Download Medical Image Processing, Reconstruction and Restoration: by Jiri Jan PDF

By Jiri Jan

Scientific imaging is particular in that it issues inner buildings of organisms which are inaccessible to universal imaging equipment and that the imaging effects are saw, evaluated, and categorised usually through non-technical employees. improvement of latest photo processing tools and the translation of complex or unforeseen effects require deep knowing of the underlying idea and strategies all in favour of snapshot processing and research. the writer of this article presents a company grounding within the rules in addition to perception into the way in which of treating the picture fabric. This publication offers readers with constant reasoning in keeping with a company seize of photograph processing basics, who prefer the continual good comprehensible rationalization instead of strict mathematical formalism.

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Extra info for Medical Image Processing, Reconstruction and Restoration: Concepts and Methods

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7 A scheme of a simple (optical) two-dimensional system — forming of the output image as a sum of weighted and shifted impulse responses. which is the convolution integral describing the already mentioned operation of convolution between two images, as symbolized by the star operator. We can thus conclude this derivation with the statement that the output of a linear space-invariant two-dimensional system is the two-dimensional convolution of its PSF and the input image. 7. Each point of the input image may be considered an impulse source with the weight f(x, y), to which the system responds with the PSF, weighted by the value of f(x, y) and correspondingly shifted — its origin located at the point x, y in the output image.

Here N r(x, y) is an unambiguous (but not necessarily inversible) function of a single variable, the definition extent of which is given by the range of the input image values, while the range of the function values corresponds to the image values of output. The left equation describes a gray-scale transform (also often called contrast transform), while the right equation, where the vector function value represents a color g = [ gR, gG, gB], leads to a false color presentation of the original gray-scale image.

However, in the mentioned example, like in many other cases suitable for homomorphic filtering, the formulation of the spectral properties is vague anyway, and the differences between the spectra of converted and nonconverted components are not critical. A smooth and monotonous character of the nonlinear transform supports this. fm Page 33 Monday, October 3, 2005 6:15 PM Analogue (Continuous-Space) Image Representation 33 without any dramatic cutoffs, as there is no distinct frequency border between the useful and disturbing components.

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