Editing Image segmentation (section)

== Applications ==
[[File:3D CT of thorax.jpg|thumb|Volume segmentation of a 3D-rendered [[CT scan]] of the [[thorax]]: The anterior thoracic wall, the airways and the pulmonary vessels anterior to the root of the lung have been digitally removed in order to visualize thoracic contents: <br />– <span style="color:blue;">blue</span>: [[pulmonary arteries]] <br />– <span style="color:red;">red</span>: [[pulmonary veins]] (and also the [[abdominal wall]])<br />– <span style="color:yellow;">yellow</span>: the [[mediastinum]] <br />– <span style="color:violet;">violet</span>: the [[Thoracic diaphragm|diaphragm]] ]]

Some of the practical applications of image segmentation are:

* [[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=18 February 2022 |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>
** Diagnosis, study of anatomical structure<ref>{{cite journal|last1=Kamalakannan|first1=Sridharan|last2=Gururajan|first2=Arunkumar|last3=Sari-Sarraf|first3=Hamed|last4=Rodney|first4=Long|last5=Antani|first5=Sameer|title=Double-Edge Detection of Radiographic Lumbar Vertebrae Images Using Pressurized Open DGVF Snakes|journal=IEEE Transactions on Biomedical Engineering|date=17 February 2010|volume=57|issue=6|pages=1325–1334|doi=10.1109/tbme.2010.2040082|pmid=20172792|s2cid=12766600}}</ref>
** Surgery planning
** Virtual surgery simulation
** Intra-surgery navigation
** Radiotherapy<ref>{{Cite arXiv |last1=Georgescu |first1=Mariana-Iuliana |last2=Ionescu |first2=Radu Tudor |last3=Miron |first3=Andreea-Iuliana |date=21 December 2022 |title=Diversity-Promoting Ensemble for Medical Image Segmentation |class=eess.IV |eprint=2210.12388 }}</ref>
* [[Object detection]]<ref>J. A. Delmerico, P. David and J. J. Corso (2011): "[http://www.jeffdelmerico.com/wp-content/papercite-data/pdf/delmerico2011building.pdf Building façade detection, segmentation and parameter estimation for mobile robot localization and guidance]", International Conference on Intelligent Robots and Systems, pp. 1632–1639.</ref>
** [[Pedestrian detection]]
** [[Face detection]]
** Brake light detection
** Locate objects in satellite images (roads, forests, crops, etc.)
* Recognition Tasks
** [[Face recognition]]
** [[Fingerprint recognition]]
** [[Iris recognition]]
** Prohibited Item at [[Airport security]] checkpoints
* Traffic control systems
* [[Video surveillance]]
*[[Object co-segmentation|Video object co-segmentation and action localization]]<ref name="Liu Wang Hua Zhang 2018 pp. 5840–5853"/><ref name="Wang Duan Zhang Niu p=1657"/>

Several general-purpose [[algorithm]]s and techniques have been developed for image segmentation. To be useful, these techniques must typically be combined with a domain's specific knowledge in order to effectively solve the domain's segmentation problems.