An Image-Processing Strategy for the Segmentation and Quantification of Microaneurysms in Fluorescein Angiograms of the Ocular Fundus

Automated detection of microaneurysms using scale-adapted blob analysis and semi-supervised learning

“…A mathematical morphology based approach using top-hat transform with a linear structural element is used to remove connected vasculatures and other retinal structures. Spencer et al [3] and Frame1998 et al [4] have extended this approach by adding shade correction as a pre-processing and match-filters of different sizes to detect MAs. However, the risk associated with intravenous contrast agents limit the usage of fluorescein angiograms for public DR screening programs [5].…”

A Study on Hemorrhage Detection Using Hybrid Method in Fundus Images

“…Spencer and Frame suggested a method using top-hat transformation and a matched filter. 4,5 Niemeijer et al 6 developed this research further and introduced the hybrid methods. Fleming et al 7 proposed a method for the detection of microaneurysms by using a watershed transform in fluorescein angiograms.…”

Automatic Detection of Microaneurysms and Hemorrhages in Digital Fundus Images

“…Morphological tophat transform has been applied for eliminating the blood vessels from fundus images in Spencer et al and Fleming et al 1,2 The residual regions were considered as candidate MA. For the MA detection from the candidates, region-growing algorithm was employed in Spencer et al 1 It was further improved in Fleming et al 2 using watershed region growing and contrast normalization to improve the ability to distinguish MA from other dots that occur on the retina. In Walter and Klein, 3 bounding box closing is used for the detection of dark details in the gray-level images.…”

“…In Niemeijer et al, 4 pixel classification based on supervised learning is used to separate the vasculature and red lesions from the background. An extensive number of new features were then added to those proposed in Spencer et al 1 In Hatanaka et al, 5 candidate red lesions were detected using density analysis and then classified using rule-based method and three Mahalanobis distance classifiers with a 45-feature analysis. The method proposed in Marino et al 6 was performed in three stages: in the first stage, candidate red lesions are obtained by using a set of correlation filters working in different resolutions.…”

“…Our primary focus in this paper is to detect images containing red lesions with a very high sensitivity and a reasonable specificity. The performance of the proposed algorithm is compared to the bounding box closing method proposed in Walter and Klein 3 and mathematical morphology scheme proposed in Spencer et al 1 The paper is organized as follows. In "Proposed Algorithm," implementation details of the proposed algorithm are presented.…”