Kalle Marjanen

Estimation of population effects in synchronized budding yeast experiments

An approach for estimating the distribution of a synchronized budding yeast (Saccharomyces cerevisiae) cell population is discussed. This involves estimation of the phase of the cell cycle for each cell. The approach is based on counting the number of buds of different sizes in budding yeast images. An image processing procedure is presented for the bud-counting task. The procedure employs clustering of the local mean-variance space for segmentation of the images. The subsequent bud-detection step is based on an object separation method which utilizes the chain code representation of objects as well as labeling of connected components. The procedure is tested with microscopic images that were obtained in a time-series experiment of a synchronized budding yeast cell population. The use of the distribution estimate of the cell population for inverse filtering of signals that are obtained in time-series microarray measurements is discussed as well.

Computational methods for estimation of cell cycle phase distributions of yeast cells

Two computational methods for estimating the cell cycle phase distribution of a budding yeast (Saccharomyces cerevisiae) cell population are presented. The first one is a nonparametric method that is based on the analysis of DNA content in the individual cells of the population. The DNA content is measured with a fluorescence-activated cell sorter (FACS). The second method is based on budding index analysis. An automated image analysis method is presented for the task of detecting the cells and buds. The proposed methods can be used to obtain quantitative information on the cell cycle phase distribution of a budding yeast S. cerevisiae population. They therefore provide a solid basis for obtaining the complementary information needed in deconvolution of gene expression data. As a case study, both methods are tested with data that were obtained in a time series experiment with S. cerevisiae. The details of the time series experiment as well as the image and FACS data obtained in the experiment can be found in the online additional material at http://www.cs.tut.fi/sgn/csb/yeastdistrib/.

Measurement of annual ring width of log ends in forest machinery

The quality of wood is of increasing importance in wood industry. One important quality aspect is the average annual ring width and its standard deviation that is related to the wood strength and stiffness. We present a camera based measurement system for annual ring measurements. The camera system is designed for outdoor use in forest harvesters. Several challenges arise, such as the quality of cutting process, camera positioning and the light variations. In the freshly cut surface of log end the annual rings are somewhat unclear due to small splinters and saw marks. In the harvester the optical axis of camera cannot be set orthogonally to the log end causing non-constant resolution of the image. The amount of natural light in forest varies from total winter darkness to midsummer brightness. In our approach the image is first geometrically transformed to orthogonal geometry. The annual ring width is measured with two-dimensional power spectra. The two-dimensional power spectra combined with the transformation provide a robust method for estimating the mean and the standard deviation of annual ring width. With laser lighting the variability due to natural lighting can be minimized.

Machine Vision Based Measurement of Dynamic Contact Angles in Microchannel Flows

When characterizing flows in miniaturized channels, determination of the dynamic contact angle is important. By measuring the dynamic contact angle, the flow properties of the flowing liquid and the effect of material properties on the flow can be characterized. A machine vision based system to measure the contact angle of a front or rear meniscus of a moving liquid plug is described in this paper. In this research, transparent MABS-based flow channel structures, sealed with adhesive tapes are used. The transparency of the channels enables image based monitoring and measurements of flow variables, including the dynamic contact angle. It is shown that the dynamic angle can be measured from a liquid flow in a channel using the image based measurement system. The image processing algorithm has been developed in a MATLAB environment. Imaging is done using a CCD camera (Firewire connection) and illumination is created using a custom made ring light.Copyright

An efficient approach for site-specific scenery prediction in surveillance imaging near Earth's surface

Surveillance camera automation and camera network development are growing areas of interest. This paper proposes a competent approach to enhance the camera surveillance with Geographic Information Systems (GIS) when the camera is located at the height of 10-1000 m. A digital elevation model (DEM), a terrain class model, and a flight obstacle register comprise exploited auxiliary information. The approach takes into account spherical shape of the Earth and realistic terrain slopes. Accordingly, considering also forests, it determines visible and shadow regions. The efficiency arises out of reduced dimensionality in the visibility computation. Image processing is aided by predicting certain advance features of visible terrain. The features include distance from the camera and the terrain or object class such as coniferous forest, field, urban site, lake, or mast. The performance of the approach is studied by comparing a photograph of Finnish forested landscape with the prediction. The predicted background is well-fitting, and potential knowledge-aid for various purposes becomes apparent.

Analysis of system noise in thermal imagers

Two different thermal imagers are tested to find out their system noise properties such as the noise variance, the distribution of the system noise, the effect of the scanning element in the image and the possible uneven distribution of the temperature caused by the optics or other phenomena. The obtained results can be used for comparing the properties of different thermal imagers and in the process of designing optimal image processing algorithms. The system noise estimation is done with three different methods under certain assumptions. These methods are; the use of the two-dimensional autocorrelation-function and the fitted polynomial, the use of suitable high frequencies of the two-dimensional spectrum and the use of stable image series. The first two methods are closely related and can give the noise variance only. The shape of the system noise histogram can be approximated somewhat from the image series under suitable conditions. The variability between the even and the odd lines in image and other, possibly stable phenomena, are also analysed. These methods are first tested with simulated data sets and comparison between the methods is performed. Also real image series from two different cameras are used and conclusions regarding their performance are drawn.

Estimation of the distribution type and parameters based on multimodal histograms

In many applications involving measuring a physical phenomenon, the output data contains a mixture of different type of distributions. The data set consists often of unimodal distributions, which overlap, i.e. the ranges of the corresponding random variables have a significant intersection. After observing a multimodal histogram that has several partially overlapping distributions the aim is to separate them by inferring the correct types of the probability density functions (PDFs) and their parameters. The method is based on the non-linear least squares estimation, where several types of PDFs are fitted to the region mostly affected by a single distribution. The possible candidate PDFs are those of the Pearson system, Weibull, Fisher, chi-squared and Rayleigh distributions. This method can be extended to multidimensional cases in certain situations. The methods developed earlier for this task are based for example on the QQ-plot technique and on order statistic filter banks. The found distribution types and their parameters can be applied to different tasks in image processing and system analysis. This algorithm can be used e.g. to the estimation of PDFs of certain phenomena and to global thresholding of images. The method is applied to real two-dimensional data sets having values coming from several distributions.