{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T22:40:12Z","timestamp":1760136012232,"version":"build-2065373602"},"reference-count":36,"publisher":"MDPI AG","issue":"3","license":[{"start":{"date-parts":[[2022,2,7]],"date-time":"2022-02-07T00:00:00Z","timestamp":1644192000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"The funds of Science Technology Department of Jilin Province","award":["NO: 20200401146GX"],"award-info":[{"award-number":["NO: 20200401146GX"]}]},{"name":"The Natural Science Foundation of China","award":["61805021"],"award-info":[{"award-number":["61805021"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>The accurate segmentation of retinal vascular is of great significance for the diagnosis of diseases such as diabetes, hypertension, microaneurysms and arteriosclerosis. In order to segment more deep and small blood vessels and provide more information to doctors, a multi-scale joint optimization strategy for retinal vascular segmentation is presented in this paper. Firstly, the Multi-Scale Retinex (MSR) algorithm is used to improve the uneven illumination of fundus images. Then, the multi-scale Gaussian matched filtering method is used to enhance the contrast of the retinal images. Optimized by the Particle Swarm Optimization (PSO) algorithm, Otsu algorithm (OTSU) multi-threshold segmentation is utilized to segment the retinal image extracted by the multi-scale matched filtering method. Finally, the image is post-processed, including binarization, morphological operation and edge-contour removal. The test experiments are implemented on the DRIVE and STARE datasets to evaluate the effectiveness and practicability of the proposed method. Compared with other existing methods, it can be concluded that the proposed method can segment more small blood vessels while ensuring the integrity of vascular structure and has a higher performance. The proposed method has more obvious targets, a higher contrast, more plentiful detailed information, and local features. The qualitative and quantitative analysis results show that the presented method is superior to the other advanced methods.<\/jats:p>","DOI":"10.3390\/s22031258","type":"journal-article","created":{"date-parts":[[2022,2,7]],"date-time":"2022-02-07T20:36:42Z","timestamp":1644266202000},"page":"1258","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":5,"title":["Multiscale Joint Optimization Strategy for Retinal Vascular Segmentation"],"prefix":"10.3390","volume":"22","author":[{"given":"Minghan","family":"Yan","sequence":"first","affiliation":[{"name":"College of Electronic Information Engineering, Changchun University, Changchun 130012, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8440-1079","authenticated-orcid":false,"given":"Jian","family":"Zhou","sequence":"additional","affiliation":[{"name":"College of Electronic Information Engineering, Changchun University, Changchun 130012, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Cong","family":"Luo","sequence":"additional","affiliation":[{"name":"College of Electronic Information Engineering, Changchun University, Changchun 130012, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Tingfa","family":"Xu","sequence":"additional","affiliation":[{"name":"School of Optics and Photonics, Beijing Institute of Technology, Beijing 100081, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5448-0036","authenticated-orcid":false,"given":"Xiaoxue","family":"Xing","sequence":"additional","affiliation":[{"name":"College of Electronic Information Engineering, Changchun University, Changchun 130012, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2022,2,7]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"651247","DOI":"10.1117\/12.708469","article-title":"Blood vessel classification into arteries and veins in retinal images","volume":"6512","author":"Kondermann","year":"2007","journal-title":"Proc. SPIE"},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"1984","DOI":"10.1093\/eurheartj\/ehm221","article-title":"Retinal vessel diameter and cardiovascular mortality: Pooled data analysis from two older populations","volume":"28","author":"Wang","year":"2007","journal-title":"Eur. Heart J."