Image Segmentation With Background Correction Using A Multiplicative Smoothing-Spline Model

This paper presents an image-segmentation method which compensates multiplicative distortions based on smooth regularity assumptions. In this work, we generalize the original Chan-Vese functional to handle a continuous multiplicative bias. In the derivation of our model, we show that the optimal correction function is necessarily a spline, which we express in terms of discrete coefficients. Following an iterative technique, we propose to find the solution by an alternate optimization of this map and of the segmented domains. In order to maximize the overall efficiency, graph cuts are combined with a specifically designed multigrid algorithm. Our experiments demonstrate the relevance of our approach for biomedical data.


Published in:
2012 9Th Ieee International Symposium On Biomedical Imaging (Isbi), 186-189
Presented at:
9th IEEE International Symposium on Biomedical Imaging (ISBI) - From Nano to Macro, Barcelona, SPAIN, MAY 02-05, 2012
Year:
2012
Publisher:
New York, Ieee
ISBN:
978-1-4577-1858-8
Keywords:
Laboratories:




 Record created 2013-03-28, last modified 2018-03-17

External links:
Download fulltextURL
Download fulltextURL
Download fulltextURL
Rate this document:

Rate this document:
1
2
3
 
(Not yet reviewed)