Multifilters-Based Unsupervised Method for Retinal Blood Vessel Segmentation

• Published in: Applied sciences Vol. 12; no. 13; p. 6393
• Main Authors: Muzammil, Nayab, Shah, Syed Ayaz Ali, Shahzad, Aamir, Khan, Muhammad Amir, Ghoniem, Rania M.
• Format: Journal Article
• Language: English
• Published: Basel MDPI AG 01.07.2022
• Subjects: Accuracy; Blood vessels; CLAHE; Datasets; Deep learning; Diabetes mellitus; Diabetic retinopathy; Equalization; Eye diseases; fundus images; fuzzy histogram; Fuzzy logic; Gabor wavelet; High performance systems; Histograms; Hypertension; Image contrast; Image enhancement; Image filters; Image processing; Literature reviews; matched filtering; Matched filters; Morlet wavelet; Morphology; Pathology; Retina; Retinal images; Sensitivity; vessel segmentation; Visual system
• ISSN: 2076-3417; 2076-3417
• DOI: 10.3390/app12136393

Fundus imaging is one of the crucial methods that help ophthalmologists for diagnosing the various eye diseases in modern medicine. An accurate vessel segmentation method can be a convenient tool to foresee and analyze fatal diseases, including hypertension or diabetes, which damage the retinal vessel’s appearance. This work suggests an unsupervised approach for vessels segmentation out of retinal images. The proposed method includes multiple steps. Firstly, from the colored retinal image, green channel is extracted and preprocessed utilizing Contrast Limited Histogram Equalization as well as Fuzzy Histogram Based Equalization for contrast enhancement. To expel geometrical articles (macula, optic disk) and noise, top-hat morphological operations are used. On the resulted enhanced image, matched filter and Gabor wavelet filter are applied, and the outputs from both is added to extract vessels pixels. The resulting image with the now noticeable blood vessel is binarized using human visual system (HVS). A final image of segmented blood vessel is obtained by applying post-processing. The suggested method is assessed on two public datasets (DRIVE and STARE) and showed comparable results with regard to sensitivity, specificity and accuracy. The results we achieved with respect to sensitivity, specificity together with accuracy on DRIVE database are 0.7271, 0.9798 and 0.9573, and on STARE database these are 0.7164, 0.9760, and 0.9560, respectively, in less than 3.17 s on average per image.