{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T15:45:18Z","timestamp":1760197518104,"version":"build-2065373602"},"reference-count":25,"publisher":"MDPI AG","issue":"7","license":[{"start":{"date-parts":[[2018,7,3]],"date-time":"2018-07-03T00:00:00Z","timestamp":1530576000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Symmetry"],"abstract":"<jats:p>In this paper, we present a new unsupervised algorithm for retinal vessels segmentation. The algorithm utilizes a directionally sensitive matched filter bank using a modified Dolph-Chebyshev type II basis function and a new method to combine the matched filter bank\u2019s responses. Fundus images from the DRIVE and STARE databases, as well as high-resolution fundus images from the HRF database, are utilized to validate the proposed algorithm. The results that we achieve on the three databases (DRIVE: Sensitivity = 0.748, F1-score = 0.786, G-score = 0.856, Matthews Correlation Coefficient = 0.758; STARE: Sensitivity = 0.793, F1-score = 0.780, G-score = 0.877, Matthews Correlation Coefficient = 0.756; HRF: Sensitivity = 0.804, F1-score = 0.764, G-score = 0.883, Matthews Correlation Coefficient = 0.741) are higher than many other competing methods.<\/jats:p>","DOI":"10.3390\/sym10070257","type":"journal-article","created":{"date-parts":[[2018,7,3]],"date-time":"2018-07-03T11:12:58Z","timestamp":1530616378000},"page":"257","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":5,"title":["A Modified Dolph-Chebyshev Type II Function Matched Filter for Retinal Vessels Segmentation"],"prefix":"10.3390","volume":"10","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-1971-3214","authenticated-orcid":false,"given":"Dhimas Arief","family":"Dharmawan","sequence":"first","affiliation":[{"name":"School of Electrical and Electronic Engineering, Nanyang Technological University, 50 Nanyang Avenue, Singapore 639798, Singapore"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Boon Poh","family":"Ng","sequence":"additional","affiliation":[{"name":"School of Electrical and Electronic Engineering, Nanyang Technological University, 50 Nanyang Avenue, Singapore 639798, Singapore"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0831-6934","authenticated-orcid":false,"given":"Susanto","family":"Rahardja","sequence":"additional","affiliation":[{"name":"School of Marine Science and Technology, Northwestern Polytechnical University, 127 West Youyi Road, Xi\u2019an 710072, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2018,7,3]]},"reference":[{"key":"ref_1","first-page":"1874","article-title":"Segmentation of Blood Vessels and Optic Disc in Retinal Images","volume":"2194","author":"Kaba","year":"2014","journal-title":"IEEE J. Biomed. Health Inform."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"263","DOI":"10.1109\/42.34715","article-title":"Detection of blood vessels in retinal images using two-dimensional matched filters","volume":"8","author":"Chaudhuri","year":"1989","journal-title":"IEEE Trans. Med. Imaging"},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"373","DOI":"10.1049\/iet-ipr.2012.0455","article-title":"Retinal vessel segmentation by improved matched filtering: Evaluation on a new high-resolution fundus image database","volume":"7","author":"Odstrcilik","year":"2013","journal-title":"IET Image Process."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"703","DOI":"10.1016\/j.patcog.2012.08.009","article-title":"An effective retinal blood vessel segmentation method using multi-scale line detection","volume":"46","author":"Nguyen","year":"2013","journal-title":"Pattern Recognit."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"1129","DOI":"10.1109\/JBHI.2015.2440091","article-title":"Leveraging Multiscale Hessian-Based Enhancement with a Novel Exudate Inpainting Technique for Retinal Vessel Segmentation","volume":"20","author":"Annunziata","year":"2016","journal-title":"IEEE J. Biomed. Health Inform."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"501","DOI":"10.1109\/TMI.2004.825627","article-title":"Ridge based vessel segmentation in color images of the retina","volume":"23","author":"Staal","year":"2005","journal-title":"IEEE Trans. Med. Imaging"},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"1738","DOI":"10.1109\/TBME.2015.2403295","article-title":"Iterative Vessel Segmentation of Fundus Images","volume":"62","author":"Roychowdhury","year":"2015","journal-title":"IEEE Trans. Biomed. Eng."