NDLI: Machine learning approach to segment Saccharomyces cerevisiae yeast cells

Content Provider	IEEE Xplore Digital Library
Author	Tleis, M. Verbeek, F.
Copyright Year	2015
Description	Author affiliation: LIACS, Leiden Univ., Leiden, Netherlands (Tleis, M.; Verbeek, F.)
Abstract	In biological studies, Saccharomyces cerevisiae yeast cells are used to study the behaviour of proteins. This is a time consuming and not completely objective process. Hence, image analysis platforms are developed to address these problems and to offer analysis per cell as well. The segmentation algorithms implemented in such platforms can segment the healthy cells, along with artefacts such as debris and dead cells that exist in the cultured medium. The novel idea in this work is to apply a machine learning approach to train the segmentation system in order to classify the healthy cell objects from the other objects. Such approach is based on the analysis of a set of relevant individual cell features extracted from the microscope images of yeast cells. These features include texture measurements and wavelet-based texture measurements, as well as moment invariant features. Those features were introduced to describe the intensity and morphology characteristics in a more sophisticated way. A set of classification systems, data sampling techniques, data normalization schemes and feature selection algorithms were tested and evaluated to build a classification model in order to be used within the segmentation module. The study picks the simple logistic classification model as the best approach to classify our dataset of 1380 cells. This system increases the performance level in our image and data analysis modules, improve the segmentation and consequently the analysis of the measurement results. This leads to a better pattern recognition system as well.
Starting Page	278
Ending Page	281
File Size	75171
Page Count	4
File Format	PDF
ISSN	23775696
e-ISBN	9781467365161
DOI	10.1109/ICABME.2015.7323306
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2015-09-16
Publisher Place	Lebanon
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Image segmentation Accuracy Machine learning algorithms Vegetation Biomedical measurement Logistics
Content Type	Text
Resource Type	Article

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1	Ministry of Education (GoI), Department of Higher Education	Sanctioning Authority	https://www.education.gov.in/ict-initiatives
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4	Project PI / Joint PI	Principal Investigator and Joint Principal Investigators of the project	Dr. B. Sutradhar bsutra@ndl.gov.in Prof. Saswat Chakrabarti will be added soon
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