{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,14]],"date-time":"2026-03-14T04:27:33Z","timestamp":1773462453386,"version":"3.50.1"},"reference-count":27,"publisher":"MDPI AG","issue":"5","license":[{"start":{"date-parts":[[2021,3,2]],"date-time":"2021-03-02T00:00:00Z","timestamp":1614643200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Resources for Research by the Ministry of Science and Higher Education","award":["WZ\/WI-IIT\/4\/2020"],"award-info":[{"award-number":["WZ\/WI-IIT\/4\/2020"]}]},{"name":"AGH University of Science and Technology in Krakow","award":["doctoral scholarship IUVENES \u2013 KNOW"],"award-info":[{"award-number":["doctoral scholarship IUVENES \u2013 KNOW"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>This paper presents an algorithm for segmentation and shape analysis of erythrocyte images collected using an optical microscope. The main objective of the proposed approach is to compute statistical object values such as the number of erythrocytes in the image, their size, and width to height ratio. A median filter, a mean filter and a bilateral filter were used for initial noise reduction. Background subtraction using a rolling ball filter removes background irregularities. Combining the distance transform with the Otsu and watershed segmentation methods allows for initial image segmentation. Further processing steps, including morphological transforms and the previously mentioned segmentation methods, were applied to each segmented cell, resulting in an accurate segmentation. Finally, the noise standard deviation, sensitivity, specificity, precision, negative predictive value, accuracy and the number of detected objects are calculated. The presented approach shows that the second stage of the two-stage segmentation algorithm applied to individual cells segmented in the first stage allows increasing the precision from 0.857 to 0.968 for the artificial image example tested in this paper. The next step of the algorithm is to categorize segmented erythrocytes to identify poorly segmented and abnormal ones, thus automating this process, previously often done manually by specialists. The presented segmentation technique is also applicable as a probability map processor in the deep learning pipeline. The presented two-stage processing introduces a promising fusion model presented by the authors for the first time.<\/jats:p>","DOI":"10.3390\/s21051720","type":"journal-article","created":{"date-parts":[[2021,3,2]],"date-time":"2021-03-02T10:36:37Z","timestamp":1614681397000},"page":"1720","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":5,"title":["Segmentation of Microscope Erythrocyte Images by CNN-Enhanced Algorithms"],"prefix":"10.3390","volume":"21","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-9920-8749","authenticated-orcid":false,"given":"Mateusz","family":"Buczkowski","sequence":"first","affiliation":[{"name":"Faculty of Physics and Applied Computer Science, AGH University of Science and Technology, aleja Adama Mickiewicza 30, 30-059 Krakow, Poland"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3215-8860","authenticated-orcid":false,"given":"Piotr","family":"Szymkowski","sequence":"additional","affiliation":[{"name":"Faculty of Computer Science, Bialystok University of Technology, ul. Wiejska 45A, 15-351 Bialystok, Poland"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7741-7045","authenticated-orcid":false,"given":"Khalid","family":"Saeed","sequence":"additional","affiliation":[{"name":"Faculty of Computer Science, Bialystok University of Technology, ul. Wiejska 45A, 15-351 Bialystok, Poland"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2021,3,2]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Saeed, E., Szymkowski, M., Saeed, K., and Mariak, Z. (2019). An Approach to Automatic Hard Exudate Detection in Retina Color Images by a Telemedicine System Based on the d-Eye Sensor and Image Processing Algorithms. Sensors, 19.","DOI":"10.3390\/s19030695"},{"key":"ref_2","doi-asserted-by":"crossref","unstructured":"Loddo, A., Di Ruberto, C., and Kocher, M. (2018). Recent advances of malaria parasites detection systems based on mathematical morphology. Sensors, 18.","DOI":"10.3390\/s18020513"},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"7068349","DOI":"10.1155\/2018\/7068349","article-title":"Deep Learning for Computer Vision: A Brief Review","volume":"2018","author":"Voulodimos","year":"2018","journal-title":"Comput. Intell. Neurosci."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"336","DOI":"10.1016\/j.compeleceng.2015.04.009","article-title":"Segmentation of erythrocytes infected with malaria parasites for the diagnosis using microscopy imaging","volume":"45","author":"Somasekar","year":"2014","journal-title":"Comput. Electr. Eng."},{"key":"ref_5","doi-asserted-by":"crossref","unstructured":"Bergen, T., Steckhan, D., Wittenberg, T., and Zerfass, T. (2008, January 20\u201325). Segmentation of leukocytes and erythrocytes in blood smear images. Proceedings of the 2008 30th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, Vancouver, BC, Canada.","DOI":"10.1109\/IEMBS.2008.4649853"},{"key":"ref_6","doi-asserted-by":"crossref","unstructured":"Ronneberger, O., Fischer, P., and Brox, T. (2015, January 5\u20139). U-net: Convolutional networks for biomedical image segmentation. Proceedings of the Medical Image Computing and Computer-Assigned Intervention\u2014MICCAI 2015, Munich, Germany.","DOI":"10.1007\/978-3-319-24574-4_28"},{"key":"ref_7","doi-asserted-by":"crossref","unstructured":"Garcia-Garcia, A., Orts-Escolano, S., Oprea, S.O., Villena-Martinez, V., and Garcia-Rodriguez, J. (2017). A Review on Deep Learning Techniques Applied to Semantic Segmentation. arXiv.","DOI":"10.1016\/j.asoc.2018.05.018"},{"key":"ref_8","first-page":"849","article-title":"Deep learning for damaged tissue detection and segmentation in ki-67 brain tumor specimens based on the u-net model","volume":"66","author":"Markiewicz","year":"2018","journal-title":"Bull. Pol. Acad. Sci. Tech. Sci."