{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,6]],"date-time":"2026-05-06T16:02:19Z","timestamp":1778083339249,"version":"3.51.4"},"reference-count":39,"publisher":"MDPI AG","issue":"1","license":[{"start":{"date-parts":[[2023,1,4]],"date-time":"2023-01-04T00:00:00Z","timestamp":1672790400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["J. Imaging"],"abstract":"<jats:p>The paper explored the problem of automatic diagnosis based on immunohistochemical image analysis. The issue of automated diagnosis is a preliminary and advisory statement for a diagnostician. The authors studied breast cancer histological and immunohistochemical images using the following biomarkers progesterone, estrogen, oncoprotein, and a cell proliferation biomarker. The authors developed a breast cancer diagnosis method based on immunohistochemical image analysis. The proposed method consists of algorithms for image preprocessing, segmentation, and the determination of informative indicators (relative area and intensity of cells) and an algorithm for determining the molecular genetic breast cancer subtype. An adaptive algorithm for image preprocessing was developed to improve the quality of the images. It includes median filtering and image brightness equalization techniques. In addition, the authors developed a software module part of the HIAMS software package based on the Java programming language and the OpenCV computer vision library. Four molecular genetic breast cancer subtypes could be identified using this solution: subtype Luminal A, subtype Luminal B, subtype HER2\/neu amplified, and basalt-like subtype. The developed algorithm for the quantitative characteristics of the immunohistochemical images showed sufficient accuracy in determining the cancer subtype \u201cLuminal A\u201d. It was experimentally established that the relative area of the nuclei of cells covered with biomarkers of progesterone, estrogen, and oncoprotein was more than 85%. The given approach allows for automating and accelerating the process of diagnosis. Developed algorithms for calculating the quantitative characteristics of cells on immunohistochemical images can increase the accuracy of diagnosis.<\/jats:p>","DOI":"10.3390\/jimaging9010012","type":"journal-article","created":{"date-parts":[[2023,1,5]],"date-time":"2023-01-05T01:33:30Z","timestamp":1672882410000},"page":"12","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":5,"title":["An Approach toward Automatic Specifics Diagnosis of Breast Cancer Based on an Immunohistochemical Image"],"prefix":"10.3390","volume":"9","author":[{"given":"Oleh","family":"Berezsky","sequence":"first","affiliation":[{"name":"Department of Computer Engineering, West Ukrainian National University, Lviviska, 11, 46003 Ternopil, Ukraine"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0280-8786","authenticated-orcid":false,"given":"Oleh","family":"Pitsun","sequence":"additional","affiliation":[{"name":"Department of Computer Engineering, West Ukrainian National University, Lviviska, 11, 46003 Ternopil, Ukraine"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Grygoriy","family":"Melnyk","sequence":"additional","affiliation":[{"name":"Department of Computer Engineering, West Ukrainian National University, Lviviska, 11, 46003 Ternopil, Ukraine"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Tamara","family":"Datsko","sequence":"additional","affiliation":[{"name":"Department of Pathological Anatomy with Section Course and Forensic Medicine, I. Horbachevsky Ternopil National Medical University, 1 Maidan Voli, 46001 Ternopil, Ukraine"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Ivan","family":"Izonin","sequence":"additional","affiliation":[{"name":"Department of Artificial Intelligence, Lviv Polytechnic National University, 79013 Lviv, Ukraine"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Bohdan","family":"Derysh","sequence":"additional","affiliation":[{"name":"Department of Computer Engineering, West Ukrainian National University, Lviviska, 11, 46003 Ternopil, Ukraine"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2023,1,4]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"267","DOI":"10.1016\/j.breast.2019.12.007","article-title":"Artificial intelligence in digital breast pathology: Techniques and applications","volume":"49","author":"Ibrahim","year":"2020","journal-title":"Breast"},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"106958","DOI":"10.1016\/j.compeleceng.2020.106958","article-title":"An automated breast cancer diagnosis using feature selection and parameter optimization in ANN","volume":"90","author":"Punitha","year":"2021","journal-title":"Comput. Electr. Eng."