{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,10]],"date-time":"2026-02-10T17:06:47Z","timestamp":1770743207170,"version":"3.49.0"},"publisher-location":"Cham","reference-count":12,"publisher":"Springer Nature Switzerland","isbn-type":[{"value":"9783031384295","type":"print"},{"value":"9783031384301","type":"electronic"}],"license":[{"start":{"date-parts":[[2023,9,11]],"date-time":"2023-09-11T00:00:00Z","timestamp":1694390400000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2023,9,11]],"date-time":"2023-09-11T00:00:00Z","timestamp":1694390400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2024]]},"DOI":"10.1007\/978-3-031-38430-1_6","type":"book-chapter","created":{"date-parts":[[2023,9,10]],"date-time":"2023-09-10T19:01:24Z","timestamp":1694372484000},"page":"67-79","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":4,"title":["Using Histogram Skewness and Kurtosis Features for Detection of White Matter Hyperintensities in MRI Images"],"prefix":"10.1007","author":[{"given":"Anna","family":"Baran","sequence":"first","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4773-5322","authenticated-orcid":false,"given":"Adam","family":"Pi\u00f3rkowski","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2023,9,11]]},"reference":[{"key":"6_CR1","doi-asserted-by":"crossref","unstructured":"Balakrishnan, R., Hern\u00e1ndez, M.d.C.V., Farrall, A.J.: Automatic segmentation of white matter hyperintensities from brain magnetic resonance images in the era of deep learning and big data\u2014A systematic review. Comput. Med. Imaging Graph. 88, 101867 (2021)","DOI":"10.1016\/j.compmedimag.2021.101867"},{"key":"6_CR2","doi-asserted-by":"crossref","unstructured":"Doane, D.P., Seward, L.E.: Measuring skewness: a forgotten statistic? J. Stat. Educ. 19(2) (2011)","DOI":"10.1080\/10691898.2011.11889611"},{"key":"6_CR3","doi-asserted-by":"publisher","first-page":"238","DOI":"10.3389\/fneur.2019.00238","volume":"10","author":"BM Frey","year":"2019","unstructured":"Frey, B.M., Petersen, M., Mayer, C., Schulz, M., Cheng, B., Thomalla, G.: Characterization of white matter hyperintensities in large-scale MRI-studies. Front. Neurol. 10, 238 (2019)","journal-title":"Front. Neurol."},{"issue":"1","key":"6_CR4","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1038\/s41598-019-52966-0","volume":"9","author":"R Heinen","year":"2019","unstructured":"Heinen, R., Steenwijk, M.D., Barkhof, F., Biesbroek, J.M., van der Flier, W.M., Kuijf, H.J., Prins, N.D., Vrenken, H., Biessels, G.J., de Bresser, J.: Performance of five automated white matter hyperintensity segmentation methods in a multicenter dataset. Sci. Rep. 9(1), 1\u201312 (2019)","journal-title":"Sci. Rep."},{"issue":"6","key":"6_CR5","doi-asserted-by":"publisher","first-page":"668","DOI":"10.1002\/jmri.10011","volume":"14","author":"CR Jack Jr","year":"2001","unstructured":"Jack, C.R., Jr., O\u2019Brien, P.C., Rettman, D.W., Shiung, M.M., Xu, Y., Muthupillai, R., Manduca, A., Avula, R., Erickson, B.J.: Flair histogram segmentation for measurement of leukoaraiosis volume. J. Magn. Reson. Imaging: Off. J. Int. Soc. Magn. Reson. Med. 14(6), 668\u2013676 (2001)","journal-title":"J. Magn. Reson. Imaging: Off. J. Int. Soc. Magn. Reson. Med."},{"issue":"2","key":"6_CR6","doi-asserted-by":"publisher","first-page":"123","DOI":"10.1007\/s40120-014-0024-7","volume":"3","author":"C Lebrun","year":"2014","unstructured":"Lebrun, C., Cohen, M., Chaussenot, A., Mondot, L., Chanalet, S.: A prospective study of patients with brain MRI showing incidental t2 hyperintensities addressed as multiple sclerosis: a lot of work to do before treating. Neurol. Therapy 3(2), 123\u2013132 (2014)","journal-title":"Neurol. Therapy"},{"key":"6_CR7","doi-asserted-by":"crossref","unstructured":"Milewska, K., Obuchowicz, R., Pi\u00f3rkowski, A.: A preliminary approach to plaque detection in mri brain images. In: Innovations and Developments of Technologies in Medicine, Biology and Healthcare: proceedings of the IEEE EMBS International Student Conference (ISC), pp. 94\u2013105. Springer (2022)","DOI":"10.1007\/978-3-030-88976-0_13"},{"key":"6_CR8","doi-asserted-by":"crossref","unstructured":"Pi\u00f3rkowski, A., Lasek, J.: Evaluation of local thresholding algorithms for segmentation of white matter hyperintensities in magnetic resonance images of the brain. In: Applied Informatics: Fourth International Conference, ICAI 2021, Buenos Aires, Argentina, Proceedings, vol. 4. pp. 331\u2013345. Springer (2021)","DOI":"10.1007\/978-3-030-89654-6_24"},{"key":"6_CR9","unstructured":"Pratt, W.K.: Digital Image Processing. Wiley (1991)"},{"key":"6_CR10","doi-asserted-by":"crossref","unstructured":"Sorysz, J., Sorysz, D.: Efficiency of local binarization methods in segmentation of selected objects in echocardiographic images. In: Intelligent Computing: Proceedings of the 2022 Computing Conference, vol. 3, pp. 179\u2013192. Springer (2022)","DOI":"10.1007\/978-3-031-10467-1_10"},{"issue":"1","key":"6_CR11","doi-asserted-by":"publisher","first-page":"66","DOI":"10.1016\/j.cmpb.2008.08.005","volume":"94","author":"PM Szczypi\u0144ski","year":"2009","unstructured":"Szczypi\u0144ski, P.M., Strzelecki, M., Materka, A., Klepaczko, A.: Mazda\u2014A software package for image texture analysis. Comput. Methods Programs Biomed. 94(1), 66\u201376 (2009)","journal-title":"Comput. Methods Programs Biomed."},{"key":"6_CR12","unstructured":"Zhang, Y., Duan, Y., Wang, X., Zhuo, Z., Haller, S., Barkhof, F., Liu, Y.: A deep learning algorithm for white matter hyperintensity lesion detection and segmentation. Neuroradiology 1\u20138 (2022)"}],"container-title":["Lecture Notes in Networks and Systems","The Latest Developments and Challenges in Biomedical Engineering"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-031-38430-1_6","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,9,10]],"date-time":"2023-09-10T19:01:54Z","timestamp":1694372514000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-031-38430-1_6"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,9,11]]},"ISBN":["9783031384295","9783031384301"],"references-count":12,"URL":"https:\/\/doi.org\/10.1007\/978-3-031-38430-1_6","relation":{},"ISSN":["2367-3370","2367-3389"],"issn-type":[{"value":"2367-3370","type":"print"},{"value":"2367-3389","type":"electronic"}],"subject":[],"published":{"date-parts":[[2023,9,11]]},"assertion":[{"value":"11 September 2023","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"PCBEE","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Polish Conference on Biocybernetics and Biomedical Engineering","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Lodz","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Poland","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2023","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"27 September 2023","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"29 September 2023","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"pcbee2023","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}}]}}