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Proceedings Paper

Optimized curve design for image analysis using localized geodesic distance transformations
Author(s): Billy Braithwaite; Harri Niska; Irene Pöllänen; Tiia Ikonen; Keijo Haataja; Pekka Toivanen; Teemu Tolonen
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Paper 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.

Paper Details

Date Published: 16 March 2015
PDF: 11 pages
Proc. SPIE 9399, Image Processing: Algorithms and Systems XIII, 939903 (16 March 2015); doi: 10.1117/12.2077826
Show Author Affiliations
Billy Braithwaite, Univ. of Eastern Finland (Finland)
Harri Niska, Univ. of Eastern Finland (Finland)
Irene Pöllänen, Univ. of Eastern Finland (Finland)
Tiia Ikonen, Univ. of Eastern Finland (Finland)
Keijo Haataja, Univ. of Eastern Finland (Finland)
Pekka Toivanen, Univ. of Eastern Finland (Finland)
Teemu Tolonen, Univ. of Tampere (Finland)


Published in SPIE Proceedings Vol. 9399:
Image Processing: Algorithms and Systems XIII
Karen O. Egiazarian; Sos S. Agaian; Atanas P. Gotchev, Editor(s)

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