The single cell gel electrophoresis, called Comet Assay is a microelectrophoretic technique of direct visualization of DNA damage at the cell level. In the comet assay, the cells suspended in an agarose gel on a microscope slide are subjected to lysis, unwinding of DNA and electrophoresis. After staining with fluorescent DNA binding dye, cells with DNA damage display increased migration of genetic material from the cell nucleus. Under the influence of weak, statics electric field, charged DNA migrates away from the nucleus forming a so called comet. The damage is quantified by measuring the amound of the genetic material, which migrates from the nucleus to form the comet tail. The foremost advantage of the comet assay is that it analyses individual cells, thus allowing the measurement of the heterogeneity of response within a cell population. In this paper we present three novel method of the comet tail and head extraction.
In this paper a novel method of noise reduction in color images is presented. The new technique is capable of attenuating both impulsive and Gaussian noise, while preserving and even enhancing sharpness of the image edges. Extensive simulations reveal that the new method outperforms significantly the standard techniques widely used in multivariate signal processing. In this work we apply the new noise reduction method for the enhancement of the images of gene chips. We demonstrate that the new technique is capable of reducing various kinds of noise present in microarray images and that it enables efficient spot location and estimation of the gene expression level due to the smoothing effect and preservation of the spot edges. This paper contains the comparison of the new technique of noise reduction with the standard procedures used for the processing of vector valued images, as well as examples of the efficiency of the new algorithm when applied to typical microarray images.
In this paper a new approach to the problem of impulsive noise reduction for color images is introduced. The presented self-adaptive image filter is based on a model of a virtual particle, which performs a random walk on the image lattice, with transition probabilities derived from the Gibbs distribution. The major advantage of the new filtering technique, is that it filters out the noise component, while adapting itself to the local image structures. In this way the new algorithm is able to eliminate strong impulsive noise, while preserving edges and fine image details. As the algorithm is a fuzzy modification of the commonly used vector median operator, it is very fast and easy to implement. Our results show that the proposed method outperforms all standard algorithms of the reduction of impulsive noise in color images.
JavaScript jest wyłączony w Twojej przeglądarce internetowej. Włącz go, a następnie odśwież stronę, aby móc w pełni z niej korzystać.