Two-tier tissue decomposition for histopathological image representation and classification

Simple item page
buir.advisorDemir, Çiğdem Gündüz
dc.contributor.authorGültekin, Tunç
dc.date.accessioned2016-07-01T11:12:11Z
dc.date.available2016-07-01T11:12:11Z
dc.date.issued2014
dc.departmentDepartment of Computer Engineeringen_US
dc.descriptionCataloged from PDF version of article.en_US
dc.description.abstractIn digital pathology, devising effective image representations is crucial to design robust automated diagnosis systems. To this end, many studies have proposed to develop object-based representations, instead of directly using image pixels, since a histopathological image may contain a considerable amount of noise typically at the pixel-level. These previous studies mostly define their objects, based on the color information, as to approximately represent histological tissue components in an image and then use the spatial distribution of these objects for image representation and classification. Thus, object definition has a direct effect on the way of representing the image, which in turn affects classification accuracies. In this thesis, we present a new model for effective representation and classification of histopathological images. The contributions of this model are twofold. First, it introduces a new two-tier tissue decomposition method for defining a set of multityped objects in an image. Different than the previous studies, these objects are defined combining the texture, shape, and size information and they may correspond to individual histological components as well as tissue sub-regions of different characteristics. As its second contribution, it defines a new metric, which we call “dominant blob scale”, to characterize the shape and size of an object with a single scalar value. Our experiments on colon tissue images reveal that this new object definition and characterization provides distinguishing representation of normal and cancerous histopathological images, which is effective to obtain more accurate classification results compared to its counterparts.en_US
dc.description.degreeM.S.en_US
dc.description.provenanceMade available in DSpace on 2016-07-01T11:12:11Z (GMT). No. of bitstreams: 1 0008011.pdf: 14190861 bytes, checksum: b123f246d75263eb2f76d39ac81b67a3 (MD5) Previous issue date: 2014en
dc.description.statementofresponsibilityGültekin, Tunçen_US
dc.format.extentxi, 57 leaves, illustrations, graphics, tablesen_US
dc.identifier.itemidB138059
dc.identifier.urihttp://hdl.handle.net/11693/30093
dc.language.isoEnglishen_US
dc.publisherBilkent Universityen_US
dc.rightsinfo:eu-repo/semantics/openAccessen_US
dc.subjectHistopathological image representationen_US
dc.subjectdigital pathologyen_US
dc.subjectautomated cancer diagnosisen_US
dc.subjecttissue decomposition modelen_US
dc.subjectbloben_US
dc.subject.lccQZ33 .G85 2014en_US
dc.subject.lcshPathology, comparative.en_US
dc.titleTwo-tier tissue decomposition for histopathological image representation and classificationen_US
dc.typeThesisen_US

Files

Original bundle
Now showing 1 - 1 of 1
Thumbnail Image
Name:
0008011.pdf
Size:
13.53 MB
Format:
Adobe Portable Document Format
Description:
Full printable version
Download

Collections

Dept. of Computer Engineering - Master's degree