Change search
CiteExportLink to record
Permanent link

Direct link
Cite
Citation style
  • apa
  • harvard1
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • Other style
More styles
Language
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Other locale
More languages
Output format
  • html
  • text
  • asciidoc
  • rtf
A modified fuzzy C means algorithm for shading correction in craniofacial CBCT images
KTH. Halmstad University, Sweden.
KTH, School of Engineering Sciences (SCI), Mathematics (Dept.), Mathematics (Div.). Elekta Instrument AB, Sweden.
2017 (English)In: IFMBE Proceedings, Springer Verlag , 2017, 531-538 p.Conference paper (Refereed)
Abstract [en]

CBCT images suffer from acute shading artifacts primarily due to scatter. Numerous image-domain correction algorithms have been proposed in the literature that use patient-specific planning CT images to estimate shading contributions in CBCT images. However, in the context of radiosurgery applications such as gamma knife, planning images are often acquired through MRI which impedes the use of polynomial fitting approaches for shading correction. We present a new shading correction approach that is independent of planning CT images. Our algorithm is based on the assumption that true CBCT images follow a uniform volumetric intensity distribution per material, and scatter perturbs this uniform texture by contributing cupping and shading artifacts in the image domain. The framework is a combination of fuzzy C-means coupled with a neighborhood regularization term and Otsu’s method. Experimental results on artificially simulated craniofacial CBCT images are provided to demonstrate the effectiveness of our algorithm. Spatial non-uniformity is reduced from 16% to 7% in soft tissue and from 44% to 8% in bone regions. With shading- correction, thresholding based segmentation accuracy for bone pixels is improved from 85% to 91% when compared to thresholding without shading-correction. The proposed algorithm is thus practical and qualifies as a p lug and p lay extension into any CBCT reconstruction software for shading correction.

Place, publisher, year, edition, pages
Springer Verlag , 2017. 531-538 p.
Keyword [en]
Cone beam CT, Fuzzy C means, Shading correction, Biochemical engineering, Bone, Clustering algorithms, Copying, Fuzzy clustering, Fuzzy systems, Magnetic resonance imaging, Volumetric analysis, Cone-beam CT, Correction algorithms, Correction approaches, Fuzzy C mean, Fuzzy C-means algorithms, Intensity distribution, Reconstruction software, Segmentation accuracy, Computerized tomography
National Category
Mathematics
Identifiers
URN: urn:nbn:se:kth:diva-207385DOI: 10.1007/978-981-10-4166-2_81ScopusID: 2-s2.0-85016072022ISBN: 9789811041655 OAI: oai:DiVA.org:kth-207385DiVA: diva2:1107474
Conference
International Conference on Medical and Biological Engineering, CMBEBIH 2017, 16 March 2017 through 18 March 2017
Note

QC 20170609

Available from: 2017-06-09 Created: 2017-06-09 Last updated: 2017-06-09Bibliographically approved

Open Access in DiVA

No full text

Other links

Publisher's full textScopus

Search in DiVA

By author/editor
Ashfaq, AwaisAdler, Jonas
By organisation
KTHMathematics (Div.)
Mathematics

Search outside of DiVA

GoogleGoogle Scholar

Altmetric score

Total: 11 hits
CiteExportLink to record
Permanent link

Direct link
Cite
Citation style
  • apa
  • harvard1
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • Other style
More styles
Language
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Other locale
More languages
Output format
  • html
  • text
  • asciidoc
  • rtf