Editing Image segmentation (section)

{{Short description|Partitioning a digital image into segments}}
{{Use dmy dates|date=November 2024}}
[[File:Model of a segmented femur - journal.pone.0079004.g005.png|thumb|Model of a segmented left human [[femur]]. It shows the outer surface (red), the surface between compact bone and spongy bone (green) and the surface of the bone marrow (blue).]]

In [[digital image processing]] and [[computer vision]], '''image segmentation''' is the process of partitioning a [[digital image]] into multiple '''image segments''', also known as '''image regions''' or '''image objects''' ([[Set (mathematics)|sets]] of [[pixel]]s). The goal of segmentation is to simplify and/or change the representation of an image into something that is more meaningful and easier to analyze.<ref name="computervision">[[Linda Shapiro|Linda G. Shapiro]] and George C. Stockman (2001):  "Computer Vision", pp 279–325, New Jersey, Prentice-Hall, {{ISBN|0-13-030796-3}}</ref><ref>Barghout, Lauren, and Lawrence W. Lee. "Perceptual information processing system." Paravue Inc. U.S. Patent Application 10/618,543, filed 11 July 2003.</ref> Image segmentation is typically used to locate objects and [[Boundary tracing|boundaries]] (lines, curves, etc.) in images. More precisely, image segmentation is the process of assigning a label to every pixel in an image such that pixels with the same label share certain characteristics.

The result of image segmentation is a set of segments that collectively cover the entire image, or a set of [[Contour line|contour]]s extracted from the image (see [[edge detection]]). Each of the pixels in a region are similar with respect to some characteristic or computed property,<ref>{{cite conference | last1=Nielsen | first1=Frank | last2=Nock | first2=Richard 
 | title=2003 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 2003. Proceedings. |
 chapter=On region merging: The statistical soundness of fast sorting, with applications |
 publisher=IEEE | year=2003 | volume=2 | doi=10.1109/CVPR.2003.1211447 | pages=II:19–26 | isbn=0-7695-1900-8 }}</ref> such as [[color]], [[luminous intensity|intensity]], or [[Image texture|texture]]. Adjacent regions are significantly different with respect to the same characteristic(s).<ref name="computervision" /> When applied to a stack of images, typical in [[medical imaging]], the resulting contours after image segmentation can be used to create [[3D reconstruction]]s with the help of geometry reconstruction algorithms like [[marching cubes]].<ref>Zachow, Stefan, Michael Zilske, and Hans-Christian Hege. "[https://opus4.kobv.de/opus4-zib/files/1044/ZR_07_41.pdf 3D reconstruction of individual anatomy from medical image data: Segmentation and geometry processing]." (2007).</ref>