{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,12,14]],"date-time":"2025-12-14T15:56:17Z","timestamp":1765727777353},"reference-count":22,"publisher":"Wiley","issue":"4","license":[{"start":{"date-parts":[[2013,12,11]],"date-time":"2013-12-11T00:00:00Z","timestamp":1386720000000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/onlinelibrary.wiley.com\/termsAndConditions#vor"},{"start":{"date-parts":[[2015,9,1]],"date-time":"2015-09-01T00:00:00Z","timestamp":1441065600000},"content-version":"tdm","delay-in-days":629,"URL":"http:\/\/doi.wiley.com\/10.1002\/tdm_license_1.1"}],"content-domain":{"domain":["wiley.com"],"crossmark-restriction":true},"short-container-title":["Int J Lab Hematology"],"published-print":{"date-parts":[[2014,8]]},"abstract":"<jats:title>Summary<\/jats:title><jats:sec><jats:title>Introduction<\/jats:title><jats:p>There are automated systems for digital peripheral blood (<jats:styled-content style=\"fixed-case\">PB<\/jats:styled-content>) cell analysis, but they operate most effectively in nonpathological blood samples. The objective of this work was to design a methodology to improve the automatic classification of abnormal lymphoid cells.<\/jats:p><\/jats:sec><jats:sec><jats:title>Methods<\/jats:title><jats:p>We analyzed 340 digital images of individual lymphoid cells from <jats:styled-content style=\"fixed-case\">PB<\/jats:styled-content> films obtained in the CellaVision <jats:styled-content style=\"fixed-case\">DM<\/jats:styled-content>96:150 chronic lymphocytic leukemia (<jats:styled-content style=\"fixed-case\">CLL<\/jats:styled-content>) cells, 100 hairy cell leukemia (<jats:styled-content style=\"fixed-case\">HCL<\/jats:styled-content>) cells, and 90 normal lymphocytes (N). We implemented the <jats:italic>Watershed Transformation<\/jats:italic> to segment the nucleus, the cytoplasm, and the peripheral cell region. We extracted 44 features and then the clustering Fuzzy C\u2010Means (<jats:styled-content style=\"fixed-case\">FCM<\/jats:styled-content>) was applied in two steps for the lymphocyte classification.<\/jats:p><\/jats:sec><jats:sec><jats:title>Results<\/jats:title><jats:p>The images were automatically clustered in three groups, one of them with 98% of the <jats:styled-content style=\"fixed-case\">HCL<\/jats:styled-content> cells. The set of the remaining cells was clustered again using <jats:styled-content style=\"fixed-case\">FCM<\/jats:styled-content> and texture features. The two new groups contained 83.3% of the N cells and 71.3% of the <jats:styled-content style=\"fixed-case\">CLL<\/jats:styled-content> cells, respectively.<\/jats:p><\/jats:sec><jats:sec><jats:title>Conclusion<\/jats:title><jats:p>The approach has been able to automatically classify with high precision three types of lymphoid cells. The addition of more descriptors and other classification techniques will allow extending the classification to other classes of atypical lymphoid cells.<\/jats:p><\/jats:sec>","DOI":"10.1111\/ijlh.12175","type":"journal-article","created":{"date-parts":[[2013,12,11]],"date-time":"2013-12-11T10:44:41Z","timestamp":1386758681000},"page":"472-480","update-policy":"http:\/\/dx.doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":24,"title":["Automatic classification of atypical lymphoid B cells using digital blood image processing"],"prefix":"10.1111","volume":"36","author":[{"given":"S.","family":"Alf\u00e9rez","sequence":"first","affiliation":[{"name":"Universitat Polit\u00e8cnica de Catalunya Barcelona Spain"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"A.","family":"Merino","sequence":"additional","affiliation":[{"name":"Servei d'Hemoterapia\u2010Hemostasia Centre de Diagn\u00f3stic Biom\u00e9dic Hospital Cl\u00ednic de Barcelona Barcelona Spain"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"L. 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