{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,26]],"date-time":"2026-09-26T17:18:53Z","timestamp":1790443133705,"version":"4.1.0"},"reference-count":46,"publisher":"MDPI AG","issue":"2","license":[{"start":{"date-parts":[[2021,1,28]],"date-time":"2021-01-28T00:00:00Z","timestamp":1611792000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Diagnostics"],"abstract":"<jats:p>In large bowel investigations using endoscopic capsules and upon detection of significant findings, physicians require the location of those findings for a follow-up therapeutic colonoscopy. To cater to this need, we propose a model based on tracking feature points in consecutive frames of videos retrieved from colon capsule endoscopy investigations. By locally approximating the colon as a cylinder, we obtained both the displacement and the orientation of the capsule using geometrical assumptions and by setting priors on both physical properties of the intestine and the image sample frequency of the endoscopic capsule. Our proposed model tracks a colon capsule endoscope through the large intestine for different prior selections. A discussion on validating the findings in terms of intra and inter capsule and expert panel validation is provided. The performance of the model is evaluated based on the average difference in multiple reconstructed capsule\u2019s paths through the large intestine. The path difference averaged over all videos was as low as 4\u00b10.7 cm, with min and max error corresponding to 1.2 and 6.0 cm, respectively. The inter comparison addresses frame classification for the rectum, descending and sigmoid, splenic flexure, transverse, hepatic, and ascending, with an average accuracy of 86%.<\/jats:p>","DOI":"10.3390\/diagnostics11020193","type":"journal-article","created":{"date-parts":[[2021,1,28]],"date-time":"2021-01-28T22:02:01Z","timestamp":1611871321000},"page":"193","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":21,"title":["Feature Point Tracking-Based Localization of Colon Capsule Endoscope"],"prefix":"10.3390","volume":"11","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-1799-8551","authenticated-orcid":false,"given":"J\u00fcrgen","family":"Herp","sequence":"first","affiliation":[{"name":"Faculty of Engineering, Applied Artificial Intelligence and Data Science, Maersk Mc-Kinney Moller Institute, University of Southern Denmark, 5230 Odense, Denmark"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8263-2989","authenticated-orcid":false,"given":"Ulrik","family":"Deding","sequence":"additional","affiliation":[{"name":"Institute of Clinical Research, University of Southern Denmark, 5230 Odense, Denmark"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Maria M.","family":"Buijs","sequence":"additional","affiliation":[{"name":"Institute of Clinical Research, University of Southern Denmark, 5230 Odense, Denmark"},{"name":"Department of Surgery, Odense University Hospital, 5700 Svendborg, Denmark"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4358-7916","authenticated-orcid":false,"given":"Rasmus","family":"Kroijer","sequence":"additional","affiliation":[{"name":"Institute of Clinical Research, University of Southern Denmark, 5230 Odense, Denmark"},{"name":"Department of Surgery, Odense University Hospital, 5700 Svendborg, Denmark"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0300-5766","authenticated-orcid":false,"given":"Gunnar","family":"Baatrup","sequence":"additional","affiliation":[{"name":"Institute of Clinical Research, University of Southern Denmark, 5230 Odense, Denmark"},{"name":"Department of Surgery, Odense University Hospital, 5700 Svendborg, Denmark"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2613-2696","authenticated-orcid":false,"given":"Esmaeil