Image segmentation: Difference between revisions

Revision as of 05:26, 11 December 2023

Revision as of 21:20, 14 December 2023

Line 4:

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 July 11, 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 color 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>

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 color 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>

== Applications ==

Line 17 ⟶ 16:

* [[Content-based image retrieval]]<ref>Belongie, Serge, et al. "[http://people.eecs.berkeley.edu/~malik/papers/blobworld98.pdf Color-and texture-based image segmentation using EM and its application to content-based image retrieval]." Sixth International Conference on Computer Vision (IEEE Cat. No. 98CH36271). IEEE, 1998.</ref>

* [[Machine vision]]

* [[Medical imaging]],<ref>{{cite journal | last1 = Pham | first1 = Dzung L. | last2 = Xu | first2 = Chenyang | last3 = Prince | first3 = Jerry L. | year = 2000 | title = Current Methods in Medical Image Segmentation | journal = Annual Review of Biomedical Engineering | volume = 2 | pages = 315–337 | pmid = 11701515 | doi = 10.1146/annurev.bioeng.2.1.315 }}</ref><ref>{{cite journal | last1 = Forghani| first1 = M. | last2 = Forouzanfar | first2 = M.| last3 = Teshnehlab| first3 = M. | year = 2010 | title = Parameter optimization of improved fuzzy c-means clustering algorithm for brain MR image segmentation | journal = Engineering Applications of Artificial Intelligence | volume = 23 | issue = 2 | pages = 160–168 | doi = 10.1016/j.engappai.2009.10.002 }}</ref>, and imaging studies in biomedical research, including [[volume rendering|volume rendered]] images from [[CT scan|computed tomography]], [[magnetic resonance imaging]], as well as volume electron microscopy techniques such as FIB-SEM .<ref>{{Cite journal |last1=Reznikov |first1=Natalie |last2=Buss |first2=Dan J. |last3=Provencher |first3=Benjamin |last4=McKee |first4=Marc D. |last5=Piché |first5=Nicolas |date=October 2020 |title=Deep learning for 3D imaging and image analysis in biomineralization research |url=http://dx.doi.org/10.1016/j.jsb.2020.107598 |journal=Journal of Structural Biology |volume=212 |issue=1 |pages=107598 |doi=10.1016/j.jsb.2020.107598 |pmid=32783967 |s2cid=221126896 |issn=1047-8477}}</ref> .

** Locate tumors and other pathologies<ref>{{cite journal | url=https://link.springer.com/article/10.1007/s11548-013-0922-7 | doi=10.1007/s11548-013-0922-7 | title=Brain tumor detection and segmentation in a CRF (Conditional random fields) framework with pixel-pairwise affinity and superpixel-level features | year=2014 | last1=Wu | first1=Wei | last2=Chen | first2=Albert Y. C. | last3=Zhao | first3=Liang | last4=Corso | first4=Jason J. | journal=International Journal of Computer Assisted Radiology and Surgery | volume=9 | issue=2 | pages=241–253 | pmid=23860630 | s2cid=13474403 }}</ref><ref>E. B. George and M. Karnan (2012): "[http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.411.7411&rep=rep1&type=pdf MR Brain image segmentation using Bacteria Foraging Optimization Algorithm]", ''International Journal of Engineering and Technology'', Vol. 4.</ref>

** Measure tissue volumes<ref>{{Cite journal |last1=Ye |first1=Run Zhou |last2=Noll |first2=Christophe |last3=Richard |first3=Gabriel |last4=Lepage |first4=Martin |last5=Turcotte |first5=Éric E. |last6=Carpentier |first6=André C. |date=February 2022 |title=DeepImageTranslator: A free, user-friendly graphical interface for image translation using deep-learning and its applications in 3D CT image analysis |journal=SLAS Technology |volume=27 |issue=1 |pages=76–84 |doi=10.1016/j.slast.2021.10.014 |pmid=35058205 |issn=2472-6303|doi-access=free }}</ref><ref>{{Cite journal |last1=Ye |first1=En Zhou |last2=Ye |first2=En Hui |last3=Bouthillier |first3=Maxime |last4=Ye |first4=Run Zhou |date=2022-02-18 |title=DeepImageTranslator V2: analysis of multimodal medical images using semantic segmentation maps generated through deep learning |language=en |biorxiv=10.1101/2021.10.12.464160v2 |doi=10.1101/2021.10.12.464160 |s2cid=239012446}}</ref>