{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,14]],"date-time":"2025-10-14T00:40:49Z","timestamp":1760402449854,"version":"build-2065373602"},"reference-count":40,"publisher":"MDPI AG","issue":"4","license":[{"start":{"date-parts":[[2021,4,1]],"date-time":"2021-04-01T00:00:00Z","timestamp":1617235200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100000266","name":"Engineering and Physical Sciences Research Council","doi-asserted-by":"publisher","award":["EP\/R041407\/1"],"award-info":[{"award-number":["EP\/R041407\/1"]}],"id":[{"id":"10.13039\/501100000266","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Biosensors"],"abstract":"<jats:p>Calculating an accurate diameter of arbitrary vessel-like shapes from 2D images is of great use in various applications within medical and biomedical fields. Understanding the changes in morphological dimensioning of the biological vessels provides a better understanding of their properties and functionality. Estimating the diameter of the tubes is very challenging as the dimensions change continuously along its length. This paper describes a novel algorithm that estimates the diameter of biological tubes with a continuously changing cross-section. The algorithm, evaluated using various controlled images, provides an automated diameter estimation with higher and better accuracy than manual measurements and provides precise information about the diametrical changes along the tube. It is demonstrated that the automated algorithm provides more accurate results in a much shorter time. This methodology has the potential to speed up diagnostic procedures in a wide range of medical fields.<\/jats:p>","DOI":"10.3390\/bios11040100","type":"journal-article","created":{"date-parts":[[2021,4,1]],"date-time":"2021-04-01T07:23:17Z","timestamp":1617261797000},"page":"100","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Diameter Estimation of Fallopian Tubes Using Visual Sensing"],"prefix":"10.3390","volume":"11","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-6666-7866","authenticated-orcid":false,"given":"Amir M.","family":"Hajiyavand","sequence":"first","affiliation":[{"name":"Mechanical Innovation and Tribology Group, Department of Mechanical Engineering, School of Engineering, University of Birmingham, Birmingham B15 2TT, UK"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Matthew J.","family":"Graham","sequence":"additional","affiliation":[{"name":"Mechanical Innovation and Tribology Group, Department of Mechanical Engineering, School of Engineering, University of Birmingham, Birmingham B15 2TT, UK"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Karl D.","family":"Dearn","sequence":"additional","affiliation":[{"name":"Mechanical Innovation and Tribology Group, Department of Mechanical Engineering, School of Engineering, University of Birmingham, Birmingham B15 2TT, UK"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2021,4,1]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"034006","DOI":"10.1117\/1.JMI.4.3.034006","article-title":"Tracking and diameter estimation of retinal vessels using Gaussian process and Radon transform","volume":"4","author":"Asl","year":"2017","journal-title":"J. Med. Imaging"},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"131","DOI":"10.1109\/TPAMI.2003.1159954","article-title":"Adaptive local thresholding by verification-based multithreshold 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_3","doi-asserted-by":"crossref","first-page":"2136","DOI":"10.1016\/j.compbiomed.2013.10.007","article-title":"Computer-aided diagnosis of diabetic retinopathy: A review","volume":"43","author":"Mookiah","year":"2013","journal-title":"Comput. Biol. Med."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"407","DOI":"10.1016\/j.cmpb.2012.03.009","article-title":"Blood vessel segmentation methodologies in retinal images\u2014A survey","volume":"108","author":"Fraz","year":"2012","journal-title":"Comput. Methods Programs Biomed."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"33","DOI":"10.1111\/j.1755-3768.1986.tb00701.x","article-title":"Theoretical relations between light streak characteristics and optical properties of retinal vessels","volume":"64","author":"Heier","year":"1986","journal-title":"Acta Ophthalmol."