A new method for segmentation of microscopic images on activated sludge

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dc.contributor.authorBoztoprak, Halime
dc.contributor.authorOzbay, Yuksel
dc.date.accessioned2020-03-26T19:00:28Z
dc.date.available2020-03-26T19:00:28Z
dc.date.issued2015
dc.departmentSelçuk Üniversitesien_US
dc.description.abstractActivated sludge samples were taken from the Konya Wastewater Treatment Plant. Two hundred images for each sample were captured by a systematic examination of the slides. Segmentation of microscopic images is a challenging process due to lack of focus. Therefore, adjustment of the focus is required for every movement of the mobile stage. Because the mobile stage does not have the z axis, the focus cannot be adjusted. A new method that uses automatic segmentation of the captured images is developed for solving this problem. The proposed method is not dependent on image content, has minimal computation complexity, and is robust to noise. This method uses a cellular neural network (CNN) in which an adaptive iterative value is calculated by wavelet transform and spatial frequency. A model is fixed in the system in order to estimate the iterative value of the CNN. Integrated automatic image capture and automatic analysis of large numbers of images by using evaluation software are improved in our system. Approximately 1000 microscopic images are processed in this experiment. The proposed method is compared with the traditional threshold method and the CNN through constant iteration. The experimental results are given.en_US
dc.description.sponsorshipCoordinatorship of Selcuk University's Scientific Research ProjectsSelcuk University [11201043]en_US
dc.description.sponsorshipThis work was supported by the Coordinatorship of Selcuk University's Scientific Research Projects under Project No. 11201043.en_US
dc.identifier.doi10.3906/elk-1307-9en_US
dc.identifier.endpage2266en_US
dc.identifier.issn1300-0632en_US
dc.identifier.issn1303-6203en_US
dc.identifier.scopusqualityQ3en_US
dc.identifier.startpage2253en_US
dc.identifier.urihttps://dx.doi.org/10.3906/elk-1307-9
dc.identifier.urihttps://hdl.handle.net/20.500.12395/31777
dc.identifier.volume23en_US
dc.identifier.wosWOS:000368649000018en_US
dc.identifier.wosqualityQ4en_US
dc.indekslendigikaynakWeb of Scienceen_US
dc.indekslendigikaynakScopusen_US
dc.language.isoenen_US
dc.publisherTUBITAK SCIENTIFIC & TECHNICAL RESEARCH COUNCIL TURKEYen_US
dc.relation.ispartofTURKISH JOURNAL OF ELECTRICAL ENGINEERING AND COMPUTER SCIENCESen_US
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/openAccessen_US
dc.selcuk20240510_oaigen_US
dc.subjectAutomatic image captureen_US
dc.subjectwastewater treatmenten_US
dc.subjectsegmentationen_US
dc.subjectactivated sludgeen_US
dc.subjectcellular neural networken_US
dc.subjectwavelet transformen_US
dc.subjectspatial frequencyen_US
dc.subjectentropyen_US
dc.titleA new method for segmentation of microscopic images on activated sludgeen_US
dc.typeArticleen_US

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