S.","family":"Nadimi","sequence":"additional","affiliation":[{"name":"Faculty of Engineering, Applied Artificial Intelligence and Data Science, Maersk Mc-Kinney Moller Institute, University of Southern Denmark, 5230 Odense, Denmark"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2021,1,28]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"782","DOI":"10.1177\/2050640620937593","article-title":"Colon Capsule Endoscopy vs CT colonography after incomplete colonoscopy. Application of Artificial Intelligence Algorithms to identify complete colonic investigations","volume":"8","author":"Deding","year":"2020","journal-title":"United Eur. Gastroenterol. J."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"13","DOI":"10.4253\/wjge.v7.i1.13","article-title":"Small bowel capsule endoscopy: Where are we after almost 15 years of use?","volume":"7","author":"Hindryckx","year":"2015","journal-title":"World J. Gastrointest. Endosc."},{"key":"ref_3","doi-asserted-by":"crossref","unstructured":"Dmitry, M., Igor, Z., Vladimir, K., Andrey, S., Timur, K., Anastasia, T., and Alexander, K. (2014, January 13\u201316). Review of features and metafeatures allowing recognition of abnormalities in the images of GIT. Proceedings of the MELECON 2014\u20142014 17th IEEE Mediterranean Electrotechnical Conference, Beirut, Lebanon.","DOI":"10.1109\/MELCON.2014.6820537"},{"key":"ref_4","doi-asserted-by":"crossref","unstructured":"Tsakalakis, M., and Bourbakis, N.G. (2014, January 26\u201330). Health care sensor\u2014Based systems for point of care monitoring and diagnostic applications: A brief survey. Proceedings of the 2014 36th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, Chicago, IL, USA.","DOI":"10.1109\/EMBC.2014.6945061"},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"1488","DOI":"10.1109\/TMI.2014.2314959","article-title":"Automated Polyp Detection in Colon Capsule Endoscopy","volume":"33","author":"Mamonov","year":"2014","journal-title":"IEEE Trans. Med. Imaging"},{"key":"ref_6","unstructured":"Yu, L., Yuen, P.C., and Lai, J. (2012, January 11\u201315). Ulcer detection in wireless capsule endoscopy images. Proceedings of the 21st International Conference on Pattern Recognition (ICPR2012), Tsukuba, Japan."},{"key":"ref_7","doi-asserted-by":"crossref","unstructured":"Karargyris, A., and Bourbakis, N. (2009, January 9\u201310). Identification of polyps in Wireless Capsule Endoscopy videos using Log Gabor filters. Proceedings of the 2009 IEEE\/NIH Life Science Systems and Applications Workshop, Bethesda, MD, USA.","DOI":"10.1109\/LISSA.2009.4906730"},{"key":"ref_8","doi-asserted-by":"crossref","unstructured":"Jebarani, W.S.L., and Daisy, V.J. (2013, January 7\u20138). Assessment of Crohn\u2019s disease lesions in Wireless Capsule Endoscopy images using SVM based classification. Proceedings of the 2013 International Conference on Signal Processing, Image Processing Pattern Recognition, Coimbatore, India.","DOI":"10.1109\/ICSIPR.2013.6497945"},{"key":"ref_9","unstructured":"Bourbakis, N. (2005, January 19\u201321). Detecting abnormal patterns in WCE images. Proceedings of the Fifth IEEE Symposium on Bioinformatics and Bioengineering (BIBE\u201905), Minneapolis, MN, USA."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"72","DOI":"10.1109\/MEMB.2009.935466","article-title":"Wireless Capsule Endoscopy and Endoscopic Imaging: A Survey on Various Methodologies Presented","volume":"29","author":"Karargyris","year":"2010","journal-title":"IEEE Eng. Med. Biol. Mag."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"411","DOI":"10.5217\/ir.2017.15.3.411","article-title":"Miss rate of colorectal neoplastic polyps and risk factors for missed polyps in consecutive colonoscopies","volume":"15","author":"Kim","year":"2017","journal-title":"Intest. Res."