Scopus İndeksli Yayınlar Koleksiyonu / Scopus Indexed Publications Collection

Permanent URI for this collectionhttps://hdl.handle.net/11147/7148

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Subject: search.filters.subject.Image processingWoS Q: search.filters.wosQuartile.Q2

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  • Article
    Citation - WoS: 246
    Citation - Scopus: 245
    Step-By Quantitative Analysis of Focal Adhesions
    (Elsevier Ltd., 2014) Horzum, Utku; Özdil, Berrin; Pesen Okvur, Devrim
    Focal adhesions (FAs) are specialized adhesive structures which serve as cellular communication units between cells and the surrounding extracellular matrix. FAs are involved in signal transduction and actin cytoskeleton organization. FAs mediate cell adhesion, which is a critical phenomenon in cancer research. Since cells can form many and micrometer scale FAs, their quantitative analysis demands well-optimized image analysis approaches [1-3]. Here, we have optimized the analysis of FAs of MDA-MB-231 breast cancer cells. The optimization is based on proper processing of immunofluorescence images of vinculin, which is one of the markers of FAs. All image processing steps are carried out using the ImageJ software, which is freely available and in the public domain. The advantages of our method are:The analysis steps are simplified by combining different plugins of the ImageJ program.FAs are better detected with minimal false negatives due to optimized processing of fluorescent images.This approach can be applied to quantify a variety of fluorescent images comprising focal and/or localized signals within a high background such as FAs, one of the many complex signaling structures in a cell.
  • Article
    Citation - WoS: 57
    Citation - Scopus: 69
    Prediction of the Weight of Alaskan Pollock Using Image Analysis
    (John Wiley and Sons Inc., 2010) Balaban, Murat Ömer; Chombeau, Melanie; Cırban, Dilşat; Gümüş, Bahar
    Determining the size and quality attributes of fish by machine vision is gaining acceptance and increasing use in the seafood industry. Objectivity, speed, and record keeping are advantages in using this method. The objective of this work was to develop the mathematical correlations to predict the weight of whole Alaskan Pollock (Theragra chalcogramma) based on its view area from a camera. One hundred and sixty whole Pollock were obtained fresh, within 2 d after catch from a Kodiak, Alaska, processing plant. The fish were first weighed, then placed in a light box equipped with a Nikon D200 digital camera. A reference square of known surface area was placed by the fish. The obtained image was analyzed to calculate the view area of each fish. The following equations were used to fit the view area (X) compared with weight (Y) data: linear, power, and 2nd-order polynomial. The power fit (Y = A·XB) gave the highest R2 for the fit (0.99). The effect of fins and tail on the accuracy of the weight prediction using view area were evaluated. Removing fins and tails did not improve prediction accuracy. Machine vision can accurately predict the weight of whole Pollock. © 2010 Institute of Food Technologists®.