Browsing by Author "Arslan, Salim"
Now showing 1 - 2 of 2
Results Per Page
Sort Options
Item Open Access Gauss tabanlı modelleme kullanarak canlı hücre görüntülerinin öğreticisiz bölütlenmesi(2011-04) Arslan, Salim; Durmaz, İrem; Çetin-Atalay, Rengül; Gündüz-Demir, ÇiğdemThe first step of targeted cancer drug development is to screen and determine drug candidates by in vitro measuring the effectiveness of the drugs. The tests developed for this purpose can be time consuming due to their procedures and cannot be conducted in every laboratory due to the required hardwares. On the other hand, an image-based screening test has a potential to be less time consuming since it can directly be carried out on the live cell images and to be more extensively used because of the availability of its required equipments and their relatively less expensive cost. With such an image-based test, it is possible to quantify the cell death by finding cellular regions and comparing it against the control group. In this work, we propose a new method that automatically locates the cellular regions by the unsupervised segmentation of live cell images. This method relies on approximately locating cellular regions and the background with gradient-based thresholding and morphological operators and then finding the final boundaries by modeling the gradient of these regions with Gaussians. Working on the images of different cell lines captured with different magnifications, our experiments show that the proposed method leads to promising results. © 2011 IEEE.Item Open Access Perceptual watersheds for cell segmentation in fluorescence microscopy images(Bilkent University, 2012) Arslan, SalimHigh 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.