16 March 2015 Optimized curve design for image analysis using localized geodesic distance transformations
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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.
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Billy Braithwaite, Billy Braithwaite, Harri Niska, Harri Niska, Irene Pöllänen, Irene Pöllänen, Tiia Ikonen, Tiia Ikonen, Keijo Haataja, Keijo Haataja, Pekka Toivanen, Pekka Toivanen, Teemu Tolonen, 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; https://doi.org/10.1117/12.2077826
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