{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,28]],"date-time":"2026-02-28T13:14:43Z","timestamp":1772284483934,"version":"3.50.1"},"reference-count":15,"publisher":"IOP Publishing","issue":"7","license":[{"start":{"date-parts":[[2019,8,1]],"date-time":"2019-08-01T00:00:00Z","timestamp":1564617600000},"content-version":"vor","delay-in-days":31,"URL":"https:\/\/publishingsupport.iopscience.iop.org\/iop-standard\/v1"},{"start":{"date-parts":[[2019,8,1]],"date-time":"2019-08-01T00:00:00Z","timestamp":1564617600000},"content-version":"tdm","delay-in-days":31,"URL":"https:\/\/iopscience.iop.org\/info\/page\/text-and-data-mining"}],"content-domain":{"domain":["iopscience.iop.org"],"crossmark-restriction":false},"short-container-title":["Physiol. Meas."],"published-print":{"date-parts":[[2019,7,1]]},"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:p>\n                    <jats:italic>Objective<\/jats:italic>\n                    : We have upgraded our own original color filtration pixel-by-pixel (CFPP) method (Klonowski\n                    <jats:italic>et al<\/jats:italic>\n                    2018a\n                    <jats:italic>Physiol. Meas<\/jats:italic>\n                    .\n                    <jats:bold>39<\/jats:bold>\n                    034002) to enable not only automatic and rapid assessment of the proliferation index of a tumor or neoplasm but also quick automatic location of hot-spots (regions of interest, ROIs) in immunohistochemically stained microscopic images of neoplasms and tumors.\n                    <jats:italic>Approach<\/jats:italic>\n                    : Neoplastic cells stain differently from normal cells. By counting in a given window the number of pixels belonging to the given subspaces of (R,G,B) color space which correspond, respectively, to proliferating cells (which are mostly neoplastic) and non-proliferating cells (which are mostly normal) we calculate the local proliferation index in this window. The window is moved all around the whole histopathological virtual slide (WSI) or around a chosen part of the WSI. By adding the respective numbers calculated for all the windows covering the WSI or the chosen part of it one can easily calculate the global proliferation index.\n                    <jats:italic>Main results<\/jats:italic>\n                    : The method is rapid and does not require the time-consuming step of selecting ROIs manually nor does it need computationally complicated detection of hot-spots, both of which attempt to emulate a pathologist\u2019s way of thinking. We apply our method to a set of slide images of diffuse large B-cell lymphoma.\n                    <jats:italic>Significance<\/jats:italic>\n                    : By appropriate changes in the (R,G,B) color filtration thresholds, our method may be adapted to the analysis of other types of tumors. It may also be adapted for analysis of microscopic images in neuropathology. Because of its rapidity and simplicity it may also used for analysis of series of images to assess local dynamics of image complexity in network physiology applications.\n                  <\/jats:p>","DOI":"10.1088\/1361-6579\/ab267e","type":"journal-article","created":{"date-parts":[[2019,6,3]],"date-time":"2019-06-03T18:30:18Z","timestamp":1559586618000},"page":"075010","update-policy":"https:\/\/doi.org\/10.1088\/crossmark-policy","source":"Crossref","is-referenced-by-count":1,"title":["Computer analysis of histopathological images for tumor grading. 