Optimized curve design for image analysis using localized geodesic distance transformations
Billy Braithwaite, Harri Niska, Irene Pöllänen, Tiia Ikonen, Keijo Haataja, Pekka Toivanen, Teemu T. Tolonen · Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE · 2015
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.