Andras Hajdu

An Ensemble-Based System for Microaneurysm Detection and Diabetic Retinopathy Grading

Reliable microaneurysm detection in digital fundus images is still an open issue in medical image processing. We propose an ensemble-based framework to improve microaneurysm detection. Unlike the well-known approach of considering the output of multiple classifiers, we propose a combination of internal components of microaneurysm detectors, namely preprocessing methods and candidate extractors. We have evaluated our approach for microaneurysm detection in an online competition, where this algorithm is currently ranked as first, and also on two other databases. Since microaneurysm detection is decisive in diabetic retinopathy (DR) grading, we also tested the proposed method for this task on the publicly available Messidor database, where a promising AUC 0.90 ± 0.01 is achieved in a “DR/non-DR”-type classification based on the presence or absence of the microaneurysms.

Retinal Microaneurysm Detection Through Local Rotating Cross-Section Profile Analysis

A method for the automatic detection of microaneurysms (MAs) in color retinal images is proposed in this paper. The recognition of MAs is an essential step in the diagnosis and grading of diabetic retinopathy. The proposed method realizes MA detection through the analysis of directional cross-section profiles centered on the local maximum pixels of the preprocessed image. Peak detection is applied on each profile, and a set of attributes regarding the size, height, and shape of the peak are calculated subsequently. The statistical measures of these attribute values as the orientation of the cross-section changes constitute the feature set that is used in a naïve Bayes classification to exclude spurious candidates. We give a formula for the final score of the remaining candidates, which can be thresholded further for a binary output. The proposed method has been tested in the Retinopathy Online Challenge, where it proved to be competitive with the state-of-the-art approaches. We also present the experimental results for a private image set using the same classifier setup.

An ensemble-based system for automatic screening of diabetic retinopathy

In this paper, an ensemble-based method for the screening of diabetic retinopathy (DR) is proposed. This approach is based on features extracted from the output of several retinal image processing algorithms, such as image-level (quality assessment, pre-screening, AM/FM), lesion-specific (microaneurysms, exudates) and anatomical (macula, optic disk) components. The actual decision about the presence of the disease is then made by an ensemble of machine learning classifiers. We have tested our approach on the publicly available Messidor database, where 90% sensitivity, 91% specificity and 90% accuracy and 0.989 AUC are achieved in a disease/no-disease setting. These results are highly competitive in this field and suggest that retinal image processing is a valid approach for automatic DR screening.

Improving microaneurysm detection using an optimally selected subset of candidate extractors and preprocessing methods

In this paper, we present an approach to improve microaneurysm detection in digital color fundus images. Instead of following the standard process which considers preprocessing, candidate extraction and classification, we propose a novel approach that combines several preprocessing methods and candidate extractors before the classification step. We ensure high flexibility by using a modular model and a simulated annealing-based search algorithm to find the optimal combination. Our experimental results show that the proposed method outperforms the current state-of-the-art individual microaneurysm candidate extractors.

Automatic detection of the optic disc using majority voting in a collection of optic disc detectors

This paper proposes an efficient method for locating the optic disc in retinal images automatically using majority voting scheme and data fusion. We show that instead of inventing a new algorithm which ends up being a minor variation on an old idea, the fusion of different optic disc (OD) detectors can enhance the overall performance of the detection system. The optic disc centre candidates of different optic disc detectors are marked in the image and a circular template is fit on each pixel in the image to count the outputs of these algorithms that fall within the radius. The location with maximum number of optic disc centre candidates is the hotspot and is used to localize the optic disc centre. An assessment of the performance of the combined optic disc detector versus detectors working separately is also presented. Our method achieved highest performance (overall 100% correct detection).

Microaneurysm detection in retinal images using a rotating cross-section based model

Retinal image analysis is currently a very vivid field in biomedical image analysis. One of the most challenging tasks is the reliable automatic detection of microaneurysms (MAs). Computer systems that aid the automatic detection of diabetic retinopathy (DR) greatly rely on MA detection. In this paper, we present a method to construct an MA score map, from which the final MAs can be extracted by simple thresholding for a binary output, or by considering all the regional maxima to obtain probability scores. In contrary to most of the currently available MA detectors, the proposed one does not use any supervised training and classification. However, it is still competitive in the field, with a prominent performance in the detection of MAs close to the vasculature, regarding the state-of-the-art methods. The algorithm has been evaluated in a publicly available online challenge.

