Quantitative comparison of image processing methods to automatically track intensity changes in submicron-sized cellular structures.

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Poster Session 2
Trupti Gore (1, 2), David Dang (1), Viji Draviam (1)
1. School of Biological and Chemical Sciences, Queen Mary University of London, London
2. University College London, London

image analysis,image segmentation, kinetochores, time-lapse microscopy

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Human kinetochores are submicron-sized cellular structures made up of over 100 proteins. These structures ensure the proper attachment of chromosomes to microtubules which is essential for the accurate segregation of chromosomes.  To understand how the kinetochore structure and function are regulated, it's important to quantify changes in kinetochore components through time. Here we first compare a variety of image analysis methods to quantify changes in kinetochore protein intensities in time-lapse microscopy movies. We present an image analysis workflow starting with image segmentation – which involves filters to denoise the image, a threshold to create a binary image and then applying the mathematical morphological operations, such as erosion and dilation to fill the holes or remove tiny particles. Particles are labelled and analysed for their size, shape and intensity. The diversity of image content means that there is no ‘one size fits all’ method, hence the sequence of techniques in the workflow can be changed to obtain the optimal results for the image dataset. Second, we identify at least two automated methods to assess the quality of the image analysis regimens, reducing the effort involved in assessing quality manually. Based on apriori kinetochore knowledge and image analysis results, a method is finalised for that dataset. Third, we generalise the image analysis regimen to multiple kinetochore protein combinations, highlighting the strengths of image processing and kinetochore protein intensity measurement methods proposed here. The workflow we propose is likely to be useful for tracking and measuring changes in submicron structures in microscopy.