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"343","DOI":"10.1111\/j.1572-0241.2006.00390.x","article-title":"Polyp miss rate determined by tandem colonoscopy: A systematic review","volume":"101","author":"Reitsma","year":"2006","journal-title":"Am. J. Gastroenterol."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"479","DOI":"10.1111\/codi.13965","article-title":"Back-to-back colon capsule endoscopy and optical colonoscopy in colorectal cancer screening individuals","volume":"20","author":"Kroijer","year":"2018","journal-title":"Color. Dis."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"532","DOI":"10.1111\/codi.14557","article-title":"Colon capsule endoscopy for colonic surveillance","volume":"21","author":"Kroijer","year":"2019","journal-title":"Color. Dis."},{"key":"ref_15","doi-asserted-by":"crossref","unstructured":"Farhadi, H., Atai, J., Skoglund, M., Nadimi, E.S., Pahlavan, K., and Tarokh, V. (2016, January 20\u201323). An adaptive localization technique for wireless capsule endoscopy. Proceedings of the 2016 10th International Symposium on Medical Information and Communication Technology (ISMICT), Worcester, MA, USA.","DOI":"10.1109\/ISMICT.2016.7498884"},{"key":"ref_16","doi-asserted-by":"crossref","unstructured":"Wang, H., Zhang, Y., Yu, X., and Wang, G. (2016, January 16\u201319). Positioning algorithm for wireless capsule endoscopy based on RSS. Proceedings of the 2016 IEEE International Conference on Ubiquitous Wireless Broadband (ICUWB), Nanjing, China.","DOI":"10.1109\/ICUWB.2016.7790599"},{"key":"ref_17","doi-asserted-by":"crossref","unstructured":"Nadimi, E.S., Blanes-Vidal, V., Tarokh, V., and Johansen, P.M. (2014, January 26\u201330). Bayesian-based localization of wireless capsule endoscope using received signal strength. Proceedings of the 2014 36th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, Chicago, IL, USA.","DOI":"10.1109\/EMBC.2014.6944993"},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"189","DOI":"10.1007\/s10776-017-0349-0","article-title":"Adaptive Wireless Biomedical Capsule Tracking Based on Magnetic Sensing","volume":"24","author":"Umay","year":"2017","journal-title":"Int. J. Wirel. Inf. Netw."},{"key":"ref_19","unstructured":"Wahid, K., Kabir, S.M.L., Khan, H.A., Helal, A.A., Mukit, M.A., and Mostafa, R. (2016, January 17\u201318). A localization algorithm for capsule endoscopy based on feature point tracking. Proceedings of the 2016 International Conference on Medical Engineering, Health Informatics and Technology (MediTec), Dhaka, Bangladesh."},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"2669","DOI":"10.1109\/JSEN.2014.2367495","article-title":"Hybrid Localization of Microrobotic Endoscopic Capsule Inside Small Intestine by Data Fusion of Vision and RF Sensors","volume":"15","author":"Bao","year":"2015","journal-title":"IEEE Sens. J."},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"015002","DOI":"10.1088\/0957-0233\/25\/1\/015002","article-title":"Video-based measurements for wireless capsule endoscope tracking","volume":"25","author":"Spyrou","year":"2014","journal-title":"Meas. Sci. Technol."},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"381","DOI":"10.1080\/17434440.2019.1608182","article-title":"Localization strategies for robotic endoscopic capsules: A review","volume":"16","author":"Bianchi","year":"2019","journal-title":"Expert Rev. Med. Devices"},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"1197","DOI":"10.1109\/JSEN.2016.2645945","article-title":"Localization of Wireless Capsule Endoscope: A Systematic Review","volume":"17","author":"Mateen","year":"2017","journal-title":"IEEE Sens. J."