},{"key":"ref_3","unstructured":"Chattoraj, S., and Vishwakarma, K. (2018). Classification of histopathological breast cancer images using iterative VMD aided Zernike moments & textural signatures. arXiv, Available online: http:\/\/arxiv.org\/abs\/1801.04880."},{"key":"ref_4","doi-asserted-by":"crossref","unstructured":"Cordeiro, C., Ioshii, S., Alves, J., and Oliveira, L. (2018). An Automatic Patch-based Approach for HER-2 Scoring in Immunohistochemical Breast Cancer Images Using Color Features. arXiv, Available online: http:\/\/arxiv.org\/abs\/1805.05392.","DOI":"10.5753\/sbcas.2018.3685"},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"1894","DOI":"10.1016\/j.ejrad.2015.07.002","article-title":"Breast cancer in very young women (<30 years): Correlation of imaging features with clinicopathological features and immunohistochemical subtypes","volume":"84","author":"An","year":"2015","journal-title":"Eur. J. Radiol."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"2612","DOI":"10.1016\/j.ultrasmedbio.2019.06.421","article-title":"Conventional US and 2-D Shear Wave Elastography of Virtual Touch Tissue Imaging Quantification: Correlation with Immunohistochemical Subtypes of Breast Cancer","volume":"45","author":"Liu","year":"2019","journal-title":"Ultrasound Med. Biol."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"34","DOI":"10.20885\/JKKI.Vol12.Iss1.art7","article-title":"Digital image analysis of immunohistochemistry KI-67 using QuPath software in breast cancer","volume":"12","author":"Usman","year":"2021","journal-title":"JKKI"},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"101042831769455","DOI":"10.1177\/1010428317694550","article-title":"Computer-aided prognosis on breast cancer with hematoxylin and eosin histopathology images: A review","volume":"39","author":"Chen","year":"2017","journal-title":"Tumor Biol."},{"key":"ref_9","unstructured":"Holten-Rossing, H., and Klingberg, H. (2019, January 5\u20136). AI deep learning tumour detection directly on ER, PR and KI-67 IHC slides yields a single slide automated workflow with high concordance to manual scoring. Proceedings of the 6th Digital Pathology & AI Congress, London, UK."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"2087","DOI":"10.1016\/j.prp.2018.10.015","article-title":"Comparison between digital image analysis and visual assessment of immunohistochemical HER2 expression in breast cancer","volume":"214","author":"Jakobsen","year":"2018","journal-title":"Pathol.-Res. Pract."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"4605","DOI":"10.2147\/CMAR.S312608","article-title":"Deep Learning-Based Multi-Class Classification of Breast Digital Pathology Images","volume":"13","author":"Mi","year":"2021","journal-title":"CMAR"},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"19","DOI":"10.1016\/j.trsl.2017.10.010","article-title":"Digital image analysis in breast pathology-from image processing techniques to artificial intelligence","volume":"194","author":"Robertson","year":"2017","journal-title":"Transl. Res."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"012018","DOI":"10.1088\/1742-6596\/1642\/1\/012018","article-title":"Application of Artificial Intelligence Technology in Pathological Image Analysis of Breast Tissue","volume":"1642","author":"Jia","year":"2020","journal-title":"J. Phys. Conf. Ser."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"105205","DOI":"10.1016\/j.compbiomed.2021.105205","article-title":"CNN architecture optimization using bio-inspired algorithms for breast cancer detection in infrared images","volume":"142","author":"Gon","year":"2022","journal-title":"Comput. Biol. Med."