Iterative Segmentation of Autofluorescence Images with Background Correction
Ramtin Madani

Seminar • 10 October 2011 • BM 4.233

Abstract
We propose an automated method for high-quality segmentation of linear backgrounds in noisy fluorescence-microscopy data. In our approach, the segmented domains of the input image are determined through graph cuts. In order to properly compensate for the varying background intensities, this estimation is performed iteratively, using interpolated versions of the current image decomposition as input reference. Each interpolation task involves the minimization a weighted smoothing-spline functional, which is done using an efficient multigrid approach. The performance of our resulting technique as compared to the state of the art is assessed through synthetic and real experime