},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"169","DOI":"10.1007\/s10776-017-0342-7","article-title":"Fundamental Limits of TOA\/DOA and Inertial Measurement Unit-Based Wireless Capsule Endoscopy Hybrid Localization","volume":"24","author":"Jeong","year":"2017","journal-title":"Int. J. Wirel. Inf. Netw."},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"135","DOI":"10.1049\/htl.2015.0024","article-title":"In vivo and in situ measurement and modelling of intra-body effective complex permittivity","volume":"2","author":"Nadimi","year":"2015","journal-title":"Health Technol. Lett."},{"key":"ref_26","doi-asserted-by":"crossref","unstructured":"Ramezani, M.H., Blanes-Vidal, V., and Nadimi, E.S. (2015). Adaptive Intra-body Channel Modeling of Attenuation Coefficient Using Transmission Line Theory. Proceedings of the 2015 Conference on Research in Adaptive and Convergent Systems, ACM.","DOI":"10.1145\/2811411.2811525"},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"1","DOI":"10.2528\/PIER15030702","article-title":"An Adaptive Path Loss Channel Model for Wave Propagation in Multilayer Transmission Medium","volume":"150","author":"Ramezani","year":"2015","journal-title":"Prog. Electromagn. Res. PIER"},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"E205","DOI":"10.1055\/s-0043-121882","article-title":"Novel experimental and software methods for image reconstruction and localization in capsule endoscopy","volume":"6","author":"Koulaouzidis","year":"2018","journal-title":"Endosc. Int. Open"},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"094005","DOI":"10.1088\/1361-6501\/aa7ebf","article-title":"An artificial neural network architecture for non-parametric visual odometry in wireless capsule endoscopy","volume":"28","author":"Dimas","year":"2017","journal-title":"Meas. Sci. Technol."},{"key":"ref_30","doi-asserted-by":"crossref","unstructured":"Iakovidis, D.K., Dimas, G., Karargyris, A., Ciuti, G., Bianchi, F., Koulaouzidis, A., and Toth, E. (2016, January 4\u20136). Robotic validation of visual odometry for wireless capsule endoscopy. Proceedings of the 2016 IEEE International Conference on Imaging Systems and Techniques (IST), Chania, Greece.","DOI":"10.1109\/IST.2016.7738202"},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"429","DOI":"10.1016\/j.compbiomed.2017.08.029","article-title":"Intelligent visual localization of wireless capsule endoscopes enhanced by color information","volume":"89","author":"Dimas","year":"2017","journal-title":"Comput. Biol. Med."},{"key":"ref_32","doi-asserted-by":"crossref","unstructured":"Dimas, G., Iakovidis, D.K., Ciuti, G., Karargyris, A., and Koulaouzidis, A. (2017, January 22\u201324). Visual Localization of Wireless Capsule Endoscopes Aided by Artificial Neural Networks. Proceedings of the 2017 IEEE 30th International Symposium on Computer-Based Medical Systems (CBMS), Thessaloniki, Greece.","DOI":"10.1109\/CBMS.2017.67"},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"346","DOI":"10.1016\/j.cviu.2007.09.014","article-title":"Speeded-Up Robust Features (SURF)","volume":"110","author":"Bay","year":"2008","journal-title":"Comput. Vis. Image Underst."},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"381","DOI":"10.1145\/358669.358692","article-title":"Random Sample Consensus: A Paradigm for Model Fitting with Applications to Image Analysis and Automated Cartography","volume":"24","author":"Fischler","year":"1981","journal-title":"Commun. ACM"},{"key":"ref_35","doi-asserted-by":"crossref","unstructured":"van der Putten, J., de Groof, J., van der Sommen, F., Struyvenberg, M., Zinger, S., Curvers, W., Schoon, E., Bergman, J., and de With, P.H.N. (2019, January 22\u201325). Informative Frame Classification of Endoscopic Videos Using Convolutional Neural Networks and Hidden Markov Models. Proceedings of the 2019 IEEE International Conference on