{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,6]],"date-time":"2026-10-06T15:02:58Z","timestamp":1791298978061,"version":"4.1.0"},"reference-count":48,"publisher":"Wiley","issue":"3","license":[{"start":{"date-parts":[[2013,12,26]],"date-time":"2013-12-26T00:00:00Z","timestamp":1388016000000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/onlinelibrary.wiley.com\/termsAndConditions#vor"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Cytometry Pt A"],"published-print":{"date-parts":[[2014,3]]},"abstract":"<jats:title>Abstract<\/jats:title><jats:p>We present two novel automated image analysis methods to differentiate centroblast (CB) cells from noncentroblast (non\u2010CB) cells in digital images of H&amp;E\u2010stained tissues of follicular lymphoma. CB cells are often confused by similar looking cells within the tissue, therefore a system to help their classification is necessary. Our methods extract the discriminatory features of cells by approximating the intrinsic dimensionality from the subspace spanned by CB and non\u2010CB cells. In the first method, discriminatory features are approximated with the help of singular value decomposition (SVD), whereas in the second method they are extracted using Laplacian Eigenmaps. Five hundred high\u2010power field images were extracted from 17 slides, which are then used to compose a database of 213 CB and 234 non\u2010CB region of interest images. The recall, precision, and overall accuracy rates of the developed methods were measured and compared with existing classification methods. Moreover, the reproducibility of both classification methods was also examined. The average values of the overall accuracy were 99.22%\u2009\u00b1\u20090.75% and 99.07%\u2009\u00b1\u20091.53% for COB and CLEM, respectively. The experimental results demonstrate that both proposed methods provide better classification accuracy of CB\/non\u2010CB in comparison with the state of the art methods. \u00a9 2013 International Society for Advancement of Cytometry<\/jats:p>","DOI":"10.1002\/cyto.a.22432","type":"journal-article","created":{"date-parts":[[2013,12,26]],"date-time":"2013-12-26T18:04:44Z","timestamp":1388081084000},"page":"242-255","source":"Crossref","is-referenced-by-count":26,"title":["Histopathological image analysis for centroblasts classification through dimensionality reduction approaches"],"prefix":"10.1002","volume":"85","author":[{"given":"Evgenios N.","family":"Kornaropoulos","sequence":"first","affiliation":[{"name":"Informatics and Telematics Institute\u2014Centre for Research and Technology Hellas (ITI\u2014CERTH) Thessaloniki Greece"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"M. Khalid Khan","family":"Niazi","sequence":"additional","affiliation":[{"name":"Department of Biomedical Informatics Ohio State University Columbus Ohio"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Gerard","family":"Lozanski","sequence":"additional","affiliation":[{"name":"Department of Pathology Ohio State University Columbus Ohio"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Metin N.","family":"Gurcan","sequence":"additional","affiliation":[{"name":"Department of Biomedical Informatics Ohio State University Columbus Ohio"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"311","published-online":{"date-parts":[[2013,12,26]]},"reference":[{"key":"e_1_2_8_2_1","first-page":"439","volume-title":"WHO Classification of Tumours of Haemaopoietic and Lymphoid Tissues","author":"Swerdlow SH","year":"2008"},{"key":"e_1_2_8_3_1","doi-asserted-by":"publisher","DOI":"10.1002\/hon.2900010209"},{"key":"e_1_2_8_4_1","first-page":"490","article-title":"World Health Organization classification of tumours: Pathology and genetics of tumours of haematopoietic