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.
challenge. This paper outlines the challenge, its organization, the dataset used, evaluation methods and results of top performing participating solutions. We observe that the top performing approaches utilize a blend of clinical information, data augmentation, and the ensemble of models. These findings have the potential to enable new developments in retinal image analysis and image-based DR screening in particular.
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.
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 disc) 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
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