An improved 3D edge surface tracking algorithm based on 3D fractional-order differentiation within confocal microscopy images

Yu Ma, Yanning Zhang, Lisheng Wang

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

Abstract

Fractional-order differentiation enhances the image nonlinearly, but only has been applied in the 2D image. The 2D fractional differentiation operator is extended to 3D and the 3D fractional differentiation discrete filtering masks are deduced. The 3D fractional differentiation is implemented to improve the traditional 3D image edge surface tracking algorithm in two aspects. Firstly, the 3D data fields of neuron slices are enhanced by the 3D fractional differentiation, ensures more detailed structures of edge surfaces with low contrast are extracted. Then integral-order gradient is modified by the fractional differentiation to get more 3D detailed structures. The proposed method has been applied to 3D confocal microscopy images, and more 3D detail structures of neuron are tracked compared to the traditional 3D edge surface tracking algorithm.

Original languageEnglish
Title of host publicationIntelligence Science and Big Data Engineering - 4th International Conference, IScIDE 2013, Revised Selected Papers
PublisherSpringer Verlag
Pages497-504
Number of pages8
ISBN (Print)9783642420566
DOIs
StatePublished - 2013
Event4th International Conference on Intelligence Science and Big Data Engineering, IScIDE 2013 - Beijing, China
Duration: 31 Jul 20132 Aug 2013

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume8261 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Conference

Conference4th International Conference on Intelligence Science and Big Data Engineering, IScIDE 2013
Country/TerritoryChina
CityBeijing
Period31/07/132/08/13

Keywords

  • 3D edge surface tracking algorithm
  • Confocal microscopy image
  • Fractional-order differentiation
  • Image enhancement
  • Neuron

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