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Distinguishing Textures

In many cases, the objects or structures that need to be distinguished in images are not characterized by color or brightness values that are different from the surrounding background. Instead, the difference may be one of texture. Human vision recognizes features based on this property. This interactive Java tutorial illustrates the application of filters to convert textural differences to brightness differences for regions in an image. In the illustrations in the tutorial, starting with an artificial texture and then in a light microscope image of cheese and an electron microscope image of liver tissue, there is no unique brightness difference in the original structures, but by calculating either the range or the variance of the pixel values in a neighborhood with radius = 2.5 pixels each distinct region is assigned a different brightness. Notice that the image histogram after processing has distinct peaks that correspond to the structures.

Interactive Java Tutorial
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The tutorial initializes with a randomly selected specimen appearing in the Specimen Image window. The Choose A Specimen pull-down menu provides a selection of specimen images, in addition to the initial randomly chosen one. The histogram of the brightness values in the image is shown in the Histogram window. Clicking on the Variance or Range button applies the corresponding filter. The resulting image and histogram are shown, illustrating the introduction of brightness differences that can be thresholded to select the various regions.

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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