Gray-level co-occurrence matrix (GLCM) is a statistical method widely used to characterize images and specifically, for Second Harmonic Generation (SHG) collagen images characterization. This method takes into account the spatial relationship between the image pixels, at specific angle. It is usually calculated for four orientations, at specific distances. Over these matrix, a textural feature function is calculated. Often, results of different orientations are compared or averaged to get a unique statistic parameter. In the present report, we will demonstrate the error that bring with this methodology, and following, we offer the correction formula. Preferred orientation of SHG images is proposed as structural property to characterize biological samples. For example, for determining the parallelism grade of collagen fibers regarding the ovarian epithelium. Here, we present a robust method to calculate this parameter, based on the two-dimensional Fourier transform. Finally, we show how these two elements help improve the discrimination between normal and pathological ovarian tissues.
The analysis of deconvolution algorithms performance is crucial when is wanted to use deconvolution as an effective image restoration approach. The performance of deconvolution algorithms available in Open Source software was compared by using three-dimensional (3D) microscopy images. DeconvolutionLab and MitivBlindDeconvolution were the plugins chosen from FIJI and Icy respectively. In the first place, analyses included 3D bead stack measurements both pre- and post-deconvolution by using theoretical and empirical Point Spread Functions (PSFs) as well as parameter variation. The set of parameters that resulted in the improvement of both the 3D morphology and intensity of the beads was applied to 3D autofluorescence colon tissue images from BALB/c mice to evaluate if original morphology and intensity features were restored.
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