2"],"prefix":"10.1088","volume":"40","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-2634-4744","authenticated-orcid":false,"given":"Wlodzimierz","family":"Klonowski","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Anna","family":"Korzynska","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Aneta","family":"Chwala","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"266","published-online":{"date-parts":[[2019,8,1]]},"reference":[{"key":"pmeaab267ebib001","doi-asserted-by":"publisher","first-page":"229","DOI":"10.4213\/rm5","type":"journal-article","article-title":"On teaching mathematics","volume":"53","author":"Arnold","year":"1998","journal-title":"Usp. Mat. Nauk"},{"key":"pmeaab267ebib002","doi-asserted-by":"publisher","first-page":"e0142143","DOI":"10.1371\/journal.pone.0142143","type":"journal-article","article-title":"Network physiology: how organ systems dynamically interact","volume":"10","author":"Bartsch","year":"2015","journal-title":"PLoS One"},{"key":"pmeaab267ebib003","doi-asserted-by":"publisher","first-page":"702","DOI":"10.1038\/ncomms1705","type":"journal-article","article-title":"Network physiology reveals relations between network topology and physiologic function","volume":"3","author":"Bashan","year":"2012","journal-title":"Nat. Commun."},{"key":"pmeaab267ebib004","doi-asserted-by":"publisher","first-page":"30","DOI":"10.1016\/j.bbe.2018.09.004","type":"journal-article","article-title":"Automatic method for assessment of proliferation index in digital images of DLBCL tissue section","volume":"39","author":"Gomolka","year":"2019","journal-title":"Biocybern. Biomed. Eng."},{"key":"pmeaab267ebib005","doi-asserted-by":"publisher","first-page":"1896","DOI":"10.1002\/jcp.24662","type":"journal-article","article-title":"Big data bioinformatics","volume":"229","author":"Greene","year":"2014","journal-title":"J. Cell. Physiol."},{"key":"pmeaab267ebib006","doi-asserted-by":"publisher","first-page":"147","DOI":"10.1109\/RBME.2009.2034865","type":"journal-article","article-title":"Histopathological image analysis: a review","volume":"2","author":"Gurcan","year":"2009","journal-title":"IEEE Rev. Biomed. Eng."},{"key":"pmeaab267ebib007","doi-asserted-by":"publisher","DOI":"10.1088\/1367-2630\/18\/10\/100201","type":"journal-article","article-title":"Focus on the emerging new fields of network physiology and network medicine","volume":"18","author":"Ivanov","year":"2016","journal-title":"New J. Phys."},{"key":"pmeaab267ebib008","doi-asserted-by":"publisher","first-page":"681","DOI":"10.1201\/b20232","type":"book","article-title":"Applications of chaos theory methods in clinical digital pathology","author":"Klonowski","year":"2016"},{"key":"pmeaab267ebib009","doi-asserted-by":"publisher","DOI":"10.1088\/1361-6579\/aaa82c","type":"journal-article","article-title":"Computer analysis of histopathological images for tumor grading","volume":"39","author":"Klonowski","year":"2018a","journal-title":"Physiol. Meas."},{"key":"pmeaab267ebib010","doi-asserted-by":"publisher","first-page":"1850021","DOI":"10.1142\/S0218348X18500214","type":"journal-article","article-title":"Analysis of anal intraepithelial neoplasia images using 1D and 2D Higuchi\u2019s fractal dimension methods","volume":"23","author":"Klonowski","year":"2018b","journal-title":"Fractals"},{"key":"pmeaab267ebib011","doi-asserted-by":"publisher","DOI":"10.1088\/1742-6596\/640\/1\/012013","type":"other","article-title":"Major component analysis of dynamic networks of physiologic organ interactions","volume":"640","author":"Liu","year":"2015"},{"key":"pmeaab267ebib012","doi-asserted-by":"publisher","first-page":"597","DOI":"10.1016\/j.bbe.2016.06.006","type":"journal-article","article-title":"MIAP\u2014web-based platform for the computer analysis of microscopic images to support the pathological diagnosis","volume":"36","author":"Markiewicz","year":"2016","journal-title":"Biocybern. Biomed. Eng."},{"key":"pmeaab267ebib013","article-title":"Bioinformatics, Big Data, and Cancer","author":"National Cancer Institute","year":"2019","type":"web-resource"},{"key":"pmeaab267ebib014","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1155\/2015\/498746","type":"journal-article","article-title":"Comparison of the manual, semiautomatic, and automatic selection and leveling of hot spots in whole slide images for Ki-67 quantification in meningiomas","volume":"2015","author":"Swiderska","year":"2015","journal-title":"Anal. Cell. Pathol."