Abstract
Image segmentation plays an important role in many medical imaging systems, yet in complex circumstances it is still a challenging problem. Among many difficulties, problem caused by the image intensity inhomogeneity is the key aspect. In this work, we develop a novel local-homogeneous region-based level set segmentation method to tackle this problem. First, we propose a novel local order energy, which interprets the local intensity constraint. And then, we integrate this energy into the objective energy function. After that, we minimize the energy function via a level set evolution process. Extensive experiments are performed to evaluate the proposed approach, showing significant improvements in both accuracy and efficiency, as compared to the state-of-the-art.
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Wang, L., Yu, Z., Pan, C. (2011). Medical Image Segmentation Based on Novel Local Order Energy. In: Kimmel, R., Klette, R., Sugimoto, A. (eds) Computer Vision – ACCV 2010. ACCV 2010. Lecture Notes in Computer Science, vol 6493. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-19309-5_12
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DOI: https://doi.org/10.1007/978-3-642-19309-5_12
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-19308-8
Online ISBN: 978-3-642-19309-5
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