Unsupervised segmentation of live cell images using gaussian modeling [Gauss tabanli modelleme kullanarak canli hücre görüntü leri̇ni̇n ögreti̇ci̇si̇z bölütlenmesi̇]
2011 IEEE 19th Signal Processing and Communications Applications Conference, SIU 2011
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Please cite this item using this persistent URLhttp://hdl.handle.net/11693/28398
The 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.
- Conference Paper 2294