Radim Chrástek

Automated segmentation of the optic nerve head for diagnosis of glaucoma

Glaucoma is the second most common cause of blindness worldwide. Low awareness and high costs connected to glaucoma are reasons to improve methods of screening and therapy. A well-established method for diagnosis of glaucoma is the examination of the optic nerve head using scanning-laser-tomography. This system acquires and analyzes the surface topography of the optic nerve head. The analysis that leads to a diagnosis of the disease depends on prior manual outlining of the optic nerve head by an experienced ophthalmologist. Our contribution presents a method for optic nerve head segmentation and its validation. The method is based on morphological operations, Hough transform, and an anchored active contour model. The results were validated by comparing the performance of different classifiers on data from a case-control study with contours of the optic nerve head manually outlined by an experienced ophthalmologist. We achieved the following results with respect to glaucoma diagnosis: linear discriminant analysis with 27.7% estimated error rate for automated segmentation (aut) and 26.8% estimated error rate for manual segmentation (man), classification trees with 25.2% (aut) and 22.0% (man) and bootstrap aggregation with 22.2% (aut) and 13.4% (man). It could thus be shown that our approach is suitable for automated diagnosis and screening of glaucoma.

Optic Disc Segmentation in Retinal Images

Retinal images give unique diagnostic information not only about eye disease but about other organs as well [1]. To give the physicians a tool for objective quantitative assessment of the retina, automated methods have been developed. In this paper an automated method for the optic disc segmentation is presented. The method consists of 4 steps: localization of the optic disc, nonlinear filtering, Canny edge detector and Hough transform. The results have shown that the algorithm is very robust. The localization was 97% successful and the segmentation 82%.

Segmentation of Fundus Eye Images Using Methods of Mathematical Morphology for Glaucoma Diagnosis

In this paper the new method for automatic segmentation of cup and optic disc in fundus eye images taken from classical fundus camera is proposed. The proposed method is fully based on techniques from mathematical morphology. Detection of cup region makes use of watershed transformation with markers imposed, while optic disk is extracted based on geodesic reconstruction by dilation. The obtained results are encouraging.

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.

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.

Automated Segmentation of the Optic Nerve Head for Glaucoma Diagnosis

The diagnosis of glaucoma is closely associated with a morphological change in the optic nerve head (ONH), which can be examined with a scanning laser ophthalmoskop (Heidelberg Retina Tomograph). In this contribution a method for automated segmentation of the external margin of the ONH is presented. The method is based on morphological operations, Hough transform and Active Contours. The method was compared with a manually outlined margin on a subset of 159 subjects from the Erlangen Glaucoma Register. The correct classification rate was estimated to be 77.8% when using a tree-based classificator. This result is comparable with the estimated rate based on a manual outlining of the ONH.

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.

Automatic Detection of Glaucomatous Changes Using Adaptive Thresholding and Neural Networks

In this paper the new method for automatic classification of fundus eye images into normal and glaucomatous ones is proposed. The new, morphological features for quantitative cup evaluation are proposed based on genetic algorithms. For computation of these features the original method for automatic segmentation of the cup contour is proposed. The computed features are then used in classification procedure which is based on multilayer perceptron. The mean sensitivity is 90%, while the mean specificity: 86%. The obtained results are encouraging.

Automatic Segmentation and Classification of Fundus Eye Images for Glaucoma Diagnosis

In this paper the new method for automatic classification of fundus eye images into normal and glaucomatous ones is proposed. The new, morphological features for quantitative cup evaluation are proposed based on genetic algorithms. For computation of these features the original method for automatic segmentation of the cup contour is proposed. The computed features are then used in classification procedure which is based on multilayer perceptron. The mean sensitivity is 90% while the mean specificity: 86%. The obtained results are encouraging.

A Hybrid Distance Map Based and Morphologic Thinning Algorithm

A lot of applications need that an object is transformed into its medial axis while preserving its topology. In this paper we present a thinning algorithm based on special masks preserving connectivity. The thinning process is controled by a distance map to overcome problems of a former approach. As a result we obtain a skeleton that has minor artefacts only and which is suitable in the field of blood vessel analysis. We are providing results on synthetic and real images that are compared with another approach.