Image segmentation: Difference between revisions

Revision as of 20:24, 16 August 2023

Revision as of 09:47, 7 September 2023

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* [[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> including [[volume rendering|volume rendered]] images from [[CT scan|computed tomography]] and [[magnetic resonance imaging]].

** 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 |lastlast1=Ye |firstfirst1=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>

** 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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Recently, methods have been developed for thresholding computed tomography (CT) images. The key idea is that, unlike Otsu's method, the thresholds are derived from the radiographs instead of the (reconstructed) image.<ref>{{cite journal |last1 = Batenburg |first1 = K J. |last2 = Sijbers |first2 = J. |year = 2009|title = Adaptive thresholding of tomograms by projection distance minimization |journal = Pattern Recognition |volume = 42 |issue = 10 |pages = 2297–2305 |doi = 10.1016/j.patcog.2008.11.027 |bibcode = 2009PatRe..42.2297B |citeseerx = 10.1.1.182.8483 }}</ref><ref>{{cite journal |first1 = K J. |last1 = Batenburg |first2 = J. |last2 = Sijbers |title = Optimal Threshold Selection for Tomogram Segmentation by Projection Distance Minimization |journal = IEEE Transactions on Medical Imaging |volume = 28 |issue = 5 |pages = 676–686 |date = June 2009 |url = http://www.visielab.ua.ac.be/publications/optimal-threshold-selection-tomogram-segmentation-projection-distance-minimization |format = PDF |doi = 10.1109/tmi.2008.2010437 |pmid = 19272989 |s2cid = 10994501 |access-date = 2012-07-31 |archive-url = https://web.archive.org/web/20130503171943/http://www.visielab.ua.ac.be/publications/optimal-threshold-selection-tomogram-segmentation-projection-distance-minimization |archive-date = 2013-05-03 |url-status = dead }}</ref>

New methods suggested the usage of multi-dimensional fuzzy rule-based non-linear thresholds. In these works decision over each pixel's membership to a segment is based on multi-dimensional rules derived from fuzzy logic and evolutionary algorithms based on image lighting environment and application.<ref>{{cite book |first1 = A. |last1 = Kashanipour |first2 = N |last2 = Milani |first3 = A. |last3 = Kashanipour |first4 = H. |last4 = Eghrary |title = 2008 Congress on Image and Signal Processing |chapter = Robust Color Classification Using Fuzzy Rule-Based Particle Swarm Optimization |journalpublisher = IEEE Congress on Image and Signal Processing |volume = 2 |pages = 110–114 |date = May 2008 |doi = 10.1109/CISP.2008.770 |isbn = 978-0-7695-3119-9 |s2cid = 8422475 }}</ref>

== Clustering methods ==