Classification of yeast cells from image features to evaluate pathogen conditions

Putten, Peter van der; Bertens, Laura M.F.; Liu, Jinshuo; Hagen, Ferry; Boekhout, Teun; Verbeek, Fons J.

doi:10.1117/12.714072

Cited by 6 publications

(5 citation statements)

References 20 publications

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“…In our work, we adopted the set of seven moment invariants as proposed by Hu [13] and are widely know as Hu's set of moment invariants. In yeast studies, the first and second moment invariants were the top predictors to classify virulent from non virulent cells [14]. Moreover, It was mentioned in a previous study that the effectiveness of moment invariants will increase when fused with the results of other techniques [12].…”

Section: Moment Invariant Featuresmentioning

confidence: 97%

Machine learning approach to segment Saccharomyces cerevisiae yeast cells

Tleis

Verbeek

2015

2015 International Conference on Advances in Biomedical Engineering (ICABME)

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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.

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Section: Moment Invariant Featuresmentioning

confidence: 97%

Machine learning approach to segment Saccharomyces cerevisiae yeast cells

Tleis

Verbeek

2015

2015 International Conference on Advances in Biomedical Engineering (ICABME)

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“…The intensity profile of an object is derived by applying the binary mask to the original image. A large range of features can be used [25] so that features can be used to discriminate between experimental conditions that are applied [18,19,31].…”

Section: Object Optimizationmentioning

confidence: 99%

“…We have included Fuzzy C-means clustering (FCM) to the tests to illustrate the enhanced performance of our approach. All algorithms have claimed the intrinsic capacity of performing well under noisy conditions typical to HTS imaging [4,5,16,25,28]. For the algorithms, open-source plug-ins implementations available in ImageJ [32] and CellProfiler [1] have been used without modifications.…”

Section: Performance Of the Wmc Algorithmmentioning

confidence: 99%

Segmentation for High-Throughput Image Analysis: Watershed Masked Clustering

Yan

Verbeek

2012

Leveraging Applications of Formal Methods, Verification and Validation. Applications and Case Studies

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High-throughput microscopy imaging applications represent an important research field that is focused on testing and comparing lots of different conditions in living systems. It runs over a limited time-frame and per time step images are generated as output; within the time-range a resilient variation in the images of the experiment is characteristic. Studies represent dynamic circumstances expressed in shape variation of the objects under study. For object extraction, i.e. the segmentation of cells, aforementioned conditions have to be taken into account. Segmentation is used to extract objects from images and from objects features are measured. For high-throughput applications generic segmentation algorithms tend to be suboptimal. Therefore, an algorithm is required that can adapt to a range of variations; i.e. self-adaptation of the segmentation parameters without prior knowledge. In order to prevent measurement bias, the algorithm should be able to assess all inconclusive configurations, e.g. cell clusters. The segmentation method must be accurate and robust so that results that can be trustfully used in further analysis and interpretation. For this study a number of algorithms were evaluated and from the results a new algorithm was developed; the watershed masked clustering algorithm. It consists of three steps: (1) a watershed algorithm is used to establish the coarse location of objects, (2) the threshold is optimized by applying a clustering in each watershed region and (3) each mask is reevaluated on consistency and reoptimized so as to result in consistent segmented objects. The evaluation of our algorithm is realized by testing with images containing artificial objects and real-life microscopy images. The result shows that our algorithm is significantly more accurate, more robust and very reproducible.

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“…[1][2][3][4][5] In a genetic study, phenotype study of yeast with the genetic mutations related to human genetic diseases has been a great help to improve the human disease diagnosis and treatment. [6][7][8][9][10] During the studies, massive images of yeast cells are generally captured with microscopes to observe the different cell structures and behaviors under mutation or different drug treatments. However, in general, the morphological analysis of yeast cells, which mainly depends on manual measurements by researchers, is time-consuming and has personal formula.…”

Section: Introductionmentioning

confidence: 99%

Segmentation of yeast cell’s bright-field image with an edge-tracing algorithm

Wang

Sun

et al. 2018

J. Biomed. Opt.

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Phenotype analysis of yeast cell requires high-throughput imaging and automatic analysis of abundant image data. At first, each cell needs to be segmented and labeled in the bright-field images. However, the ambiguous boundary of bright-field yeast cell images leads to the failure of traditional segmentation algorithms. We propose a segmentation algorithm based on the morphological characteristics of yeast cells. Seed points are first identified along the cell contour and then connected by an edge tracing approach. In this way, "ill-detected" noise points are removed so that edges of yeast cells can be successfully extracted in bright-field images with sparsely distributed cells. In densely packed images, yeast cells with normal morphology can also be correctly segmented and labeled.

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Classification of yeast cells from image features to evaluate pathogen conditions

Cited by 6 publications

References 20 publications

Machine learning approach to segment Saccharomyces cerevisiae yeast cells

Machine learning approach to segment Saccharomyces cerevisiae yeast cells

Segmentation for High-Throughput Image Analysis: Watershed Masked Clustering

Segmentation of yeast cell’s bright-field image with an edge-tracing algorithm

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