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By John G. Webster (Editor)

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As with most low-pass filtering, noise reduction comes at the cost of sensitivity. The more noise eliminated, the less distinct the edges will become. See Fig. 6 for examples of edge detection (3). Gradient-Based Edge Detection. The most common method for detecting edges in an intensity map is to use a gradient enhancer. The gradient enhancer is a special filtering process that highlights edges by returning the magnitude of the gradient of the image. Edges tend to be characterized by rapid changes in intensity and have high local gradients.

Perimeter. Perimeter can aid the determination of scale with area; however, because of the discrete nature of digital images, perimeter is very sensitive to noise and sampling errors. • Centroid. The centroid or center of area is used to determine the center of the object. Note that the calculation occurs on the two-dimensional image before any threedimensional reconstruction takes place. The measurement may not be valid for objects being observed from arbitrary angles in three space because of the different area profiles that are possible.

The pixels are stored in an array and processed again using one of many traditional lineor curve-fitting techniques. A graph-following algorithm is usually executed after an edge detection algorithm. The graph-following algorithm starts at a pixel known to be on a contour (usually just the gray-scale value) and adds pixels to the edge list based on a heuristic. , ‘‘add all pixels such that the curvature of the resulting line is minimized’’). Graph following can have two results—detecting the member pixels of a curve for interpretation using a curve fitting algorithm or storing a minimum number of points along a boundary in order to perform discrete segmentation and feature extraction.

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