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 (sets of pixels). 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.[1][2] Image segmentation is typically used to locate objects and 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 contours extracted from the image (see edge detection). Each of the pixels in a region are similar with respect to some characteristic or computed property,[3] such as color, intensity, or texture. Adjacent regions are significantly different with respect to the same characteristic(s).[1] When applied to a stack of images, typical in medical imaging, the resulting contours after image segmentation can be used to create 3D reconstructions with the help of geometry reconstruction algorithms like marching cubes.[4]
- ^ a b Linda G. Shapiro and George C. Stockman (2001): "Computer Vision", pp 279–325, New Jersey, Prentice-Hall, ISBN 0-13-030796-3
- ^ Barghout, Lauren, and Lawrence W. Lee. "Perceptual information processing system." Paravue Inc. U.S. Patent Application 10/618,543, filed 11 July 2003.
- ^ Nielsen, Frank; Nock, Richard (2003). "On region merging: The statistical soundness of fast sorting, with applications". 2003 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 2003. Proceedings. Vol. 2. IEEE. pp. II:19–26. doi:10.1109/CVPR.2003.1211447. ISBN 0-7695-1900-8.
- ^ Zachow, Stefan, Michael Zilske, and Hans-Christian Hege. "3D reconstruction of individual anatomy from medical image data: Segmentation and geometry processing." (2007).