Thresholding is the simplest method of image segmentation. From a grayscale
image, thresholding can be used to create binary images.
Definition The simplest thresholding methods
replace each pixel in an image with a black pixel if the image intensity is
less than some fixed constant T, or a white pixel if the image intensity is
greater than that constant. In the example image on the right, this results
in the dark tree becoming completely black, and the white snow becoming
complete white. Categorizing thresholding Methods
To make thresholding completely automated, it is necessary for the
computer to automatically select the threshold T. Sezgin and Sankur
categorize thresholding methods into the following six groups based on the
information the algorithm manipulates: Histogram shape-based methods, where,
for example, the peaks, valleys and curvatures of the smoothed histogram are
analyzed Clustering-based methods, where the
gray-level samples are clustered in two parts as background and foreground, or
alternately are modeled as a mixture of two Gaussians
Entropy-based methods result in algorithms that use the entropy of the
foreground and background regions, the cross-entropy between the original and
binarized image, etc. Object Attribute-based methods search a
measure of similarity between the gray-level and the binarized images,
such as fuzzy shape similarity, edge coincidence, etc.
Spatial methods [that] use higher-order probability distribution and/or
correlation between pixels Local methods adapt the threshold value
on each pixel to the local image characteristics. In these methods, a
different T is selected for each pixel in the image.
Multiband thresholding Colour images can also be thresholded.
One approach is to designate a separate threshold for each of the RGB components
of the image and then combine them with an AND operation. This reflects the way
the camera works and how the data is stored in the computer, but it does not
correspond to the way that people recognize colour. Therefore, the HSL and
HSV colour models are more often used; note that since hue is a circular
quantity it requires circular thresholding. It is also possible to use
the CMYK colour model. Probability distributions
Histogram shape-based methods in particular, but also many other
thresholding algorithms, make certain assumptions about the image intensity
probability distribution. The most common thresholding methods work on
bimodal distributions, but algorithms have also been developed for unimodal
distributions, multimodal distributions, and circular distributions.
See also Otsu's Method
Balanced histogram thresholding Citations
Pham N, Morrison A, Schwock J et al.. Quantitative image analysis of
immunohistochemical stains using a CMYK color model. Diagn Pathol. 2:8.
Shapiro, Linda G. & Stockman, George C.. "Computer Vision". Prentice Hall. ISBN
0-13-030796-3 Mehmet Sezgin and Bulent Sankur, Survey
over image thresholding techniques and quantitative performance evaluation,
Journal of Electronic Imaging 13(1), 146–165. doi:10.1117/1.1631315
References and further reading Gonzalez, Rafael C. & Woods, Richard E..
Thresholding. In Digital Image Processing, pp. 595–611. Pearson
Education. ISBN 81-7808-629-8 M. Luessi, M. Eichmann, G. M. Schuster,
and A. K. Katsaggelos, Framework for efficient optimal multilevel image
thresholding, Journal of Electronic Imaging, vol. 18, pp. 013004+, 2009.
doi:10.1117/1.3073891 Y.K. Lai, P.L. Rosin, Efficient Circular
Thresholding, IEEE Trans. on Image Processing 23(3), pp. 992–1001.
doi:10.1109/TIP.2013.2297014 References