dc.contributor.advisor | Demir, Çiğdem Gündüz | |
dc.contributor.author | Arslan, Salim | |
dc.date.accessioned | 2016-01-08T18:24:37Z | |
dc.date.available | 2016-01-08T18:24:37Z | |
dc.date.issued | 2012 | |
dc.identifier.uri | http://hdl.handle.net/11693/15785 | |
dc.description | Ankara : The Department of Computer Engineering and the Graduate School of Engineering and Science of Bilkent University, 2012. | en_US |
dc.description | Thesis (Master's) -- Bilkent University, 2012. | en_US |
dc.description | Includes bibliographical refences. | en_US |
dc.description.abstract | High content screening aims to analyze complex biological systems and collect
quantitative data via automated microscopy imaging to improve the quality of
molecular cellular biology research in means of speed and accuracy. More rapid
and accurate high-throughput screening becomes possible with advances in automated
microscopy image analysis, for which cell segmentation commonly constitutes
the core step. Since the performance of cell segmentation directly a ects
the output of the system, it is of great importance to develop e ective segmentation
algorithms. Although there exist several promising methods for segmenting
monolayer isolated and less con
uent cells, it still remains an open problem to
segment more con
uent cells that grow in aggregates on layers.
In order to address this problem, we propose a new marker-controlled watershed
algorithm that incorporates human perception into segmentation. This
incorporation is in the form of how a human locates a cell by identifying its correct
boundaries and piecing these boundaries together to form the cell. For this
purpose, our proposed watershed algorithm de nes four di erent types of primitives
to represent di erent types of boundaries (left, right, top, and bottom)
and constructs an attributed relational graph on these primitives to represent
their spatial relations. Then, it reduces the marker identi cation problem to
the problem of nding prede ned structural patterns in the constructed graph.
Moreover, it makes use of the boundary primitives to guide the
ooding process
in the watershed algorithm. Working with
uorescence microscopy images, our
experiments demonstrate that the proposed algorithm results in locating better
markers and obtaining better cell boundaries for both less and more con
uent
cells, compared to previous cell segmentation algorithms. | en_US |
dc.description.statementofresponsibility | Arslan, Salim | en_US |
dc.format.extent | xvii, 63 leaves, illustrations | en_US |
dc.language.iso | English | en_US |
dc.rights | info:eu-repo/semantics/openAccess | en_US |
dc.subject | Cell segmentation | en_US |
dc.subject | Uorescence microscopy imaging | en_US |
dc.subject | Markercontrolled watershed | en_US |
dc.subject | Watershed | en_US |
dc.subject | Attributed relational graphs | en_US |
dc.subject.lcc | QH212.F55 A76 2012 | en_US |
dc.subject.lcsh | Fluorescence microscopy. | en_US |
dc.subject.lcsh | Image processing. | en_US |
dc.subject.lcsh | Diagnostic image. | en_US |
dc.subject.lcsh | Imaging systems in biology. | en_US |
dc.title | Perceptual watersheds for cell segmentation in fluorescence microscopy images | en_US |
dc.type | Thesis | en_US |
dc.department | Department of Computer Engineering | en_US |
dc.publisher | Bilkent University | en_US |
dc.description.degree | M.S. | en_US |
dc.identifier.itemid | B133700 | |