Libor Kubecka

Registration of bimodal retinal images - improving modifications

The proper optical disc segmentation in images provided by confocal laser scanning ophthalmoscope and by color fundus-camera is a necessary step in early glaucoma or arteriosclerosis detection. Fusing information from both modalities into a vector-valued image is expected to improve the segmentation reliability. The paper describes a registration of these images using optimization based on mutual information criterion function extended with gradient-image mutual information. The controlled random search (CRS) has been found to be a more robust optimization routine than the simulated annealing (SA) while tested on a set of 174 image pairs. Finally, the multi-resolution algorithm for bimodal retinal image registration achieving the success-rate of 94% is proposed.

Retrospective illumination correction of retinal images

A method for correction of nonhomogenous illumination based on optimization of parameters of B-spline shading model with respect to Shannons entropy is presented. The evaluation of Shannons entropy is based on Parzen windowing method (Mangin, 2000) with the spline-based shading model. This allows us to express the derivatives of the entropy criterion analytically, which enables efficient use of gradient-based optimization algorithms. Seven different gradient- and nongradient-based optimization algorithms were initially tested on a set of 40 simulated retinal images, generated by a model of the respective image acquisition system. Among the tested optimizers, the gradient-based optimizer with varying step has shown to have the fastest convergence while providing the best precision. The final algorithm proved to be able of suppressing approximately 70% of the artificially introduced non-homogenous illumination. To assess the practical utility of the method, it was qualitatively tested on a set of 336 real retinal images; it proved the ability of eliminating the illumination inhomogeneity substantially in most of cases. The application field of this method is especially in preprocessing of retinal images, as preparation for reliable segmentation or registration.

Registration and Fusion of the Autofluorescent and Infrared Retinal Images

This article deals with registration and fusion of multimodal opththalmologic images obtained by means of a laser scanning device (Heidelberg retina angiograph). The registration framework has been designed and tested for combination of autofluorescent and infrared images. This process is a necessary step for consecutive pixel level fusion and analysis utilizing information from both modalities. Two fusion methods are presented and compared.

Towards automated diagnostic evaluation of retina images

In this paper we describe the automatic segmentation of the optic nerve head (ONH) with the long-term goal of automatically diagnosing early stages of glaucoma. The images are average images obtained from a scanning laser ophthalmoscope (SLO). The segmentation consists of the main s teps of finding a region of interest containing the ONH, constraining the search space for final segmentation, and computing the fine segmentation by an active contour model. The agreement of “true positive pixels,” i.e., pixels attributed to the ONH by both manual and automatic segmentation, is very good. The classification results from three different classifiers using manual or automatic segmentation still show an advantage of manual segmentation. One means to further improve the automatic segmentation is to use information from an SLO as well as from a fundus camera.

Optimization methods for registration of multimodal images of retina

Registration of multimodal images of retina is essential for correct diagnosis of the optic nerve head and retina. For reliable vessel segmentation, it is also important to use information from both, colour photographs and Heidelberg Retina Tomograph (HRT) scans. Mutual information was tested as a coincidence measure and has proven functional and reliable. This paper compares several methods for finding the correct transformation parameters.

Optic nerve head segmentation in multimodal retinal images

An established method for glaucoma diagnosis is the morphological analysis of the optic nerve head (ONH) by the scanning-laser-tomography (SLT). This analysis depends on prior manual outlining of the ONH. The first automated segmentation method that we developed is limited in its reliability by noise, non-uniform illumination and presence of blood vessels. Inspired by recent medical research we developed a new algorithm improving our previous method by segmenting in registered multimodal retinal images. The multimodal approach combines SLT-images with color fundus photographs (CFP). The first step of the algorithm, the registration, is based on gradient-image mutual information maximization using controlled random search as the optimization procedure. The kernel of the segmentation module consists in the anchored active contours. The initial contour is obtained from the CFP. The points the initial curve should be attracted to, the anchors, are constrained by the Hough transform applied to a morphologically processed SLT-image. The false anchors are eliminated by masking out blood vessels that are extracted in the CFP. The method was tested on 174 multimodal image pairs. The overall performance of the system yielded 89% correctly segmented ONH, qualitatively evaluated comparing the automated contours with manual ones drawn by an experienced ophthalmologist. This represents an appreciable improvement in reliability (from 74% to 89%) compared to monomodal approach. The developed method is the basis for a promising tool for glaucoma screening.

Analysis of Fused Ophthalmologic Image Data

-The contribution summarises the results of a long-term project concerning processing and analysis of multimodal retinal image data, run in cooperation between Brno University of Technology -Dept. of Biomedical Engineering and Erlangen University -Clinic of Ophthalmology. From the medical application point of view, the main stimulus is the improvement of diagnostics (primarily of glaucoma but other diseases as well) by making the image segmentation and following analysis reproducible and possibly independent on the evaluator. Concerning the methodology, different image processing approaches had to be combined and modified in order to achieve reliable clinically applicable procedures.