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"37","DOI":"10.1016\/j.cmpb.2018.08.005","article-title":"Fast unsupervised nuclear segmentation and classification scheme for automatic allred cancer scoring in immunohistochemical breast tissue images","volume":"165","author":"Mouelhi","year":"2018","journal-title":"Comput. Methods Programs Biomed."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"692","DOI":"10.1016\/j.procs.2021.12.065","article-title":"Data warehouse for machine learning: Application to breast cancer diagnosis","volume":"196","author":"Ammar","year":"2022","journal-title":"Procedia Comput. Sci."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"100507","DOI":"10.1016\/j.repbio.2021.100507","article-title":"IHC_Tool: An open-source Fiji procedure for quantitative evaluation of cross sections of testicular explants","volume":"21","author":"Dumont","year":"2021","journal-title":"Reprod. Biol."},{"key":"ref_18","doi-asserted-by":"crossref","unstructured":"Yoshizawa, K., Ando, H., Kimura, Y., Kawashiri, S., Moroi, A., and Ueki, K. (2020). Automatic Machine-Learning Classification of the Mode of Invasion of Oral Squamous Cell Carcinoma Using Digital Microscopic Images: A Retrospective Study. Review, preprint.","DOI":"10.21203\/rs.3.rs-52099\/v1"},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"735","DOI":"10.1007\/s40846-020-00545-4","article-title":"Automatic Detection and Counting of Lymphocytes from Immunohistochemistry Cancer Images Using Deep Learning","volume":"40","author":"Evangeline","year":"2020","journal-title":"J. Med. Biol. Eng."},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"102914","DOI":"10.1016\/j.bspc.2021.102914","article-title":"An automatic Computer-Aided Diagnosis system based on the Multimodal fusion of Breast Cancer (MF-CAD","volume":"69","author":"Mokni","year":"2021","journal-title":"Biomed. Signal Process. Control."},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"3225","DOI":"10.1093\/bioinformatics\/btaa107","article-title":"Marker controlled superpixel nuclei segmentation and automatic counting on immunohistochemistry staining images","volume":"36","author":"Shu","year":"2020","journal-title":"Bioinformatics"},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"135","DOI":"10.1002\/cac2.12023","article-title":"Overview of multiplex immunohistochemistry\/immunofluorescence techniques in the era of cancer immunotherapy","volume":"40","author":"Tan","year":"2020","journal-title":"Cancer Commun."},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"100","DOI":"10.1186\/s13000-020-01003-0","article-title":"Deep learning-based image analysis methods for brightfield-acquired multiplex immunohistochemistry images","volume":"15","author":"Fassler","year":"2020","journal-title":"Diagn. Pathol."},{"key":"ref_24","doi-asserted-by":"crossref","unstructured":"Elazab, N., Soliman, H., El-Sappagh, S., Islam, S., and Elmogy, M. (2020). Objective Diagnosis for Histopathological Images Based on Machine Learning Techniques: Classical Approaches and New Trends. Mathematics, 8.","DOI":"10.3390\/math8111863"},{"key":"ref_25","first-page":"47","article-title":"Diagnose Like A Pathologist: Weakly-Supervised Pathologist-Tree Network for Slide-Level Immunohistochemical Scoring","volume":"35","author":"Chen","year":"2021","journal-title":"Proc. Conf. AAAI Artif. Intell."},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"1124","DOI":"10.1038\/s41374-020-0429-0","article-title":"Brightfield multiplex immunohistochemistry with multispectral imaging","volume":"100","author":"Morrison","year":"2020","journal-title":"Lab. Investig."},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"116471","DOI":"10.1016\/j.eswa.2021.116471","article-title":"Deep learning-based instance segmentation for the precise automated quantification of digital breast cancer immunohistochemistry images","volume":"193","year":"2022","journal-title":"Expert Syst. Appl."