2-D and 3-D Image Registration for Medical, Remote Sensing, - download pdf or read online

By A. Ardeshir Goshtasby

A finished source at the basics and state-of-the-art in picture registration This complete ebook offers the suitable theories and underlying algorithms had to grasp the fundamentals of photo registration and to find the state of the art strategies utilized in scientific purposes, distant sensing, and business functions. 2-D and 3-D photo Registration starts off with definitions of major phrases after which offers a close exam-ple of picture registration, describing each one severe step. subsequent, preprocessing strategies for picture registration are mentioned. The center of the textual content offers insurance of the entire key recommendations had to comprehend, implement,and overview a number of photo registration tools. those key equipment contain: * function choice * characteristic correspondence * Transformation capabilities * evaluate equipment * picture fusion * snapshot mosaicking

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Extra info for 2-D and 3-D Image Registration for Medical, Remote Sensing, and Industrial Applications

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Geometric distortions due to lens nonlinearities can be corrected by placing a uniform grid in front of the camera and determining a transformation that maps the captured grid to the ideal grid [151, 205, 408]. Distortions in MR images occur due to the inhomogeneous magnetic field and the spatial nonlinearity of the field gradients and vary with tissue type. Distortions that are independent of the tissue type can be corrected by placing a uniform 3-D grid in the space of the scanner, obtaining an image of the grid, and determining a transformation function that maps the captured grid to the ideal grid [289, 340, 345].

The original image is shown in Fig. 14a and the obtained edges are shown in Fig. 14c. The luminance component of the color image obtained by averaging the red, green, and blue components is shown in Fig. 14b, and edges of the luminance image obtained by locating locally maximum gradients in the gradient direction are shown in Fig. 14d. We see that color edges pick up sharp changes in chroma in addition to sharp changes in luminance. Color edges separate adjacent regions with significant color differences.

56) fz (x, y, z) = ∂G(z) ∂z G(x) G(y) f (x, y, z). 58) and gradient direction at (x, y, z) is a unit vector with components u(x, y, z) = fx (x, y, z) fy (x, y, z) fz (x, y, z) , , M (x, y, z) M (x, y, z) M (x, y, z) . 59) 36 PREPROCESSING Note that convolutions in 2-D as well as in 3-D are performed by a combination of 1-D convolutions to achieve high speed. 60) ∂x2 ∂y 2 ∂z 2 separately with the image, creating two images, and then finding the ratio of the images point by point. The process closely follows that described for 2-D images.

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