Effective histopathological image area reduction method for real-time applications

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dc.contributor.authorOzturk, Saban
dc.contributor.authorAkdemir, Bayram
dc.date.accessioned2020-03-26T19:53:37Z
dc.date.available2020-03-26T19:53:37Z
dc.date.issued2018
dc.departmentSelçuk Üniversitesien_US
dc.description.abstractHistopathologic images are time consuming for both specialist and machine learning methods with their complex structure and huge dimensions. In these cases, delays in the diagnosis of disease occur, as well as the treatment of fewer patients. When the histopathological images are examined at low resolution for shortening the examination time, it is almost impossible to identify the cancerous regions. When examining high-resolution images, it takes a long time to inspect because the image is divided into patches. Despite the fact that fairly fast machine learning methods are offered for the shortening of the analysis period, the number of patches to be examined has a negative effect on the decision time. For this reason, the area under examination needs to be reduced. For this, first of all, the destruction of cell-free areas and then the destruction of areas containing noncancerous cells must be eliminated. An effective and fast method of area reduction is presented for faster analysis and real-time use of histopathological images by machine learning algorithms. A two-step approach is used in the proposed method. In the first step, 3 x 3 texture properties of images are obtained and discrete wavelet transform is applied. Then, the image is cleaned with simple morphological processes. In the second step, the cleaned image is subjected to a discrete wavelet transform. Thus, the changes in cell-containing regions are captured, and regions that may be dangerous are identified. The proposed method reduced the areas to be examined by 98.5% to 99.5% with 95.33% accuracy. (C) 2018 SPIE and IS&Ten_US
dc.identifier.doi10.1117/1.JEI.27.5.051213en_US
dc.identifier.issn1017-9909en_US
dc.identifier.issn1560-229Xen_US
dc.identifier.issue5en_US
dc.identifier.scopusqualityQ3en_US
dc.identifier.urihttps://dx.doi.org/10.1117/1.JEI.27.5.051213
dc.identifier.urihttps://hdl.handle.net/20.500.12395/36550
dc.identifier.volume27en_US
dc.identifier.wosWOS:000443279700012en_US
dc.identifier.wosqualityQ4en_US
dc.indekslendigikaynakWeb of Scienceen_US
dc.indekslendigikaynakScopusen_US
dc.language.isoenen_US
dc.publisherIS&T & SPIEen_US
dc.relation.ispartofJOURNAL OF ELECTRONIC IMAGINGen_US
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/closedAccessen_US
dc.selcuk20240510_oaigen_US
dc.subjectdimension reductionen_US
dc.subjectdiscrete wavelet transformen_US
dc.subjecthistopathological image classificationen_US
dc.subjecttexture analysisen_US
dc.titleEffective histopathological image area reduction method for real-time applicationsen_US
dc.typeArticleen_US

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