Optimized clinical segmentation of retinal blood vessels by using combination of adaptive filtering, fuzzy entropy and skeletonization

• Published in: Applied soft computing 2017-03, Vol.52, p.937-951
• Main Authors: Rezaee, Khosro, Haddadnia, Javad, Tashk, Ashkan
• Format: Article
• Language: English
• Subjects: Adaptive filter; Fuzzy entropy and skeleton method; Image processing; Retinas vessels; Wiener filter
• ISSN: 1568-4946; 1872-9681
• DOI: 10.1016/j.asoc.2016.09.033

[Display omitted] The block diagram of the proposed system. •Occasionally certain areas in the retina can be questionable for physicians which can lead to wrong interpretations for patients.•A method is proposed that introduces a higher ability of segmentation by employing Skeletonization and a threshold selection based on Fuzzy Entropy.•By extracting indices of the human retina properly, physicians will be able to estimate pathological injuries with a higher confidence.•The proposed approach is fast and outperforms over other previously competitive techniques.•The proposed approach consists of two stages. First of all, the retinal vessels was preprocessed by the HSV space and Wiener Filter. Then, the segmentation level is implemented by using Adaptive Filter that employs optimum threshold based on Fuzzy Entropy and Skeleton algorithm. The analysis of retina blood vessels in clinics indices is one of the most efficient methods employed for diagnosing diseases such as diabetes, hypertension and arthrosclerosis. In this paper, an efficient algorithm is proposed that introduces a higher ability of segmentation by employing Skeletonization and a threshold selection based on Fuzzy Entropy. In the first step, the blurring noises caused by hand shakings during ophthalmoscopy and color photography imageries are removed by a designed Wiener’s filter. Then, in the second step, a basic extraction of the blood vessels from the retina based on an adaptive filtering is obtained. At the last step of the proposed method, an optimal threshold for discriminating main vessels of the retina from other parts of the tissue is achieved by employing fuzzy entropy. Finally, an assessment procedure based on four different measurement techniques in the terms of retinal fundus colors is established and applied to DRIVE and STARE database images. Due to the evaluation comparative results, the proposed extraction of retina blood vessels enables specialists to determine the progression stage of potential diseases, more accurate and in real-time mode.