computer algorithms for statistical calculations

Taken together, these findings indicate that deblurring algorithms improve contrast, but they do so at the expense of decreasing the signal-to-noise ratio and may also introduce structural artifacts in the image.

This problem is particularly severe when deblurring single two-dimensional images because they often contain diffraction rings or light from other structures that will then be sharpened as if they were in that focal plane. Third, features whose point spread functions overlap in particular z planes may be sharpened in planes where they do not really belong (in effect, the apparent position of features may be altered).

Second, deblurring algorithms remove blurred signal and thus reduce overall signal levels. First, noise from several planes is added together. However, there are several major disadvantages to these approaches. The deblurring algorithms are computationally economical because they involve relatively simple calculations performed on single image planes. The data were acquired from a preparation of Xenopus cells stained for microtubules. Figure 1 presents a single focal plane selected from a three-dimensional stack of optical sections, before processing (Figure 1(a)), and after deconvolution by a nearest-neighbor algorithm (Figure 1(b)). In this manner, an estimate of the blur is removed from each plane. A three-dimensional stack is processed by applying the algorithm to every plane in the stack. Multi-neighbor techniques extend this concept to a user-selectable number of planes. For example, the nearest-neighbor algorithm operates on the plane z by blurring the neighboring planes (z + 1 and z - 1, using a digital blurring filter), then subtracting the blurred planes from the z plane. As a class, these algorithms apply an operation plane-by-plane to each two-dimensional plane of a three-dimensional image stack. Particular regions of interest within the image are referred to as features.Īlthough common algorithms often referred to as nearest-neighbor, multi-neighbor, no-neighbor, and unsharp masking are fundamentally two-dimensional, they are classified for the purposes of this discussion as deblurring algorithms. A raw image refers to an unprocessed digital image or image stack acquired from the microscope. The object refers to a three-dimensional pattern of light emitted by fluorescent structures in the microscope's field of view. Before continuing, several technical terms must be defined.

Computer algorithms for statistical calculations