Summary It is generally accepted that the functional compartmentalization of eukaryotic cells is reflected by the differential occurrence of proteins in their compartments. The location and physiological function of a protein are closely related; local information of a protein is thus crucial to understanding its role in biological processes. The visualization of proteins residing on intracellular structures by fluorescence microscopy has become a routine approach in cell biology and is increasingly used to assess their colocalization with well-characterized markers. However, imageanalysis methods for colocalization studies are a field of contention and enigma. We have therefore undertaken to review the most currently used colocalization analysis methods, introducing the basic optical concepts important for image acquisition and subsequent analysis. We provide a summary of practical tips for image acquisition and treatment that should precede proper colocalization analysis. Furthermore, we discuss the application and feasibility of colocalization tools for various biological colocalization situations and discuss their respective strengths and weaknesses. We have created a novel toolbox for subcellular colocalization analysis under Image J, named JACoP, that integrates current global statistic methods and a novel object-based approach.

A guided tour into subcellular colocalization analysis in light microscopy

A review on image segmentation techniques

http://crocker.seas.upenn.edu/CrockerGrier1996b.pdf

Methods of Digital Video Microscopy for Colloidal Studies

MYC on the Path to Cancer

Preface Through many centuries physics has been one of the most fruitful sources of inspiration for mathematics. As a consequence, mathematics has become an economic language providing a few basic principles which allow to explain a large variety of physical phenomena. Many of them are described in terms of partial diierential equations (PDEs). In recent years, however, mathematics also has been stimulated by other novel elds such as image processing. Goals like image segmentation, multiscale image representation, or image restoration cause a lot of challenging mathematical questions. Nevertheless, these problems frequently have been tackled with a pool of heuristical recipes. Since the treatment of digital images requires very much computing power, these methods had to be fairly simple. With the tremendous advances in computer technology in the last decade, it has become possible to apply more sophisticated techniques such as PDE-based methods which have been inspired by physical processes. Among these techniques, parabolic PDEs have found a lot of attention for smoothing and restoration purposes, see e.g. 113]. To restore images these equations frequently arise from gradient descent methods applied to variational problems. Image smoothing by parabolic PDEs is closely related to the scale-space concept where one embeds the original image into a family of subsequently simpler , more global representations of it. This idea plays a fundamental role for extracting semantically important information. The pioneering work of Alvarez, Guichard, Lions and Morel 11] has demonstrated that all scale-spaces fulllling a few fairly natural axioms are governed by parabolic PDEs with the original image as initial condition. Within this framework, two classes can be justiied in a rigorous way as scale-spaces: the linear diiusion equation with constant dif-fusivity and nonlinear so-called morphological PDEs. All these methods satisfy a monotony axiom as smoothing requirement which states that, if one image is brighter than another, then this order is preserved during the entire scale-space evolution. An interesting class of parabolic equations which pursue both scale-space and restoration intentions is given by nonlinear diiusion lters. Methods of this type have been proposed for the rst time by Perona and Malik in 1987 190]. In v vi PREFACE order to smooth the image and to simultaneously enhance semantically important features such as edges, they apply a diiusion process whose diiusivity is steered by local image properties. These lters are diicult to analyse mathematically , as they may act locally like a backward diiusion process. …

/pdf/anisotropic-diffusion-in-image-processing-z13u09qvsm.pdf

Anisotropic diffusion in image processing

In this paper the Kolmogorov-Smirnov statistical test for the analysis of histograms is presented. The test is discussed for both the two-sample case (comparing fn1(X) to fn2 (X)) and the one-sample case (comparing fn1 (X) to f(X)). Presentation of the specific algorithmic steps involved is done through development of an example where the data are from an experiment discussed elsewhere in this issue. It is shown that the two histograms examined come from two different parent populations at the 99.9% confidence level.

Proof without prejudice: use of the Kolmogorov-Smirnov test for the analysis of histograms from flow systems and other sources.:

https://www.cell.com/biophysj/pdf/S0006-3495(95)80442-X.pdf

Photobleaching kinetics of fluorescein in quantitative fluorescence microscopy

A number of functions for the autofocusing of microscopes and other optical instruments are to be found in the literature. In this article we compare 11 of them to determine, in an objective manner, which functions are most suitable for implementation with real-time video acquisition systems. Three different images, each representing a typical class of image, are used in the comparison. Among the best focus functions found in our study are the squared magnitude gradient, the squared Laplacian, and the normalized image standard deviation.

/pdf/a-comparison-of-different-focus-functions-for-use-in-4gg29b5jtx.pdf

A comparison of different focus functions for use in autofocus algorithms

Background: Spectral imaging extends the capabilities of biological and clinical studies to simultaneously study multiple features such as organelles and proteins qualitatively and quantitatively. Spectral imaging combines two well-known scientific methodologies, namely spectroscopy and imaging, to provide a new advantageous tool. The need to measure the spectrum at each point of the image requires combining dispersive optics with the more common imaging equipment, and introduces constrains as well. Methods and Results: The principles of spectral imaging and a few representative applications are described. Spectral imaging analysis is necessary because the complex data structure cannot be analyzed visually. A few of the algorithms are discussed with emphasis on the usage for different experimental modes (fluorescence and bright field). Finally, spectral imaging, like any method, should be evaluated in light of its advantages to specific applications, a selection of which is described. Conclusions: Spectral imaging is a relatively new technique and its full potential is yet to be exploited. Nevertheless, several applications have already shown its potential. q 2006 International Society for Analytical Cytology

http://www.cb.uu.se/~milan/histo/before2011august/GariniYoung06.pdf

Spectral imaging: Principles and applications

https://repository.tudelft.nl/islandora/object/uuid%3A6fea1bc9-de9c-450f-8e34-a33c1129589a/datastream/OBJ/download

Ian T. Young

Papers

Proof without prejudice: use of the Kolmogorov-Smirnov test for the analysis of histograms from flow systems and other sources.:

Photobleaching kinetics of fluorescein in quantitative fluorescence microscopy

A comparison of different focus functions for use in autofocus algorithms

Spectral imaging: Principles and applications

Recursive implementation of the Gaussian filter