Image Processing (ICIP), Taipei, Taiwan.","DOI":"10.1109\/ICIP.2019.8802947"},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"E1044","DOI":"10.1055\/a-0627-7136","article-title":"Assessment of bowel cleansing quality in colon capsule endoscopy using machine learning: A pilot study","volume":"6","author":"Buijs","year":"2018","journal-title":"Endosc. Int. Open"},{"key":"ref_37","doi-asserted-by":"crossref","unstructured":"Parmar, R., Martel, M., Rostom, A., and Barkun, A.N. (2016). Validated Scales for Colon Cleansing: A Systematic Review. Am. J. Gastroenterol., 111.","DOI":"10.1038\/ajg.2015.417"},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"E501","DOI":"10.1055\/s-0034-1392109","article-title":"Clean Colon Software Program (CCSP), Proposal of a standardized Method to quantify Colon Cleansing During Colonoscopy: Preliminary Results","volume":"3","author":"Dupuis","year":"2015","journal-title":"Endosc. Int. Open"},{"key":"ref_39","unstructured":"Navidi, W. (2011). Statistics for Engineers and Scientists, McGraw-Hill Education."},{"key":"ref_40","unstructured":"Walpole, R.E., Myers, R.H., Myers, S.L., and Ye, K. (2011). Probability & Statistics for Engineers and Scientists, Pearson Education. [9th ed.]."},{"key":"ref_41","unstructured":"Gilroy, A., MacPherson, B., and Ross, L. (2012). Atlas of Anatomy, Thieme. Thieme Anatomy."},{"key":"ref_42","doi-asserted-by":"crossref","first-page":"9394","DOI":"10.3748\/wjg.v22.i42.9394","article-title":"Irritable bowel syndrome evaluation using computed tomography colonography","volume":"22","author":"Ohgo","year":"2016","journal-title":"World J. Gastroenterol."},{"key":"ref_43","doi-asserted-by":"crossref","unstructured":"Nadimi, E.S., Herp, J., Buijs, M.M., and Blanes-Vidal, V. (2017, January 25\u201328). Texture classification from single uncalibrated images: Random matrix theory approach. Proceedings of the 2017 IEEE 27th International Workshop on Machine Learning for Signal Processing (MLSP), Tokyo, Japan.","DOI":"10.1109\/MLSP.2017.8168115"},{"key":"ref_44","doi-asserted-by":"crossref","first-page":"1379","DOI":"10.1002\/mp.12147","article-title":"Deep learning for polyp recognition in wireless capsule endoscopy images","volume":"44","author":"Yuan","year":"2017","journal-title":"Med. Phys."},{"key":"ref_45","doi-asserted-by":"crossref","unstructured":"Brandao, P., Zisimopoulos, O., Mazomenos, E., Ciuti, G., Bernal, J., Visentini-Scarzanella, M., Menciassi, A., Dario, P., Koulaouzidis, A., and Arezzo, A. (2018). Towards a Computed-Aided Diagnosis System in Colonoscopy: Automatic Polyp Segmentation Using Convolution Neural Networks. J. Med. Robot. Res., 3.","DOI":"10.1142\/S2424905X18400020"},{"key":"ref_46","doi-asserted-by":"crossref","unstructured":"S. Nadimi, E., Buijs, M., Herp, J., Kr\u00f8ijer, R., Kobaek-Larsen, M., Nielsen, E., Duedal Pedersen, C., Blanes-Vidal, V., and Baatrup, G. (2020). Application of Deep Learning for Autonomous Detection and Localization of Colorectal Polyps in Wireless Colon Capsule Endoscopy. Comput. Electr. Eng., 81.","DOI":"10.1016\/j.compeleceng.2019.106531"}],"container-title":["Diagnostics"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2075-4418\/11\/2\/193\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T05:16:52Z","timestamp":1760159812000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2075-4418\/11\/2\/193"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021,1,28]]},"references-count":46,"journal-issue":{"issue":"2","published-online":{"date-parts":[[2021,2]]}},"alternative-id":["diagnostics11020193"],"URL":"https:\/\/doi.org\/10.3390\/diagnostics11020193","relation":{},"ISSN":["2075-4418"],"issn-type":[{"value":"2075-4418","type":"electronic"}],"subject":[],"published":{"date-parts":[[2021,1,28]]}}}