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"1797","DOI":"10.1109\/TMI.2015.2409024","article-title":"Automated Vessel Segmentation Using Infinite Perimeter Active Contour Model with Hybrid Region Information with Application to Retinal Images","volume":"34","author":"Zhao","year":"2015","journal-title":"IEEE Trans. Med. Imaging"},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"1214","DOI":"10.1109\/TMI.2006.879967","article-title":"Retinal Vessel Segmentation Using the 2-D Gabor Wavelet and Supervised Classification","volume":"25","author":"Soares","year":"2006","journal-title":"IEEE Trans. Med. Imaging"},{"key":"ref_10","first-page":"1118","article-title":"Blood vessel segmentation of fundus images by major vessel extraction and subimage classification","volume":"19","author":"Roychowdhury","year":"2015","journal-title":"IEEE J. Biomed. Health Inform."},{"key":"ref_11","doi-asserted-by":"crossref","unstructured":"Dai, P., Luo, H., Sheng, H., Zhao, Y., Li, L., Wu, J., Zhao, Y., and Suzuki, K. (2015). A new approach to segment both main and peripheral retinal vessels based on gray-voting and Gaussian mixture model. PLoS ONE, 10.","DOI":"10.1371\/journal.pone.0127748"},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"109","DOI":"10.1109\/TMI.2015.2457891","article-title":"A cross-modality learning approach for vessel segmentation in retinal images","volume":"35","author":"Li","year":"2016","journal-title":"IEEE Trans. Med. Imaging"},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"16","DOI":"10.1109\/TBME.2016.2535311","article-title":"A Discriminatively Trained Fully Connected Conditional Random Field Model for Blood Vessel Segmentation in Fundus Images","volume":"64","author":"Orlando","year":"2017","journal-title":"IEEE Trans. Biomed. Eng."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"2369","DOI":"10.1109\/TMI.2016.2546227","article-title":"Segmenting Retinal Blood Vessels With Deep Neural Networks","volume":"35","author":"Liskowski","year":"2016","journal-title":"IEEE Trans. Med. Imaging"},{"key":"ref_15","doi-asserted-by":"crossref","unstructured":"Ourselin, S., Joskowicz, L., Sabuncu, M.R., Unal, G., and Wells, W. (2016). DeepVessel: Retinal Vessel Segmentation via Deep Learning and Conditional Random Field. Medical Image Computing and Computer-Assisted Intervention\u2014MICCAI 2016, Springer.","DOI":"10.1007\/978-3-319-46726-9_73"},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"13","DOI":"10.1016\/j.cmpb.2017.06.016","article-title":"Improving dense conditional random field for retinal vessel segmentation by discriminative feature learning and thin-vessel enhancement","volume":"148","author":"Zhou","year":"2017","journal-title":"Comput. Methods Programs Biomed."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"203","DOI":"10.1109\/42.845178","article-title":"Locating blood vessels in retinal images by piecewise threshold probing of a matched filter response","volume":"19","author":"Hoover","year":"2000","journal-title":"IEEE Trans. Med. Imaging"},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"335","DOI":"10.1109\/JRPROC.1946.225956","article-title":"A Current Distribution for Broadside Arrays Which Optimizes the Relationship between Beam Width and Side-Lobe Level","volume":"34","author":"Dolph","year":"1946","journal-title":"Proc. IRE"},{"key":"ref_19","unstructured":"Williams, A.B., and Taylor, F.J. (2006). Electronic Filter Design Handbook, The McGraw-Hill Companies, Inc.. [4th ed.]."},{"key":"ref_20","doi-asserted-by":"crossref","unstructured":"Xiao, Z., Wang, M., Zhang, F., Geng, L., Wu, J., Su, L., and Tong, J. (2016, January 16\u201318). Retinal vessel segmentation based on adaptive difference of Gauss filter. Proceedings of the 2016 IEEE International Conference on Digital Signal Processing (DSP), Beijing, China.","DOI":"10.1109\/ICDSP.2016.7868506"},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"62","DOI":"10.1109\/TSMC.1979.4310076","article-title":"A Threshold Selection Method from Gray-Level Histograms","volume":"9","author":"Otsu","year":"1979","journal-title":"IEEE Trans. Syst. Man Cybern."},{"key":"ref_22","unstructured":"Chanwimaluang, T., and Fan, G.F.G. (2003, January 25\u201328). An efficient blood vessel detection algorithm for retinal images using local entropy thresholding. Proceedings of the 2003 International Symposium on Circuits and Systems, Bangkok, Thailan."},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"1183","DOI":"10.1109\/TBME.2010.2097599","article-title":"Retinal image analysis using curvelet transform and multistructure elements morphology by reconstruction","volume":"58","author":"Miri","year":"2011","journal-title":"IEEE Trans. Biomed. Eng."},{"key":"ref_24","doi-asserted-by":"crossref","unstructured":"Ardizzone, E., Pirrone, R., Gambino, O., and Radosta, S. (2008, January 20\u201325). Blood Vessels and Feature Points Detection on Retinal Images. Proceedings of the 30th Annual International EEE Engineering in Medicine and Biology Society Conference, Vancouver, BC, Canada.","DOI":"10.1109\/IEMBS.2008.4649643"},{"key":"ref_25","doi-asserted-by":"crossref","unstructured":"Nergiz, M., and Akin, M. (2017). Retinal vessel segmentation via structure tensor coloring and anisotropy enhancement. 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