DeadEasy Mito-Glia: Automatic Counting of Mitotic Cells and Glial Cells in Drosophila

Cell number changes during normal development, and in disease (e.g., neurodegeneration, cancer). Many genes affect cell number, thus functional genetic analysis frequently requires analysis of cell number alterations upon loss of function mutations or in gain of function experiments. Drosophila is a most powerful model organism to investigate the function of genes involved in development or disease in vivo. Image processing and pattern recognition techniques can be used to extract information from microscopy images to quantify automatically distinct cellular features, but these methods are still not very extended in this model organism. Thus cellular quantification is often carried out manually, which is laborious, tedious, error prone or humanly unfeasible. Here, we present DeadEasy Mito-Glia, an image processing method to count automatically the number of mitotic cells labelled with anti-phospho-histone H3 and of glial cells labelled with anti-Repo in Drosophila embryos. This programme belongs to the DeadEasy suite of which we have previously developed versions to count apoptotic cells and neuronal nuclei. Having separate programmes is paramount for accuracy. DeadEasy Mito-Glia is very easy to use, fast, objective and very accurate when counting dividing cells and glial cells labelled with a nuclear marker. Although this method has been validated for Drosophila embryos, we provide an interactive window for biologists to easily extend its application to other nuclear markers and other sample types. DeadEasy MitoGlia is freely available as an ImageJ plug-in, it increases the repertoire of tools for in vivo genetic analysis, and it will be of interest to a broad community of developmental, cancer and neuro-biologists

The Generalized Tailor Problem

. The so-called `Tailor Problem' concerns putting a number of sets within another set by translation, such that the translated sets do not overlap. In this paper we consider a generalization of this problem in which also rotations of the sets are allowed. Key words: Tailor problem, Minkowski operations, group morphology. 1. Introduction The goal of this paper is to give a solution by morphological operators to the following Generalized Tailor Problem: Problem Given a set X and a collection of sets A 1 ; A 2 ; : : : ; An , is it possible to put A 1 ; A 2 ; : : : ; An within X using translations and rotations such that no two of the translated and/or rotated sets intersect? If so, what are the possible solutions? The problem where only translations are allowed (the Tailor Problem) was posed by Serra [5], see also [2]. He obtained an elegant solution in terms of Minkowski operations. Our solution of the Generalized Tailor Problem involves a general construction of morphological opera..

The effect of image enhancement on the statistical analysis of functional neuroimages:Wavelet-based denoising and Gaussian smoothing

The quality of statistical analyses of functional neuroimages is studied after applying various preprocessing methods. We present wavelet-based denoising as an alternative to Gaussian smoothing, the standard denoising method in statistical parametric mapping (SPM). The wavelet-based denoising schemes are extensions of WaveLab routines, using the symmetric orthogonal cubic spline wavelet basis. In a first study, activity in a time series is simulated by superimposing a time-dependent signal on a selected region. We add noise with a known signal-to-noise ratio (SNR) and spatial correlation. After denoising, the statistical analysis, performed with SPM, is evaluated. We compare the shapes of activations detected after applying the wavelet-based methods with the shapes of activations detected after Gaussian smoothing. In a second study, the denoising schemes are applied to a real functional MRI time series, where signal and noise cannot be separated. The denoised time series are analysed with SPM, while false discovery rate (FDR) control is used to correct for multiple testing. Wavelet-based denoising, combined with FDR control, yields reliable activation maps. While Gaussian smoothing and wavelet-based methods producing smooth images work well with very low SNRs, less smoothing wavelet-based methods produce better results for time series of moderate quality

Efficient surface reconstruction using generalized Coulomb potentials

We propose a novel, geometrically adaptive method for surface reconstruction from noisy and sparse point clouds, without orientation information. The method employs a fast convection algorithm to attract the evolving surface towards the data points. The force field in which the surface is convected is based on generalized Coulomb potentials evaluated on an adaptive grid (i.e., an octree) using a fast, hierarchical algorithm. Formulating reconstruction as a convection problem in a velocity field generated by Coulomb potentials offers a number of advantages. Unlike methods which compute the distance from the data set to the implicit surface, which are sensitive to noise due to the very reliance on the distance transform, our method is highly resilient to shot noise since global, generalized Coulomb potentials can be used to disregard the presence of outliers due to noise. Coulomb potentials represent long-range interactions that consider all data points at once, and thus they convey global information which is crucial in the fitting process. Both the spatial and temporal complexities of our spatially-adaptive method are proportional to the size of the reconstructed object, which makes our method compare favorably with respect to previous approaches in terms of speed and flexibility. Experiments with sparse as well as noisy data sets show that the method is capable of delivering crisp and detailed yet smooth surfaces

Hyperconnectivity, Attribute-Space Connectivity and Path Openings: Theoretical Relationships

In this paper the relationship of hyperconnected filters with path openings and attribute-space connected filters is studied. Using a recently developed axiomatic framework based on hyperconnectivity operators, which are the hyperconnected equivalents of connectivity openings, it is shown that path openings are a special case of hyperconnected area openings. The new axiomatics also yield insight into the relationship between hyperconnectivity and attribute-space connectivity. It is shown any hyperconnectivity is an attribute-space connectivity, but that the reverse is not true