access icon free Retinal image blood vessel extraction and quantification with Euclidean distance transform approach

Image processing applications remarkably contributes to modern ophthalmology. This technology is designed to analyse the characteristics of the human eye microvasculature images. The retinal microvasculature is an excellent non-invasive screening window for the assessment of systemic diseases such as diabetes, hypertension, and stroke. Retinal microvasculature character such as widening vessel diameter is recognised as an analysable feature for stroke or transient ischemic attack for predicting the progression of this pathology. Thus, in this study, a computer-assisted method has been developed for this task applying the Euclidean distance transform (EDT) technique. This newly developed algorithm computes the Euclidean distance of the remaining white pixels on the area of interest. Central Light Reflex Image Set (CLRIS) and Vascular Disease Image Set (VDIS) of Retinal Vessel Image set for Estimation of Width database were used for the performance evaluation of the proposed algorithm that showed 98.1 and 97.7% accurate result for both CLRIS and VDIS, respectively. The significantly high accuracy in this newly developed vessel diameter quantification algorithm indicates excellent potential for further development, evaluation, validation, and integration into ophthalmic diagnostic instruments.

Inspec keywords: medical image processing; image segmentation; diseases; image processing; blood vessels; biomedical optical imaging; feature extraction; eye

Other keywords: transient ischemic attack; Retinal Vessel Image; newly developed vessel diameter quantification algorithm; Euclidean distance; systemic diseases; stroke; human eye microvasculature images; Image processing applications; modern ophthalmology; computer-assisted method; Retinal microvasculature character; Central Light Reflex Image Set; analysable feature; remaining white pixels; newly developed algorithm; noninvasive screening window; Vascular Disease Image Set

Subjects: Optical, image and video signal processing; Optical and laser radiation (medical uses); Biomedical measurement and imaging; Biology and medical computing; Optical and laser radiation (biomedical imaging/measurement); Computer vision and image processing techniques; Patient diagnostic methods and instrumentation

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