},{"key":"ref_6","doi-asserted-by":"crossref","unstructured":"Acharya, K.C., Feiring, A.J., and Skrade, B.L. (1989). Automatic edge detection and diameter calculation of coronary arteries using DSA images. Images of the Twenty-First Century, Proceedings of the Annual International Engineering in Medicine and Biology Society, Seattle, WA, USA, 9\u201312 November 1989, IEEE.","DOI":"10.1109\/IEMBS.1989.95768"},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"169","DOI":"10.1109\/RBME.2010.2084567","article-title":"Retinal imaging and image analysis","volume":"3","author":"Garvin","year":"2010","journal-title":"IEEE Rev. Biomed. Eng."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"2","DOI":"10.1007\/BF02347689","article-title":"Progress towards automated diabetic ocular screening: A review of image analysis and intelligent systems for diabetic retinopathy","volume":"40","author":"Teng","year":"2002","journal-title":"Med. Biol. Eng. Comput."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"2437","DOI":"10.1016\/j.patcog.2014.01.006","article-title":"Retinal vessels segmentation based on level set and region growing","volume":"47","author":"Zhao","year":"2014","journal-title":"Pattern Recognit."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"78","DOI":"10.1109\/42.20365","article-title":"Automated identification of vessel contours in coronary arteriograms by an adaptive tracking algorithm","volume":"8","author":"Sun","year":"1989","journal-title":"IEEE Trans. Med. Imaging"},{"key":"ref_11","first-page":"1258","article-title":"Measurement of retinal blood vessel width using computerized image analysis","volume":"29","author":"Eaton","year":"1988","journal-title":"Investig. Ophthalmol. Vis. Sci."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"1147","DOI":"10.1016\/j.ophtha.2010.03.046","article-title":"Automated early detection of diabetic retinopathy","volume":"117","author":"Reinhardt","year":"2010","journal-title":"Ophthalmology"},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"3546","DOI":"10.1167\/iovs.12-10347","article-title":"Validating retinal fundus image analysis algorithms: Issues and a proposal","volume":"54","author":"Trucco","year":"2013","journal-title":"Investig. Ophthalmol. Vis. Sci."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"2107","DOI":"10.1109\/TMI.2016.2550102","article-title":"Enhancement of vascular structures in 3D and 2D angiographic images","volume":"35","author":"Jerman","year":"2016","journal-title":"IEEE Trans. Med. Imaging"},{"key":"ref_15","first-page":"94132A","article-title":"Beyond Frangi: An improved multiscale vesselness filter","volume":"Volume 9413","author":"Jerman","year":"2015","journal-title":"Proceedings of the Medical Imaging, 2015: Image Processing"},{"key":"ref_16","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_17","doi-asserted-by":"crossref","first-page":"99","DOI":"10.1016\/j.preteyeres.2005.07.001","article-title":"Retinal image analysis: Concepts, applications and potential","volume":"25","author":"Patton","year":"2006","journal-title":"Prog. Retin. Eye Res."},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"819","DOI":"10.1016\/j.media.2009.07.011","article-title":"A review of 3D vessel lumen segmentation techniques: Models, features and extraction schemes","volume":"13","author":"Lesage","year":"2009","journal-title":"Med. Image Anal."},{"key":"ref_19","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_20","doi-asserted-by":"crossref","first-page":"815","DOI":"10.1016\/j.media.2006.06.003","article-title":"Vessel enhancing diffusion: A scale space representation of vessel structures","volume":"10","author":"Manniesing","year":"2006","journal-title":"Med. Image Anal."},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"19","DOI":"10.1007\/BF03192392","article-title":"A fast and accurate approach for computing the dimensions of boxes from single perspective images","volume":"12","author":"Fernandes","year":"2006","journal-title":"J. Braz. Comput. Soc."},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"9","DOI":"10.1016\/j.jvlc.2018.05.002","article-title":"Automatic human body feature extraction and personal size measurement","volume":"47","author":"Tan","year":"2018","journal-title":"J. Vis. Lang. Comput."},{"key":"ref_23","doi-asserted-by":"crossref","unstructured":"Sadak, F., Saadat, M., and Hajiyavand, A.M. (2019). Vision-Based Sensor for Three-Dimensional Vibrational Motion Detection in Biological Cell Injection. Sensors, 19.","DOI":"10.3390\/s19235074"},{"key":"ref_24","doi-asserted-by":"crossref","unstructured":"Hajiyavand, A.M., Saadat, M., and Bedi, A.P.S. (2016, January 18\u201322). Polar body detection for ICSI cell manipulation. Proceedings of the 2016 International Conference on Manipulation, Automation and Robotics at Small Scales (MARSS), Paris, France.","DOI":"10.1109\/MARSS.2016.7561746"},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"303","DOI":"10.1007\/s003710050142","article-title":"Three-dimensional skeleton and centerline generation based on an approximate minimum distance field","volume":"14","author":"Zhou","year":"1998","journal-title":"Vis. Comput."