Binary image

Figure 2. An example of typical computer print out showing (a) the histogram of pixel frequency versus intensity with values of maximum and minimum intensity (b) adjusted raw data image (c) binary image and (d) calculated stereological parameters.

Fig. 47.2 TEM image of sample A (left) and the digitally inverted binary image (right)...

Figure 17.6 shows the results for a typical composite image of the entire flame in which the image intensity, or gray scale value, is plotted on the vertical axis against radial and axial distance coordinates. To find the flame area, a threshold intensity is first picked and a binary image is created with all pixels with intensities above the threshold set to 1 (white) and the rest set to 0 (black). The number of white pixels is then proportional to the surface area of the flame at that intensity contour. This process is then repeated to build up a plot of flame area versus a normalized threshold number (intensity value/256) such as that shown in Fig. 17.7a. 

Figure 1. Binary image of a Pb crystal of 7 mm of diameter supported by a vitrous carbon plate.

Figure 2.9 The figure is a binary image in which the caffeine domains created using particle statistics are visualized. Other sample components are set to zero, so only the caffeine domains are apparent.

To achieve that, an object reconstruction algorithm detects connected components (blobs) in the binary images of each material class. In a first step, blobs that do not meet pre-defined size restrictions are considered as classification errors and filtered out (e.g. the black line due to a faulty camera pixel in object D). The binary image of each material class (material and overlay) is then morphologically dilated... 

Image segmentation Create and view binary images created from image thresholding... 

Figure 3 A binary image obtained by edge detection and...

An object in a binary image (two grey level) consists of a string which has n consecutive ones separated from other groups of ones by at least n zeros on either side. The parameter, n, is chosen as the optimal choice between the width of a chromosome and the spacing between two chromosomes in a metaphase cell under this magnification. Thus, for example, the string... 

The values for features in both grey and binary images are related to whole values and to other parameters in recognition, a key factor is the effectiveness or otherwise of the features and values selected—as these decide its precision. 

A basic treatment of bio-images can be proposed. Its main objective is to reduce the total amount of information contained in the gray-level or color images to focus the quantification on the key features the result of this treatment will be a binary image containing only the silhouettes of the cells of interest (their pixel values are set to 1) and the background (its pixel values are set to 0). The size and shape measurements will be later conducted on these silhouettes, and the densitometric or color analysis will use them as masks to consider only the regions of interest. 

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