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| 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]{{Dead link|date=August 2025 |bot=InternetArchiveBot |fix-attempted=yes }}." (2007).</ref>
== Applications ==
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* [[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|url-access=subscription }}</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 | url-access=subscription }}</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|doi-access=free }}</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
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== Graph partitioning methods ==
[[Graph (data structure)|Graph]] partitioning methods are an effective tools for image segmentation since they model the impact of pixel neighborhoods on a given cluster of pixels or pixel, under the assumption of homogeneity in images. In these methods, the image is modeled as a weighted, [[undirected graph]]. Usually a pixel or a group of pixels are associated with [[Vertex (graph theory)|nodes]] and [[Glossary of graph theory#Basics|edge]] weights define the (dis)similarity between the neighborhood pixels. The graph (image) is then partitioned according to a criterion designed to model "good" clusters. Each partition of the nodes (pixels) output from these algorithms are considered an object segment in the image; see [[Segmentation-based object categorization]]. Some popular algorithms of this category are normalized cuts,<ref>Jianbo Shi and [[Jitendra Malik]] (2000): [https://www.cs.cmu.edu/~jshi/papers/pami_ncut.pdf "Normalized Cuts and Image Segmentation"], ''IEEE Transactions on Pattern Analysis and Machine Intelligence'', pp 888–905, Vol. 22, No. 8</ref> [[random walker (computer vision)|random walker]],<ref>Leo Grady (2006): [http://vision.cse.psu.edu/people/chenpingY/paper/grady2006random.pdf "Random Walks for Image Segmentation"], ''IEEE Transactions on Pattern Analysis and Machine Intelligence'', pp. 1768–1783, Vol. 28, No. 11</ref> minimum cut,<ref>Z. Wu and R. Leahy (1993): [ftp://sipi.usc.edu/pub/leahy/pdfs/MAP93.pdf "An optimal graph theoretic approach to data clustering: Theory and its application to image segmentation"]{{
=== Markov random fields ===
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