and lymphoid tissues","volume":"3","author":"Jaffe ES","year":"2001","journal-title":"Ann Oncol"},{"key":"e_1_2_8_5_1","doi-asserted-by":"publisher","DOI":"10.1200\/JCO.1985.3.1.25"},{"key":"e_1_2_8_6_1","first-page":"1137","article-title":"Use of the working formulation for non\u2010Hodgkin's lymphoma in epidemiological studies: Agreement between reported diagnoses and a panel of experienced pathologists","volume":"78","author":"Dick F","year":"1987","journal-title":"J Natl Cancer Inst"},{"key":"e_1_2_8_7_1","doi-asserted-by":"publisher","DOI":"10.1056\/NEJMoa066099"},{"key":"e_1_2_8_8_1","doi-asserted-by":"publisher","DOI":"10.1097\/01.rti.0000203638.28511.9b"},{"key":"e_1_2_8_9_1","doi-asserted-by":"publisher","DOI":"10.1118\/1.1515762"},{"key":"e_1_2_8_10_1","doi-asserted-by":"publisher","DOI":"10.1007\/s10278-007-9020-0"},{"key":"e_1_2_8_11_1","doi-asserted-by":"publisher","DOI":"10.4103\/2153-3539.74940"},{"key":"e_1_2_8_12_1","doi-asserted-by":"publisher","DOI":"10.1109\/TMI.2007.898536"},{"key":"e_1_2_8_13_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.patcog.2008.10.035"},{"key":"e_1_2_8_14_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.patcog.2008.08.027"},{"key":"e_1_2_8_15_1","doi-asserted-by":"publisher","DOI":"10.1109\/TBME.2010.2041232"},{"key":"e_1_2_8_16_1","doi-asserted-by":"publisher","DOI":"10.1109\/RBME.2009.2034865"},{"key":"e_1_2_8_17_1","doi-asserted-by":"publisher","DOI":"10.1007\/s11265-008-0201-y"},{"key":"e_1_2_8_18_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.cmpb.2009.04.012"},{"key":"e_1_2_8_19_1","doi-asserted-by":"publisher","DOI":"10.1117\/12.770936"},{"key":"e_1_2_8_20_1","first-page":"6969","article-title":"Imaging mass spectrometry analysis for follicular lymphoma grading","volume":"2009","author":"Samsi SS","year":"2009","journal-title":"Proc IEEE Eng Med Biol Soc"},{"key":"e_1_2_8_21_1","first-page":"3685","article-title":"Extraction of color features in the spectral domain to recognize centroblasts in histopathology","volume":"2009","author":"Belkacem\u2010Boussaid K","year":"2009","journal-title":"Proc IEEE Eng Med Biol Soc"},{"key":"e_1_2_8_22_1","doi-asserted-by":"publisher","DOI":"10.1109\/TBME.2010.2055058"},{"key":"e_1_2_8_23_1","doi-asserted-by":"publisher","DOI":"10.1117\/12.911360"},{"key":"e_1_2_8_24_1","doi-asserted-by":"publisher","DOI":"10.1109\/TBME.2010.2058111"},{"key":"e_1_2_8_25_1","first-page":"254","article-title":"Computer\u2010aided classification of centroblast cells in follicular lymphoma","volume":"32","author":"Belkacem\u2010Boussaid K","year":"2010","journal-title":"Anal Quant Cytol Histol"},{"key":"e_1_2_8_26_1","first-page":"154","volume-title":"Best Practice in Lymphoma Diagnosis and Reporting","author":"Parker A","year":"2012"},{"key":"e_1_2_8_27_1","doi-asserted-by":"publisher","DOI":"10.1137\/1.9780898718867"},{"key":"e_1_2_8_28_1","doi-asserted-by":"publisher","DOI":"10.1126\/science.290.5500.2268"},{"key":"e_1_2_8_29_1","doi-asserted-by":"publisher","DOI":"10.1109\/TPAMI.2007.70823"},{"key":"e_1_2_8_30_1","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-642-23678-5_62"},{"key":"e_1_2_8_31_1","first-page":"585","article-title":"Laplacian eigenmaps and spectral techniques for embedding and clustering","volume":"14","author":"Belkin M","year":"2001","journal-title":"NIPS"},{"key":"e_1_2_8_32_1","first-page":"474","article-title":"Using mutual information and cross correlation as metrics for registration of images","volume":"4","author":"Roshni VS","year":"2008","journal-title":"J Theor Appl Inform Technol"},{"key":"e_1_2_8_33_1","doi-asserted-by":"crossref","unstructured":"InabaM ImaiH KatohN.Experimental results of a randomized clustering algorithm. 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