},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"420","DOI":"10.1016\/1049-9652(92)90026-T","article-title":"Generating skeletons and centerlines from the distance transform","volume":"54","author":"Niblack","year":"1992","journal-title":"CVGIP Graph. Models Image Process."},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"846","DOI":"10.1016\/j.media.2009.07.010","article-title":"Automatic generation of 3D coronary artery centerlines using rotational X-ray angiography","volume":"13","author":"Jandt","year":"2009","journal-title":"Med. Image Anal."},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"49","DOI":"10.1007\/BF00414653","article-title":"Measurement of vessel tortuosity on fundus photographs","volume":"211","author":"Lotmar","year":"1979","journal-title":"Albrecht von Graefes Archiv f\u00fcr Klin. und Exp. Ophthalmol."},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"1196","DOI":"10.1109\/TMI.2004.830524","article-title":"Measurement of retinal vessel widths from fundus images based on 2-D modeling","volume":"23","author":"Lowell","year":"2004","journal-title":"IEEE Trans. Med. Imaging"},{"key":"ref_30","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_31","doi-asserted-by":"crossref","first-page":"87","DOI":"10.1007\/s11517-009-0559-4","article-title":"Measurement of retinal vascular tortuosity and its application to retinal pathologies","volume":"48","author":"Dougherty","year":"2010","journal-title":"Med. Biol. Eng. Comput."},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"677","DOI":"10.1016\/j.medengphy.2006.07.008","article-title":"Robust measures of three-dimensional vascular tortuosity based on the minimum curvature of approximating polynomial spline fits to the vessel mid-line","volume":"29","author":"Johnson","year":"2007","journal-title":"Med. Eng. Phys."},{"key":"ref_33","unstructured":"Hunter, A., Lowell, J., Steel, D., Basu, A., and Ryder, R. (2003). Non-Linear Filtering for Vascular Segmentation and Detection of Venous Beading, University of Durham. Technical Report."},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"291","DOI":"10.1006\/cbmr.1995.1020","article-title":"Automated grading of venous beading","volume":"28","author":"Gregson","year":"1995","journal-title":"Comput. Biomed. Res."},{"key":"ref_35","first-page":"1578","article-title":"Sobel edge detection algorithm","volume":"2","author":"Gupta","year":"2013","journal-title":"Int. J. Comput. Sci. Manag. Res."},{"key":"ref_36","unstructured":"Xu, J.W., Pham, T.D., and Zhou, X. (2011, January 4\u20136). A double thresholding method for cancer stem cell detection. Proceedings of the 2011 7th International Symposium on Image and Signal Processing and Analysis (ISPA), Dubrovnik, Croatia."},{"key":"ref_37","unstructured":"Aiger, D., and Guimares, S.J.F. (2021, March 29). Efficient Model Based Single and Double Thresholding for Real Time Recognition. Available online: https:\/\/perso.esiee.fr\/~aigerd\/accv12.pdf."},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"236","DOI":"10.1145\/357994.358023","article-title":"A fast parallel algorithm for thinning digital patterns","volume":"27","author":"Zhang","year":"1984","journal-title":"Commun. ACM"},{"key":"ref_39","doi-asserted-by":"crossref","first-page":"116","DOI":"10.1093\/comjnl\/25.1.116","article-title":"Incremental line compaction","volume":"25","author":"Bresenham","year":"1982","journal-title":"Comput. J."},{"key":"ref_40","unstructured":"Hatami, N., and Goldbaum, M. (2016). Automatic identification of retinal arteries and veins in fundus images using local binary patterns. arXiv."}],"container-title":["Biosensors"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2079-6374\/11\/4\/100\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,13]],"date-time":"2025-10-13T13:33:20Z","timestamp":1760362400000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2079-6374\/11\/4\/100"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021,4,1]]},"references-count":40,"journal-issue":{"issue":"4","published-online":{"date-parts":[[2021,4]]}},"alternative-id":["bios11040100"],"URL":"https:\/\/doi.org\/10.3390\/bios11040100","relation":{},"ISSN":["2079-6374"],"issn-type":[{"type":"electronic","value":"2079-6374"}],"subject":[],"published":{"date-parts":[[2021,4,1]]}}}