Herrera-Navarro AM et al. (2014), Detection and measurement of the intracellular ...

XB-ART-50908

Comput Math Methods Med 2014 Jan 01;2014:484656. doi: 10.1155/2014/484656.

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Detection and measurement of the intracellular calcium variation in follicular cells.

Herrera-Navarro AM , Terol-Villalobos IR , Jiménez-Hernández H , Peregrina-Barreto H , Gonzalez-Barboza JJ .

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This work presents a new method for measuring the variation of intracellular calcium in follicular cells. The proposal consists in two stages: (i) the detection of the cell's nuclei and (ii) the analysis of the fluorescence variations. The first stage is performed via watershed modified transformation, where the process of labeling is controlled. The detection process uses the contours of the cells as descriptors, where they are enhanced with a morphological filter that homogenizes the luminance variation of the image. In the second stage, the fluorescence variations are modeled as an exponential decreasing function, where the fluorescence variations are highly correlated with the changes of intracellular free Ca(2+). Additionally, it is introduced a new morphological called medium reconstruction process, which helps to enhance the data for the modeling process. This filter exploits the undermodeling and overmodeling properties of reconstruction operators, such that it preserves the structure of the original signal. Finally, an experimental process shows evidence of the capabilities of the proposal.

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Species referenced: Xenopus laevis

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References [+] :

Berridge, Calcium signalling: dynamics, homeostasis and remodelling. 2003, Pubmed

	Figure 1. (a) Original image, (b) original image in pseudocolor before background correction, (c) opening morphological, and (d) image after background correction.
	Figure 2. (a) Input image, (b) regional minima of original image, (c) minima obtained after applied closing by reconstruction, (d) function constructed from the minimum of the sequence of image, (e) morphological closing φλ=3, (f) minima obtained by the difference: Mi(x) = Mi(x) − γλ=6Mi(x), and (g) set of markers obtained by the function Im(x).
	Figure 3. (a) Input image, (b) internal gradient, (c) external gradient, and (d) segmented cells.
	Figure 4. Cells volume curves over time showing an exponential decreasing behavior.
	Figure 5. (a) Original signal that has multiple maxima caused by the noise interference. (b) Filtered signal presents a smoothing wave in which the global maximum is easy to detect.
	Figure 6. Fitting an exponential curve over the volume of cell behavior.
	Figure 7. Histogram of differences from original signal and reconstructed signal (a) opening operator histogram, (b) closing operator histogram.
	Figure 8. (a) Matching of original data and filtered data with the morphological medium filter. (b) Histogram of differences from original surface and reconstructed surface, as noted, the expected value is centered in zero and would be considered as a normal distribution.
	Figure 9. Block's diagram of the proposal.
	Figure 10. (a) Cells segmented, (b) curves calculated by the least squares fitting.