Editing Image segmentation (section)

== Thresholding ==
{{Main articles|Thresholding (image processing)}}
The simplest method of image segmentation is called the [[Thresholding (image processing)|thresholding]] method. This method is based on a clip-level (or a threshold value) to turn a gray-scale image into a binary image.

The key of this method is to select the threshold value (or values when multiple-levels are selected). Several popular methods are used in industry including the maximum entropy method, [[balanced histogram thresholding]], [[Otsu's method]] (maximum variance), and [[k-means clustering]].

Recently, methods have been developed for thresholding computed tomography (CT) images. The key idea is that, unlike Otsu's method, the thresholds are derived from the radiographs instead of the (reconstructed) image.<ref>{{cite journal |last1 = Batenburg |first1 = K J. |last2 = Sijbers |first2 = J. |year =  2009|title = Adaptive thresholding of tomograms by projection distance minimization |journal = Pattern Recognition |volume = 42 |issue = 10 |pages = 2297–2305 |doi = 10.1016/j.patcog.2008.11.027 |bibcode = 2009PatRe..42.2297B |citeseerx = 10.1.1.182.8483 }}</ref><ref>{{cite journal |first1 = K J. |last1 = Batenburg |first2 = J. |last2 = Sijbers |title = Optimal Threshold Selection for Tomogram Segmentation by Projection Distance Minimization |journal = IEEE Transactions on Medical Imaging |volume = 28 |issue = 5 |pages = 676–686 |date = June 2009 |url = http://www.visielab.ua.ac.be/publications/optimal-threshold-selection-tomogram-segmentation-projection-distance-minimization |format = PDF |doi = 10.1109/tmi.2008.2010437 |pmid = 19272989 |s2cid = 10994501 |access-date = 31 July 2012 |archive-url = https://web.archive.org/web/20130503171943/http://www.visielab.ua.ac.be/publications/optimal-threshold-selection-tomogram-segmentation-projection-distance-minimization |archive-date = 3 May 2013  }}</ref>

New methods suggest the use of multi-dimensional, fuzzy rule-based, non-linear thresholds. In these approaches, the decision regarding each pixel's membership in a segment is based on multi-dimensional rules derived from fuzzy logic and evolutionary algorithms, considering factors such as image lighting, environment, and application.<ref>{{cite book |first1 = A. |last1 = Kashanipour |first2 = N |last2 = Milani |first3 = A. |last3 = Kashanipour |first4 = H. |last4 = Eghrary |title = 2008 Congress on Image and Signal Processing |chapter = Robust Color Classification Using Fuzzy Rule-Based Particle Swarm Optimization |publisher = IEEE Congress on Image and Signal Processing |volume = 2 |pages = 110–114 |date = May 2008 |doi = 10.1109/CISP.2008.770 |isbn = 978-0-7695-3119-9 |s2cid = 8422475 }}</ref>