URE 17. 4 Contrast stretching. The image on the right has gray values between 0 and 63, causing the contrast to look washed out. The image on the right has been contrast enhanced by multiplying the gray levels by four. Point operations Perhaps the simplest image processing operation is that of modifying the values of individual pixels in an image These operations are commonly known as point operations. A point operation might be used to highlight certain regions in an image. Suppose one wished to know where all the pixels in a certain gray level region were spatially located in the image. One would modify all those pixel values to 0 (black) or 255(white)such that the observer could see where they were located Another example of a point operation is contrast enhancement or contrast stretching. The pixel values in a articular image may occupy only a small region of gray level distribution. For instance, the pixels in an image may only take on values between 0 and 63, when they could nominally take on values between 0 and 255. Thi is sometimes caused by the way the image was digitized and/or by the type of transducer used. When this image is examined on a CrT display the contrast looks washed out. A simple point operation that multiplies each pixel value in the image by four will increase the apparent contrast in the image; the new image now has gray values between 0 and 252. This operation is shown in Fig. 17. 4. Possibly the most widely used point operation in medical imaging is pseudo-coloring. In this point operation all the pixels in the image with a particular gray value are assigned a color. Various schemes have been proposed for appropriate pseudo-color tables that assign he gray values to colors. It should be mentioned that point operations are often cascaded, i. e, an image undergoes contrast enhancement and then pseudo-coloring The operations described above can be thought of as operations(or algorithms)that modify the range of the gray levels of the pixels. An important feature that describes a great deal about an image is the histogram of the pixel values. A histogram is a table that lists how many pixels in an image take on a particular gray value. These data are often plotted as a function of the gray value. Point operations are also known as histogram modification or histogram stretching. The contrast enhancement operation shown in Fig. 17.4 modifies the histogram of the resultant image by stretching the gray values from a range of 0-63 to a range of 0-252 Some point operations are such that the resulting histogram of the processed image has a particular shape. a popular rm of histogram modification is known as histogram equalization, whereby the pixels are modified such that ge is almost flat, i. e,, all the pixel values occur equally It is impossible to list all possible types of point operations; however, the important thing to remember is that these operations process one pixel at a time by modifying the pixel based only on its gray level value and nowhere it is distributed spatially(i. e, location in the pixel matrix). These operations are performed to enhance the image, make it easier to see certain structures or regions in the image, or to force a particular shape to the histogram of the image. They are also used as initial operations in a more complicated image processing e 2000 by CRC Press LLC© 2000 by CRC Press LLC Point Operations Perhaps the simplest image processing operation is that of modifying the values of individual pixels in an image. These operations are commonly known as point operations. A point operation might be used to highlight certain regions in an image. Suppose one wished to know where all the pixels in a certain gray level region were spatially located in the image. One would modify all those pixel values to 0 (black) or 255 (white) such that the observer could see where they were located. Another example of a point operation is contrast enhancement or contrast stretching. The pixel values in a particular image may occupy only a small region of gray level distribution. For instance, the pixels in an image may only take on values between 0 and 63, when they could nominally take on values between 0 and 255. This is sometimes caused by the way the image was digitized and/or by the type of transducer used. When this image is examined on a CRT display the contrast looks washed out. A simple point operation that multiplies each pixel value in the image by four will increase the apparent contrast in the image; the new image now has gray values between 0 and 252. This operation is shown in Fig. 17.4. Possibly the most widely used point operation in medical imaging is pseudo-coloring. In this point operation all the pixels in the image with a particular gray value are assigned a color. Various schemes have been proposed for appropriate pseudo-color tables that assign the gray values to colors. It should be mentioned that point operations are often cascaded, i.e., an image undergoes contrast enhancement and then pseudo-coloring. The operations described above can be thought of as operations (or algorithms) that modify the range of the gray levels of the pixels. An important feature that describes a great deal about an image is the histogram of the pixel values. A histogram is a table that lists how many pixels in an image take on a particular gray value. These data are often plotted as a function of the gray value. Point operations are also known as histogram modification or histogram stretching. The contrast enhancement operation shown in Fig. 17.4 modifies the histogram of the resultant image by stretching the gray values from a range of 0–63 to a range of 0–252. Some point operations are such that the resulting histogram of the processed image has a particular shape. A popular form of histogram modification is known as histogram equalization, whereby the pixels are modified such that the histogram of the processed image is almost flat, i.e., all the pixel values occur equally. It is impossible to list all possible types of point operations; however, the important thing to remember is that these operations process one pixel at a time by modifying the pixel based only on its gray level value and not where it is distributed spatially (i.e., location in the pixel matrix). These operations are performed to enhance the image, make it easier to see certain structures or regions in the image, or to force a particular shape to the histogram of the image. They are also used as initial operations in a more complicated image processing algorithm. FIGURE 17.4 Contrast stretching. The image on the right has gray values between 0 and 63, causing the contrast to look washed out. The image on the right has been contrast enhanced by multiplying the gray levels by four