In this work, data from two-dimensional (2D) images of the human retina were taken as a case study. First, the characteristic data points had been removed using the Douglas-Peucker (DP) method, and subsequently, more data points were added using random fractal interpolation approach, to reconstruct a three-dimensional (3D) model of the blood vessel. By visualizing the result, we can see that all the small blood vessels in the human retina are more visible and detailed. This algorithm of 3D reconstruction has the advantage of being fast with calculation time less than 40 s and also can reduce the 3D image storage level on a disk with a reduction ratio between 78% and 96.65%.
Several clinical studies reveal the relationship between alterations in the topologies of the human retinal blood vessel, the outcrop and the disease evolution, such as diabetic retinopathy, hypertensive retinopathy, and macular degeneration. Indeed, the detection of these vascular changes always has gaps. In addition, the manual steps are slow, which may be subjected to a bias of the perceiver. However, we can overcome these troubles using computer algorithms that are quicker and more accurate. This paper presents and investigates a novel method for measuring the blood vessel diameter in the retinal image. The proposed method is based on a thresholding segmentation and thinning step, followed by the characteristic point determination step by the Douglas-Peucker algorithm. Thereafter, it uses the active contours to detect vessel contour. Finally, Heron’s Formula is applied to assure the calculation of vessel diameter. The obtained results for six sample images showed that the proposed method generated less errors compared to other techniques, which confirms the high performance of the proposed method.
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