},{"key":"ref_28","doi-asserted-by":"crossref","unstructured":"Berezsky, O., Pitsun, O., Derysh, B., Pazdriy, I., Melnyk, G., and Batko, Y. (2021, January 22\u201325). Automatic Segmentation of Immunohistochemical Images Based on U-net Architecture. Proceedings of the 2021 IEEE 16th International Conference on Computer Sciences and Information Technologies (CSIT), IEEE, Lviv, Ukraine.","DOI":"10.1109\/CSIT52700.2021.9648669"},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"106493","DOI":"10.1016\/j.cmpb.2021.106493","article-title":"Improved U-Net based on contour prediction for efficient segmentation of rectal cancer","volume":"213","author":"Li","year":"2021","journal-title":"Comput. Methods Programs Biomed."},{"key":"ref_30","doi-asserted-by":"crossref","unstructured":"Berezsky, O., Pitsun, O., Derish, B., Berezska, K., Melnyk, G., and Batko, Y. (2020, January 16\u201318). Adaptive Immunohistochemical Image Pre-processing Method. Proceedings of the 2020 10th International Conference on Advanced Computer Information Technologies (ACIT), IEEE, Deggendorf, Germany.","DOI":"10.1109\/ACIT49673.2020.9208920"},{"key":"ref_31","first-page":"1","article-title":"Image Superresolution via Divergence Matrix and Automatic Detection of Crossover","volume":"8","author":"Peleshko","year":"2016","journal-title":"Int. J. Intell. Syst. Appl."},{"key":"ref_32","doi-asserted-by":"crossref","unstructured":"Berezsky, O., Pitsun, O., Dubchak, L., Berezka, K., Dolynyuk, T., and Derish, B. (, January 23\u201326). Cytological Images Clustering of Breast Pathologies. Proceedings of the 2020 IEEE 15th International Conference on Computer Sciences and Information Technologies (CSIT), IEEE, Zbarazh, Ukraine.","DOI":"10.1109\/CSIT49958.2020.9321867"},{"key":"ref_33","first-page":"69","article-title":"Fuzzy System For Breast Disease Diagnosing Based On Image Analysis","volume":"2488","author":"Berezsky","year":"2019","journal-title":"Inform. Data-Driven Med."},{"key":"ref_34","first-page":"10","article-title":"Video Shots\u2018 Matching via Various Length of Multidimensional Time Sequences","volume":"9","author":"Hu","year":"2017","journal-title":"Int. J. Intell. Syst. Appl."},{"key":"ref_35","doi-asserted-by":"crossref","unstructured":"Berezsky, O., Verbovyy, S., and Datsko, T. (2015, January 7\u20139). The intelligent system for diagnosing breast cancers based on image analysis. Proceedings of the 2015 Information Technologies in Innovation Business Conference (ITIB), IEEE, Kharkiv, Ukraine.","DOI":"10.1109\/ITIB.2015.7355067"},{"key":"ref_36","doi-asserted-by":"crossref","unstructured":"Hore, A., and Ziou, D. (2010, January 23\u201326). Image Quality Metrics: PSNR vs. SSIM. Proceedings of the 2010 20th International Conference on Pattern Recognition, IEEE, Istanbul, Turkey.","DOI":"10.1109\/ICPR.2010.579"},{"key":"ref_37","doi-asserted-by":"crossref","unstructured":"Vasuki, P., Kanimozhi, J., and Devi, M. (2017, January 27\u201328). A survey on image preprocessing techniques for diverse fields of medical imagery. Proceedings of the 2017 IEEE International Conference on Electrical, Instrumentation and Communication Engineering (ICEICE), IEEE, Karur, India.","DOI":"10.1109\/ICEICE.2017.8192443"},{"key":"ref_38","first-page":"75","article-title":"Breast cancer immunohistological imaging database","volume":"1","author":"Berezsky","year":"2022","journal-title":"Comput. Syst. Inf. Technol."},{"key":"ref_39","first-page":"29","article-title":"A Multidimensional Extended Neo-Fuzzy Neuron for Facial Expression Recognition","volume":"9","author":"Hu","year":"2017","journal-title":"Int. J. Intell. Syst. Appl."}],"container-title":["Journal of Imaging"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2313-433X\/9\/1\/12\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T17:58:54Z","timestamp":1760119134000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2313-433X\/9\/1\/12"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,1,4]]},"references-count":39,"journal-issue":{"issue":"1","published-online":{"date-parts":[[2023,1]]}},"alternative-id":["jimaging9010012"],"URL":"https:\/\/doi.org\/10.3390\/jimaging9010012","relation":{},"ISSN":["2313-433X"],"issn-type":[{"value":"2313-433X","type":"electronic"}],"subject":[],"published":{"date-parts":[[2023,1,4]]}}}