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Proceedings Article

Optimized curve design for image analysis using localized geodesic distance transformations

[+] Author Affiliations
Billy Braithwaite, Harri Niska, Irene Pöllänen, Tiia Ikonen, Keijo Haataja, Pekka Toivanen

Univ. of Eastern Finland (Finland)

Teemu Tolonen

Univ. of Tampere (Finland)

Proc. SPIE 9399, Image Processing: Algorithms and Systems XIII, 939903 (March 16, 2015); doi:10.1117/12.2077826
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From Conference Volume 9399

  • Image Processing: Algorithms and Systems XIII
  • Karen O. Egiazarian; Sos S. Agaian; Atanas P. Gotchev
  • San Francisco, California, United States | February 08, 2015

abstract

We consider geodesic distance transformations for digital images. Given a M × N digital image, a distance image is produced by evaluating local pixel distances. Distance Transformation on Curved Space (DTOCS) evaluates shortest geodesics of a given pixel neighborhood by evaluating the height displacements between pixels. In this paper, we propose an optimization framework for geodesic distance transformations in a pattern recognition scheme, yielding more accurate machine learning based image analysis, exemplifying initial experiments using complex breast cancer images. Furthermore, we will outline future research work, which will complete the research work done for this paper. © (2015) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
Citation

Billy Braithwaite ; Harri Niska ; Irene Pöllänen ; Tiia Ikonen ; Keijo Haataja, et al.
" Optimized curve design for image analysis using localized geodesic distance transformations ", Proc. SPIE 9399, Image Processing: Algorithms and Systems XIII, 939903 (March 16, 2015); doi:10.1117/12.2077826; http://dx.doi.org/10.1117/12.2077826


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