELS , 2010 .

[20]  Hua Cao,et al.  A novel automated approach of multi-modality retinal image registration and fusion , 2008 .

[21]  Ke Huang,et al.  A Region Based Algorithm for Vessel Detection in Retinal Images , 2006, MICCAI.

[22]  Enrico Grisan,et al.  Detection of optic disc in retinal images by means of a geometrical model of vessel structure , 2004, IEEE Transactions on Medical Imaging.

[23]  P. Gregson,et al.  Automated grading of venous beading. , 1995, Computers and biomedical research, an international journal.

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

[25]  Jan Flusser,et al.  The Independence of the Affine Moment Invariants , 2006 .

[26]  Jan Flusser,et al.  Pattern recognition by affine moment invariants , 1993, Pattern Recognit..

[27]  S F Barrett,et al.  Employing the Hough Transform to locate the optic disk. , 2001, Biomedical sciences instrumentation.

[28]  Yang-Ming Zhu,et al.  Mutual information-based registration of temporal and stereo retinal images using constrained optimization , 2007, Comput. Methods Programs Biomed..