Show simple item record

dc.contributor.authorÖnder, D.
dc.contributor.authorKaraçali, B.
dc.date.accessioned2021-02-12T18:43:49Z
dc.date.available2021-02-12T18:43:49Z
dc.date.issued2009
dc.identifier.isbn9781424436064
dc.identifier.urihttps://doi.org/10.1109/BIYOMUT.2009.5130342
dc.identifier.urihttps://hdl.handle.net/11147/9759
dc.description2009 14th National Biomedical Engineering Meeting, BIYOMUT 2009en_US
dc.description.abstractThe aim of this work is to perform automated texture classification of histology slides using grayscale images and manifold learning method. Texture feature vectors were obtained using local gray scale co-occurrence matrices and the dimension of the feature vector space was lowered using Isomap dimension reduction. In a lower dimension feature space, k-means clustering operation was performed in order to provide separate texture clusters. In this work, experimental results were obtained using human kidney histology slides. Corresponding feature vectors and determined texture types were given as results. ©2009 IEEE.en_US
dc.language.isoturen_US
dc.relation.isversionof10.1109/BIYOMUT.2009.5130342en_US
dc.rightsinfo:eu-repo/semantics/closedAccessen_US
dc.subjectCo-occurrenceen_US
dc.subjectDimensionality reductionen_US
dc.subjectIsomapen_US
dc.subjectTextureen_US
dc.titleAutomated clustering of histology slide texture using co-occurrence based grayscale image features and manifold learning [Gri seviye birliktelik matrisi öznitelikleri ve manifold ö?renme yardimiyla histoloji görüntülerinde otomatik doku siniflandirilmasi]en_US
dc.typeconferenceObjecten_US
dc.typeconferenceObjecten_US
dc.relation.journalProceedings of 2009 14th National Biomedical Engineering Meeting, BIYOMUT 2009en_US
dc.contributor.departmentIzmir Isntitute of Technologyen_US
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US


Files in this item

FilesSizeFormatView

There are no files associated with this item.

This item appears in the following Collection(s)

Show simple item record