Abstract
This paper studies the problem of simultaneously registering and segmenting a pair of images despite the presence of non-smooth boundaries. We assume one of the images is well segmented by automated algorithm or user interaction. This image acts as an atlas for the segmentation process. For the remaining images in the set, we propose a novel L1 minimization based technique, which leverages the fact that the other image is not closely segmented but has a reasonably ’thin’ boundary around it. The images are allowed to have non-rigid transformations amongst each other. We extend the two image formulation to multiple image registration and segmentation by introducing a low rank prior on the error matrix. We compare against rigid as well as non-rigid registration techniques. We present results on multi-modal real medical data.
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Shah, P., Das Gupta, M. (2012). Simultaneous Registration and Segmentation by L1 Minimization. In: Wang, F., Shen, D., Yan, P., Suzuki, K. (eds) Machine Learning in Medical Imaging. MLMI 2012. Lecture Notes in Computer Science, vol 7588. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-35428-1_16
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DOI: https://doi.org/10.1007/978-3-642-35428-1_16
Publisher Name: Springer, Berlin, Heidelberg
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