},{"key":"pmeaab267ebib015","doi-asserted-by":"publisher","first-page":"6703","DOI":"10.1158\/0008-5472.CAN-18-0653","type":"journal-article","article-title":"A convolutional neural network uses microscopic images to differentiate between mouse and human cell lines and their radioresistant clones","volume":"78","author":"Toratani","year":"2018","journal-title":"Cancer Res."}],"container-title":["Physiological Measurement"],"original-title":[],"link":[{"URL":"http:\/\/iopscience.iop.org\/article\/10.1088\/1361-6579\/ab267e","content-type":"text\/html","content-version":"am","intended-application":"text-mining"},{"URL":"http:\/\/iopscience.iop.org\/article\/10.1088\/1361-6579\/ab267e\/pdf","content-type":"application\/pdf","content-version":"am","intended-application":"text-mining"},{"URL":"https:\/\/iopscience.iop.org\/article\/10.1088\/1361-6579\/ab267e\/pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/iopscience.iop.org\/article\/10.1088\/1361-6579\/ab267e","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/iopscience.iop.org\/article\/10.1088\/1361-6579\/ab267e","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/iopscience.iop.org\/article\/10.1088\/1361-6579\/ab267e\/pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"http:\/\/iopscience.iop.org\/article\/10.1088\/1361-6579\/ab267e\/pdf","content-type":"application\/pdf","content-version":"am","intended-application":"syndication"},{"URL":"https:\/\/iopscience.iop.org\/article\/10.1088\/1361-6579\/ab267e\/pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"syndication"},{"URL":"http:\/\/iopscience.iop.org\/article\/10.1088\/1361-6579\/ab267e\/pdf","content-type":"application\/pdf","content-version":"am","intended-application":"similarity-checking"},{"URL":"https:\/\/iopscience.iop.org\/article\/10.1088\/1361-6579\/ab267e\/pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"},{"URL":"https:\/\/iopscience.iop.org\/article\/10.1088\/1361-6579\/ab267e","content-type":"text\/html","content-version":"vor","intended-application":"similarity-checking"},{"URL":"https:\/\/iopscience.iop.org\/article\/10.1088\/1361-6579\/ab267e\/pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,2,28]],"date-time":"2026-02-28T12:27:59Z","timestamp":1772281679000},"score":1,"resource":{"primary":{"URL":"https:\/\/iopscience.iop.org\/article\/10.1088\/1361-6579\/ab267e"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2019,7,1]]},"references-count":15,"journal-issue":{"issue":"7","published-online":{"date-parts":[[2019,8,1]]},"published-print":{"date-parts":[[2019,7,1]]}},"URL":"https:\/\/doi.org\/10.1088\/1361-6579\/ab267e","relation":{},"ISSN":["0967-3334","1361-6579"],"issn-type":[{"value":"0967-3334","type":"print"},{"value":"1361-6579","type":"electronic"}],"subject":[],"published":{"date-parts":[[2019,7,1]]},"assertion":[{"value":"Computer analysis of histopathological images for tumor grading. 2","name":"article_title","label":"Article Title"},{"value":"Physiological Measurement","name":"journal_title","label":"Journal Title"},{"value":"paper","name":"article_type","label":"Article Type"},{"value":"\u00a9 2019 Institute of Physics and Engineering in Medicine. All rights, including for text and data mining, AI training, and similar technologies, are reserved.","name":"copyright_information","label":"Copyright Information"},{"value":"2019-02-23","name":"date_received","label":"Date Received","group":{"name":"publication_dates","label":"Publication dates"}},{"value":"2019-06-03","name":"date_accepted","label":"Date Accepted","group":{"name":"publication_dates","label":"Publication dates"}},{"value":"2019-08-01","name":"date_epub","label":"Online publication date","group":{"name":"publication_dates","label":"Publication dates"}}]}}