Abstract
Malarial Retinopathy (MR) is indicated by retina alteration such as white dots occurrence which is caused by malaria. Leak detection is a key factor of MR’s early diagnosis. Inconsistent size and shape of the leakages with the colour contrast that relatively similar with the background. Leak detection’s algorithm is one of the most complex algorithms on the fundus image analysis field. Therefore, improving performance in the leakage detection is essential. This study focuses on automated leakage detection on fluorescein angiography (FA) images. The methods used in this study are vessel segmentation, saliency detection, phase stretch transform (PST), optic disk removal and leak detection to extract some features which then classified to correctly validate the leak. From 20 patient data large focal leak images with 31 leak points, 28 of them have been correctly detected. So, the experiment produced the accuracy and specificity of 0.98 and 0.9, respectively. With the proposed method of this study, there is a potential to enhance the knowledge on MR field in the future.
Highlights
Genus Plasmodium is a kind of parasites that cause human malaria
Severe malaria caused by P. falciparum can lead to death if it is not correctly handled; since there is a neurological complication as severe as on cerebral malaria
(1) Vessel Segmentation Some methods used for segmenting vessels are explained as follows: a) Pre-processing Firstly, fluorescein angiography (FA) image is enhanced by using adaptive histogram to obtain brighter vessels than that of on the original image
Summary
Genus Plasmodium is a kind of parasites that cause human malaria. For the specific species, it consists of P. ovale, P. knowlesi, P. falciparum, P. malariae and P. vivax [1]. Retinal alteration on severe malaria such as bleeding has been observed more than 130 years [4] and the unique signs of retina are described for the first time in the Africa [5]. MR revealed almost all common different disorder of angiographic vascular for retina condition and because of that, it is easy to use this condition to develop semi-automatic or automatic device to measure retinal disorder quantitatively. In another hand, the similarity between the eye and brain associated malaria brain seems to suggest that the retina may be a good source of potential biomarkers that may cast light on the processes of cerebral malaria disease. This research focuses on the application of PST method to detect the retinal leakage for diagnosing malaria disease
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