NDLI: Unsupervised segmentation of live cell images using Gaussian modeling

Content Provider	IEEE Xplore Digital Library
Author	Arslan, S. Durmaz, I. Cetin-Atalay, R. Gunduz-Demir, C.
Copyright Year	2011
Description	Author affiliation: Moleküler Biyoloji ve Genetik Bölümü, Bilkent Üniversitesi, Turkey (Durmaz, I.; Cetin-Atalay, R.) \|\| Bilgisayar Mühendisliİi Bölümü, Bilkent Üniversitesi, Turkey (Arslan, S.; Gunduz-Demir, C.)
Abstract	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.
Starting Page	347
Ending Page	350
File Size	9581586
Page Count	4
File Format	PDF
ISBN	9781457704628
e-ISBN	9781457704635
e-ISBN	9781457704611
DOI	10.1109/SIU.2011.5929658
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2011-04-20
Publisher Place	Turkey
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Drugs Image segmentation Image recognition Conferences Signal processing Pattern recognition Biomedical imaging
Content Type	Text
Resource Type	Article

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