},{"key":"ref_3","first-page":"31","article-title":"Analysis of Current Research Status of Retinal Vessel Segmentation","volume":"2019","author":"Qiu","year":"2019","journal-title":"Graph. Image"},{"key":"ref_4","doi-asserted-by":"crossref","unstructured":"Proch\u00e1zka, A. (2014, January 1\u20132). Registration and Analysis of Retinal Images for Diagnosis and Treatment Monitoring. Proceedings of the 2014 International Workshop on Computational Intelligence for Multimedia Understanding (IWCIM), Paris, France.","DOI":"10.1109\/IWCIM.2014.7008817"},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"100","DOI":"10.1016\/j.compbiomed.2017.07.007","article-title":"A tool for automated diabetic retinopathy pre-screening based on retinal image computer analysis","volume":"88","author":"Marin","year":"2017","journal-title":"Comput. Biol. Med."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"101971","DOI":"10.1016\/j.media.2021.101971","article-title":"Applications of Deep Learning in Fundus Images: A Review","volume":"69","author":"Li","year":"2021","journal-title":"Med. Image Anal."},{"key":"ref_7","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_8","doi-asserted-by":"crossref","first-page":"1912","DOI":"10.1109\/TBME.2018.2828137","article-title":"Joint Segment-Level and Pixel-Wise Losses for Deep Learning Based Retinal Vessel Segmentation","volume":"65","author":"Yan","year":"2018","journal-title":"IEEE Trans. Biomed. Eng."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"35","DOI":"10.3389\/fcomp.2020.00035","article-title":"Dynamic Deep Networks for Retinal Vessel Segmentation","volume":"2","author":"Khanal","year":"2020","journal-title":"Front. Comput. Sci."},{"key":"ref_10","first-page":"1060811","article-title":"A new deep learning method for blood vessel segmentation in retinal images based on convolutional kernels and modified U-Net model","volume":"205","year":"2021","journal-title":"Comput. Methods Programs Biomed."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"263","DOI":"10.1109\/42.700738","article-title":"A fuzzy vessel tracking algorithm for retinal images based on fuzzy clustering","volume":"17","author":"Tolias","year":"1998","journal-title":"IEEE Trans. Med. Imaging"},{"key":"ref_12","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_13","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_14","first-page":"58","article-title":"Theory of communication","volume":"93","author":"Gabor","year":"1946","journal-title":"J. Inst. Electr. Eng.-Part III Radio Commun. Eng."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"1160","DOI":"10.1364\/JOSAA.2.001160","article-title":"Uncertainty relation for resolution in space, spatial frequency, and orientation optimized by two-dimensional visual cortical filters","volume":"2","author":"Daugman","year":"1985","journal-title":"JOSA A"},{"key":"ref_16","unstructured":"Duits, R. (2005). Perceptual Organization in Image Analysis: A Mathematical Approach Based on Scale, Orientation and Curvature. [Ph.D. Thesis, Technische Universiteit Eindhoven]."},{"key":"ref_17","first-page":"116","article-title":"Retinal Vessel Segmentation Based on Multi-scale Frangi Filter","volume":"4","author":"Pan","year":"2020","journal-title":"Mod. Inform. Technol."},{"key":"ref_18","first-page":"2228","article-title":"New approach to segment retinal vessel using morphology and Otsu","volume":"36","author":"Wang","year":"2019","journal-title":"Appl. Res. Comput."},{"key":"ref_19","doi-asserted-by":"crossref","unstructured":"Oliveira, W.S., Teixeira, J.V., Ren, T.I., Cavalcanti, G., and Sijbers, J. (2016). Unsupervised Retinal Vessel Segmentation Using Combined Filters. PLoS ONE, 11.","DOI":"10.1371\/journal.pone.0149943"},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"4897258","DOI":"10.1155\/2017\/4897258","article-title":"Blood Vessel Extraction in Color Retinal Fundus Images with Enhancement Filtering and Unsupervised Classification","volume":"2017","author":"Yavuz","year":"2017","journal-title":"J. Healthc. Eng."},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"619","DOI":"10.1109\/42.363106","article-title":"The detection and quantification of retinopathy using digital angiograms","volume":"13","author":"Zhou","year":"1994","journal-title":"IEEE Trans. Med. Imaging"},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"600","DOI":"10.1016\/j.cmpb.2011.08.009","article-title":"An approach to localize the retinal blood vessels using bit planes and centerline detection","volume":"108","author":"Fraz","year":"2012","journal-title":"Comput. Methods Programs Biomed."