Canlı hücre bölütlemesi için gözeticili öğrenme modeli

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dc.citation.epage1974en_US
dc.citation.spage1971en_US
dc.contributor.authorKoyuncu, Can Fahrettinen_US
dc.contributor.authorDurmaz, İremen_US
dc.contributor.authorÇetin-Atalay, Rengülen_US
dc.contributor.authorGündüz-Demir, Çiğdemen_US
dc.coverage.spatialTrabzon, Turkey
dc.date.accessioned2016-02-08T12:03:41Z
dc.date.available2016-02-08T12:03:41Z
dc.date.issued2014-04en_US
dc.departmentDepartment of Computer Engineeringen_US
dc.descriptionDate of Conference: 23-25 April, 2014
dc.descriptionConference name: 22nd Signal Processing and Communications Applications Conference, SIU 2014
dc.description.abstractAutomated cell imaging systems have been proposed for faster and more reliable analysis of biological events at the cellular level. The first step of these systems is usually cell segmentation whose success affects the other system steps. Thus, it is critical to implement robust and efficient segmentation algorithms for the design of successful systems. In the literature, the most commonly used methods for cell segmentation are marker controlled watersheds. These watershed algorithms assume that markers one-to-one correspond to cells and identify their boundaries by growing these markers. Thus, it is very important to correctly define the markers for these algorithms. The markers are usually defined by finding local minima/maxima on intensity or gradient values or by applying morphological operations on the corresponding binary image. In this work, we propose a new marker controlled watershed algorithm for live cell segmentation. The main contributions of this algorithm are twofold. First, different than the approaches in the literature, it implements a new supervised learning model for marker detection. In this model, it has been proposed to extract features for each pixel considering its neighbors' intensities and gradients and to decide whether this pixel is a marker pixel or not by a classifier using these extracted features. Second, it has been proposed to group the neighboring pixels based on the direction information and to extract features according to these groups. The experiments on 1954 cells show that the proposed algorithm leads to higher segmentation results compared to other watersheds. © 2014 IEEE.en_US
dc.identifier.doi10.1109/SIU.2014.6830643en_US
dc.identifier.urihttp://hdl.handle.net/11693/27878
dc.language.isoTurkishen_US
dc.publisherIEEE Computer Societyen_US
dc.relation.isversionofhttp://dx.doi.org/10.1109/SIU.2014.6830643en_US
dc.source.title22nd Signal Processing and Communications Applications Conference, SIU 2014en_US
dc.subjectCell linesen_US
dc.subjectCell segmentationen_US
dc.subjectMarker controlled watershed algorithmsen_US
dc.subjectSupport vector machinesen_US
dc.subjectAlgorithmsen_US
dc.subjectCell cultureen_US
dc.subjectMathematical morphologyen_US
dc.subjectPixelsen_US
dc.subjectSignal processingen_US
dc.subjectSupervised learningen_US
dc.subjectSupport vector machinesen_US
dc.subjectWatershedsen_US
dc.subjectCell linesen_US
dc.subjectCell segmentationen_US
dc.subjectMarker controlled watershed algorithmsen_US
dc.subjectMarker-controlled watershedsen_US
dc.subjectMorphological operationsen_US
dc.subjectSegmentation algorithmsen_US
dc.subjectSegmentation resultsen_US
dc.subjectWater-shed algorithmen_US
dc.subjectImage segmentationen_US
dc.titleCanlı hücre bölütlemesi için gözeticili öğrenme modelien_US
dc.title.alternativeA supervised learning model for live cell segmentationen_US
dc.typeConference Paperen_US

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