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"147","DOI":"10.1016\/j.compbiomed.2018.05.015","article-title":"Segmentation of histological images and fibrosis identification with a convolutional neural network","volume":"98","author":"Fu","year":"2018","journal-title":"Comput. Biol. Med."},{"key":"ref_10","doi-asserted-by":"crossref","unstructured":"Pratt, W.K. (2007). Digital Image Processing, Wiley-Interscience. [4th ed.].","DOI":"10.1002\/0470097434"},{"key":"ref_11","doi-asserted-by":"crossref","unstructured":"Suhas, S., and Venugopal, C.R. (2017, January 15\u201316). Mri image preprocessing and noise removal technique using linear and nonlinear filters. Proceedings of the 2017 International Conference on Electrical, Electronics, Communication, Computer, and Optimization Techniques (ICEECCOT), Mysuru, India.","DOI":"10.1109\/ICEECCOT.2017.8284595"},{"key":"ref_12","doi-asserted-by":"crossref","unstructured":"Hunnur, S.S., Raut, A., and Kulkarni, S. (2017, January 18\u201319). Implementation of image processing for detection of brain tumors. Proceedings of the 2017 International Conference on Computing Methodologies and Communication (ICCMC), Erode, India.","DOI":"10.1109\/ICCMC.2017.8282559"},{"key":"ref_13","doi-asserted-by":"crossref","unstructured":"Shreyamsha, K.B.K. (2013). Image denoising based on gaussian\/bilateral filter and its method noise thresholding. Signal Image and Video Processing, Springer.","DOI":"10.1007\/s11760-012-0372-7"},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"3867","DOI":"10.1088\/0031-9155\/61\/10\/3867","article-title":"Bilateral filtering using the full noise covariance matrix applied to x-ray phase-contrast computed tomography","volume":"61","author":"Allner","year":"2016","journal-title":"Phys. Med. Biol."},{"key":"ref_15","doi-asserted-by":"crossref","unstructured":"Buczkowski, M., and Saeed, K. (2015). Fusion-based noisy image segmentation method. Advanced Computing and Systems for Security, Springer. Volume 396 of the Series Advances in Intelligent Systems and Computing.","DOI":"10.1007\/978-81-322-2653-6_2"},{"key":"ref_16","first-page":"22","article-title":"Biomedical image processing","volume":"16","author":"Sternberg","year":"1983","journal-title":"IEEE Comput. Comput. Archit. Image Process."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"676","DOI":"10.1038\/nmeth.2019","article-title":"Fiji: An open-source platform for biological-image analysis","volume":"9","author":"Schindelin","year":"2012","journal-title":"Nat. Methods"},{"key":"ref_18","unstructured":"Gonzalez, R.C., and Woods, R.E. (2008). Digital Image Processing, Pearson Prentice Hall. [3rd ed.]."},{"key":"ref_19","first-page":"713","article-title":"A fast algorithm for multilevel thresholding","volume":"17","year":"2008","journal-title":"J. Inf. Sci. Eng."},{"key":"ref_20","unstructured":"Fabija\u0144ska, A. (2010, January 20\u201323). A survey of thresholding algorithms on yarn images. Proceedings of the 2010 VIth International Conference on Perspective Technologies and Methods in MEMS Design, Lviv, Ukraine."},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"53","DOI":"10.1016\/j.bspc.2015.11.002","article-title":"Automatic multi-organ segmentation of prostate magnetic resonance images using watershed and nonsubsampled contourlet transform","volume":"25","author":"Huang","year":"2016","journal-title":"Biomed. Signal Process. Control"},{"key":"ref_22","unstructured":"(2018, July 19). Insight Toolkit (itk). Available online: https:\/\/itk.org\/."},{"key":"ref_23","first-page":"1","article-title":"Label object representation and manipulation with itk","volume":"8","author":"Lehmann","year":"2007","journal-title":"Insight J."},{"key":"ref_24","doi-asserted-by":"crossref","unstructured":"Soille, P. (2003). Morphological Image Analysis Principles and Applications, Springer.","DOI":"10.1007\/978-3-662-05088-0"},{"key":"ref_25","doi-asserted-by":"crossref","unstructured":"Caicedo, J., Goodman, A., Karhohs, K., Cimini, B., Ackerman, J., Haghighi, M., Heng, C., Becker, T., Doan, M., and McQuin, C. (2020). Publisher correction: Nucleus segmentation across imaging experiments: The 2018 data science bowl. Nat. Methods, 1247\u20131253.","DOI":"10.1038\/s41592-019-0612-7"},{"key":"ref_26","doi-asserted-by":"crossref","unstructured":"Sandler, M., Howard, A., Zhu, M., Zhmoginov, A., and Chen, L.-C. (2018, January 18\u201322). Mobilenetv2: Inverted residuals and linear bottlenecks. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Salt Lake City, UT, USA.","DOI":"10.1109\/CVPR.2018.00474"},{"key":"ref_27","first-page":"799","article-title":"Speeding-up convolutional neural networks: A survey","volume":"66","author":"Lebedev","year":"2018","journal-title":"Bull. Pol. Acad. Sci. Tech. Sci."}],"container-title":["Sensors"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1424-8220\/21\/5\/1720\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T05:31:24Z","timestamp":1760160684000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1424-8220\/21\/5\/1720"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021,3,2]]},"references-count":27,"journal-issue":{"issue":"5","published-online":{"date-parts":[[2021,3]]}},"alternative-id":["s21051720"],"URL":"https:\/\/doi.org\/10.3390\/s21051720","relation":{},"ISSN":["1424-8220"],"issn-type":[{"value":"1424-8220","type":"electronic"}],"subject":[],"published":{"date-parts":[[2021,3,2]]}}}