Automatic Detection and Classification of Ca2+ Release Events in Confocal Line- and Framescan Images

Abstractabstract text

Introduction

Numerous methods exist for analysing sparks in confocal linescans. These employ various different approaches: noise tresholding \cite{Ros2001,Picht2007}, wavelet transform \cite{Szab_2010}, etc.

Recently, a method was developed by Tian et al., (Tian 2012), where fluorescence time trace in each pixel is fitted and provides a practically noise free approximation of the original fluorescence data. This pixel-by-pixel method had, however, several limitations which made it impractical to be used for Ca2+ release event detection.

Here we extend the method by Tian et al., in several ways. The new method allows for Ca2+ release event classification based on pixel-by-pixel denoising of the original signal.

Methods

Cell isolations

Experimental

Data acquisition was performed on two confocal setups. Linescan images were obtained on a Olympus FluoView 1000 confocal microscope. Framescan images were recorded with a VTInfinity multi-beam confocal microscope recording 512x64 pixel images at 150Hz freqency.

Computational

The detection algorithm is presented in the Results section.

Results

Pixel by pixel event classification

The algorithm is presented schematically on figure 1. Each subroutine is explained in detail below.

Image preprocessing

The only preprocessing step used is convolving the image with a \((2n+1)\times(2n+1)\) kernel where the center element is \(1/(n+1)\) and the k-th layer surrounding the center is made up of values \(1/(8k\cdot(n+1))\). For example, when n=1 the kernel would be \[ \left( \begin{array}{ccc} ^1/_{16} & ^1/_{16} & ^1/_{16}\\\\ ^1/_{16} & ^1/_{2}& ^1/_{16}\\\\ ^1/_{16} & ^1/_{16} & ^1/_{16} \end{array} \right) \] Convolving the image with this kind of kernel reduces the noise while retaining more of the original signal than simple averaging. Contributions from the i-th layer around the center will have the same weight as the central pixel. In this work we use the kernel with \(n=1\).