},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"131","DOI":"10.1109\/TPAMI.2003.1159954","article-title":"Adaptive local thresholding by verification-based multi threshold probing with application to vessel detection in retinal images","volume":"25","author":"Jiang","year":"2003","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"1651","DOI":"10.1364\/JOSAA.3.001651","article-title":"Analysis of the retinex theory of color vision","volume":"3","author":"Brainard","year":"1986","journal-title":"J. Opt. Soc. Am. A"},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"965","DOI":"10.1109\/83.597272","article-title":"A multiscale retinex for bridging the gap between color images and the human observation of scenes","volume":"6","author":"Jobson","year":"1997","journal-title":"IEEE Trans. Image Process."},{"key":"ref_26","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_27","first-page":"2856","article-title":"Multi-level threshold image segmentation algorithm based on particle swarm optimization and fuzzy entropy","volume":"36","author":"Fuqi","year":"2019","journal-title":"Appl. Res. Comput."},{"key":"ref_28","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_29","doi-asserted-by":"crossref","first-page":"600","DOI":"10.1109\/TIP.2003.819861","article-title":"Image Quality Assessment: From Error Visibility to Structural Similarity","volume":"13","author":"Wang","year":"2004","journal-title":"IEEE Trans. Image Process."},{"key":"ref_30","doi-asserted-by":"crossref","unstructured":"Fan, D.P., Cheng, M.M., Liu, Y., Li, T., and Borji, A. (2017, January 22\u201329). Structure-Measure: A New Way to Evaluate Foreground Maps. Proceedings of the IEEE International Conference on Computer Vision, Venice, Italy.","DOI":"10.1109\/ICCV.2017.487"},{"key":"ref_31","doi-asserted-by":"crossref","unstructured":"Dasgupta, A., and Singh, S. (2017, January 18\u201321). A Fully Convolutional Neural Network Based Structured Prediction Approach towards the Retinal Vessel Segmentation. Proceedings of the 14th International Symposium on Biomedical Imaging (ISBI), Melbourne, Australia.","DOI":"10.1109\/ISBI.2017.7950512"},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"1529","DOI":"10.1007\/s11760-019-01501-9","article-title":"Discriminative dictionary learning for retinal vessel segmentation using fusion of multiple features","volume":"13","author":"Yang","year":"2019","journal-title":"Signal Image Video Process."},{"key":"ref_33","doi-asserted-by":"crossref","unstructured":"Adapa, D., Raj, A.N.J., Alisetti, S.N., Zhuang, Z., and Naik, G. (2020). A supervised blood vessel segmentation technique for digital Fundus images using Zernike Moment based features. PLoS ONE, 15.","DOI":"10.1371\/journal.pone.0229831"},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"389","DOI":"10.1049\/iet-ipr.2017.0329","article-title":"Robust retinal blood vessel segmentation using line detectors with multiple masks","volume":"12","author":"Biswal","year":"2018","journal-title":"IET Image Process."},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"159","DOI":"10.1007\/s00521-016-2811-9","article-title":"Noise-estimation-based anisotropic diffusion approach for retinal blood vessel segmentation","volume":"29","author":"Azar","year":"2018","journal-title":"Neural Comput. Appl."},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"34839","DOI":"10.1007\/s11042-019-08111-0","article-title":"Blood vessel segmentation of retinal image using Clifford matched filter and Clifford convolution","volume":"78","author":"Roy","year":"2019","journal-title":"Multimed. Tools Appl."}],"container-title":["Sensors"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1424-8220\/22\/3\/1258\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T22:15:28Z","timestamp":1760134528000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1424-8220\/22\/3\/1258"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,2,7]]},"references-count":36,"journal-issue":{"issue":"3","published-online":{"date-parts":[[2022,2]]}},"alternative-id":["s22031258"],"URL":"https:\/\/doi.org\/10.3390\/s22031258","relation":{},"ISSN":["1424-8220"],"issn-type":[{"type":"electronic","value":"1424-8220"}],"subject":[],"published":{"date-parts":[[2022,2,7]]}}}