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Figure 1.1b Andre Duerinckx, the Conference Chairman of the First SPIE Medical Imaging Conference (International Society for Optical Engineering) where the term PACS was coined.

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of concentric circular fringes For the light source we can use a sodium or even a uorescent lamp In the situation where the laser is the source of light, there is a much higher limit for the value of t Even though several meters can be used for t, we shall set t 1000 mm In this case, using l 632:8 nm, we get for f an upper limit of 5 s of arc Hence it is not dif cult to design a collimating system to satisfy this condition Another aspect that is important, especially with large values of t, is the lateral shear one can get in the instrument To avoid this, the autocollimated pinhole images must coincide with the pinhole itself Similarly, if the collimating lens is not properly collimated, either a convergent or a divergent beam will emerge.

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The collimation may be accurately performed by using any of the various devices available, such as the plane parallel plate shearing interferometer (Murty, 1964b) A somewhat better method is to use a lens for focusing the system of Haidinger fringes on its focal plane This requires a setup almost identical to that for the Fizeau interferometer The only difference is that, instead of a pinhole, a wider aperture is used to have a large angular size for the source The Haidinger fringes are then formed in the focal plane of the lens 141 Applications of Haidinger Fringes The Haidinger fringes may be used as a complementary test to that provided by the Fizeau interferometer If we are testing a nearly parallel plate, we can nd its wedge angle either by the Fizeau or by the Haidinger method.

FIGURE 4.16 The contents of the cyan, magenta, yellow, and black channels from the image first seen in Figure 4.15

In the Haidinger method we look for the stability of the concentric fringes as we move our line of sight across the plate with a small aperture If t is slowly varying, the center of the.

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We develop two distinct sets of mathematical tools: 1. Transformation tools, which enable us to analyze propagation of elds from one space to another 2. Sampling tools, which enable us to represent continuous eld distributions using discrete sets of numbers A third set of mathematical tools, signal encoding and estimation from sample data, is considered in s 7 and 8. This chapter initially considers transformation and sampling in the context of conventional Fourier analysis, meaning that transformations are based on Fourier transfer functions and impulse response kernels and sampling is based on the Whittaker Shannon sampling theorem. Since the mid-1980s, however, dramatic discoveries have revolutionized harmonic signal analysis. New tools drawn from wavelet theory allow us to compare various mathematical models for elds, including different classes of functions (plane waves, modes, and wavelets) that can be used to represent elds. Different mathematical bases enable us to exibly distribute discrete components to optimize computational processing ef ciency in eld analysis. Section 3.2 broadly describes the nature of elds and eld transformations. Sections 3.3 3.5 focus on basic properties of Fourier and Fresnel transformations. We consider conventional Shannon sampling in Section 3.6. Section 3.7 discusses discrete numerical analysis of linear transformations. Sections 3.8 3.10 brie y extend sampling and discrete transformation analysis to include wavelet analysis. 3.2 FIELDS AND TRANSFORMATIONS

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