Automatic retinal image registration scheme using global optimization techniques

Abstract

Retinal image registration is commonly required in order to combine the complementary information in different retinal modalities. In this paper, a new automatic scheme to register retinal images is presented and is currently tested in a clinical environment. The scheme considers the suitability and efficiency of different image transformation models and function optimization techniques, following an initial preprocessing stage. Three different transformation models--affine, bilinear and projective--as well as three optimization techniques--downhill simplex method, simulated annealing and genetic algorithms--are investigated and compared in terms of accuracy and efficiency. The registration of 26 pairs of Fluoroscein Angiography and Indocyanine Green Chorioangiography images with the corresponding Red-Free retinal images, showed the superiority of combining genetic algorithms with the affine and bilinear transformation models. A comparative study of the proposed automatic registration scheme against the manual method, commonly used in the clinical practice, is finally presented showing the advantage of the proposed automatic scheme in terms of accuracy and consistency.

Hybrid Retinal Image Registration Using Mutual Information and Salient Features

“…These methods are computationally efficient and robust to intensity variation, but their performance largely depends on the quality of segmentation and whether or not there are sufficient and reliable correspondences. Intensity-based methods optimize a similarity measure such as intensity difference, cross correlation, phase correlation, or mutual information (MI) [7]- [9]. The MI, a measure of the statistical correlation between intensity values of the images, has been widely used in medical imaging.…”

An Analysis of Registration of Brain Images using Fast Walsh Hadamard Transform

“…An automatic scheme using global optimization technique for retinal image registration was put forth by Matsopoulos et al in [1]. A robust approach that estimates the affine transformation parameters necessary to register any two digital images misaligned due to rotation, scale, shear, and translation was proposed by Wolberg and Zokai in [2].…”

“…Such data is very crucial in medicine for doctors to prepare for surgery. The most general and significant classes of image analysis algorithm with medical applications [1,3] are image registration and image segmentation. In image analysis procedure, the same input gives out somewhat detail sketch of the scene whose image is being considered.…”

“…There is no universal registration [1][2][3][4][5][6][7][8][9][10][11][12][13][14][15][16][17][18] algorithm that can work reasonably well for all images. An appropriate registration algorithm for the particular problem must be chosen or developed, as they are adhoc in nature.…”

Analysis and Implementation of Methodologies for the Monitoring of Changes in Eye Fundus Images

“…From the pairs of points generated various transformation functions were applied [4][5][6][7], obtaining very good results with the affine and polynomial transformations. It was observed that the affine transformation required fewer pairs of points that the polynomial and was approximately 50% faster.…”