Approximating the Euclidean distance using non-periodic neighbourhood sequences

Abstract In this paper, we discuss some possibilities of approximating the Euclidean distance in Z 2 by the help of digital metrics induced by neighbourhood sequences. Contrary to the earlier approaches, we use general (non-periodic) neighbourhood sequences which allows us to derive more precise results. We determine these metrics which can be regarded as the best approximations to the Euclidean distance in some sense. We compare our results with earlier studies of Das (J. Approx. Theory 68 (1992) 155) and Mukherjee et al. (Pattern Recognition Lett. 21 (2000) 573).

Tear fluid proteomics multimarkers for diabetic retinopathy screening

BackgroundThe aim of the project was to develop a novel method for diabetic retinopathy screening based on the examination of tear fluid biomarker changes. In order to evaluate the usability of protein biomarkers for pre-screening purposes several different approaches were used, including machine learning algorithms.MethodsAll persons involved in the study had diabetes. Diabetic retinopathy (DR) was diagnosed by capturing 7-field fundus images, evaluated by two independent ophthalmologists. 165 eyes were examined (from 119 patients), 55 were diagnosed healthy and 110 images showed signs of DR. Tear samples were taken from all eyes and state-of-the-art nano-HPLC coupled ESI-MS/MS mass spectrometry protein identification was performed on all samples. Applicability of protein biomarkers was evaluated by six different optimally parameterized machine learning algorithms: Support Vector Machine, Recursive Partitioning, Random Forest, Naive Bayes, Logistic Regression, K-Nearest Neighbor.ResultsOut of the six investigated machine learning algorithms the result of Recursive Partitioning proved to be the most accurate. The performance of the system realizing the above algorithm reached 74% sensitivity and 48% specificity.ConclusionsProtein biomarkers selected and classified with machine learning algorithms alone are at present not recommended for screening purposes because of low specificity and sensitivity values. This tool can be potentially used to improve the results of image processing methods as a complementary tool in automatic or semiautomatic systems.

Improving microaneurysm detection in color fundus images by using context-aware approaches

In this paper, we present two approaches to improve microaneurysm detector ensembles. First, we provide an approach to select a set of preprocessing methods for a microaneurysm candidate extractor to enhance its detection performance in color fundus images. The performance of the candidate extractor with each preprocessing method is measured in six microaneurysm categories. The best performing preprocessing method for each category is selected and organized into an ensemble-based method. We tested our approach on the publicly available DiaretDB1 database, where the proposed approach led to an improvement regarding the individual approaches. Second, an adaptive weighting approach for microaneurysm detector ensembles is presented.The basis of the adaptive weighting approach is the spatial location and contrast of the detected microaneurysm. During training, the performance of ensemble members is measured with respect to these contextual information, which serves as a basis for the optimal weights assigned to the detectors. We have tested this approach on two publicly available datasets, where it showed its competitiveness compared without previously published ensemble-based approach for microaneurysm detection. Moreover, the proposed approach outperformed all the investigated individual detectors.

Automatic exudate detection using active contour model and regionwise classification

Diabetic retinopathy is one the most common cause of blindness in the world. Exudates are among the early signs of this disease, so its proper detection is a very important task to prevent consequent effects. In this paper, we propose a novel approach for exudate detection. First, we identify possible regions containing exudates using grayscale morphology. Then, we apply an active contour based method to minimize the Chan-Vese energy to extract accurate borders of the candidates. To remove those false candidates that have sufficient strong borders to pass the active contour method we use a regionwise classifier. Hence, we extract several shape features for each candidate and let a boosted Naïve Bayes classifier eliminate the false candidates. We considered the publicly available DiaretDB1 color fundus image set for testing, where the proposed method outperformed several state-of-the-art exudate detectors.