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Binary Images

Thresholding an image converts a gray scale or color original to a black-and-white version that distinguishes feature(s) from background. The features are composed of those pixels that are of current interest for some kind of measurement procedure, whereas the background consists of the pixels that are not of current interest. The features are usually assumed to correspond to some objects or structure that is present in the scene. Of course, the original image should always be kept because in the future the objects or structures of interest may change!

Thresholding - The most widely used thresholding methods utilize the image histogram. Manual interactive setting of thresholds on the histogram while viewing the image can be used to produce a binary image, and most programs offer this capability. Automatic methods are more consistent but the choice of an effective algorithm depends on the nature of the original image.

Erosion, Dilation, Opening, and Closing - The binary images produced by thresholding rarely provide a perfect delineation of the features or structures of interest. Variations in pixel brightness or color, such as random or shot noise in the original image, can cause some pixels to be included or excluded. Imperfections introduced in specimen preparation or imaging can include fine lines or rough borders. These types of defects in binary images are usually addressed by morphological operations.

Watershed and Skeletons - The Euclidean Distance Map introduced as a tool for erosion is also important as the basis for a technique called watershed segmentation that can separate features which touch each other. Skeletonization is produced by an erosion that iteratively removes pixels leaving just the midlines of structures. These lines capture the topological shape of objects.

Boolean Combinations - Combining binary images using Boolean logic makes it possible to select structures or objects based on multiple criteria. For example, the thresholded binary images from the red, green and blue channels may not by themselves select features of a single color. Combinations based on texture, brightness, skeletons, and other results from processing are often used.

Contributing Authors

John C. Russ - Materials Science and Engineering Dept., North Carolina State University, Raleigh, North Carolina, 27695.

Matthew Parry-Hill and Michael W. Davidson - National High Magnetic Field Laboratory, 1800 East Paul Dirac Dr., The Florida State University, Tallahassee, Florida, 32310.



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