16 March 2015 Optimized curve design for image analysis using localized geodesic distance transformations
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Proceedings Volume 9399, Image Processing: Algorithms and Systems XIII; 939903 (2015); doi: 10.1117/12.2077826
Event: SPIE/IS&T Electronic Imaging, 2015, San Francisco, California, United States
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.
Billy Braithwaite, Harri Niska, Irene Pöllänen, Tiia Ikonen, Keijo Haataja, Pekka Toivanen, Teemu Tolonen, "Optimized curve design for image analysis using localized geodesic distance transformations", Proc. SPIE 9399, Image Processing: Algorithms and Systems XIII, 939903 (16 March 2015); doi: 10.1117/12.2077826; http://dx.doi.org/10.1117/12.2077826

Image analysis

Image segmentation

Digital imaging

Medical imaging


Pattern recognition

Image processing

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