Abstract
Recently a growing interest has been seen in minimally invasive treatments with open configuration magnetic resonance (Open-MR) scanners. Because of the lower magnetic field (0.5T), the contrast of Open-MR images is very low. In this paper, we address the problem of liver segmentation from low-contrast Open-MR images. The proposed segmentation method consists of two steps. In the first step, we use K-means clustering and a priori knowledge to find and identify liver and non-liver index pixels, which are used as “object” and “background” seeds, respectively, for graph-cut. In the second step, a graph-cut based method is used to segment the liver from the low-contrast Open MR images. The main contribution of this paper is that the object (liver) and background (non-liver) seeds (regions) in every low-contrast slice of the volume can be obtained automatically by K-means clustering without user interaction.
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Chen, YW., Tsubokawa, K., Foruzan, A.H. (2010). Liver Segmentation from Low Contrast Open MR Scans Using K-Means Clustering and Graph-Cuts. In: Zhang, L., Lu, BL., Kwok, J. (eds) Advances in Neural Networks - ISNN 2010. ISNN 2010. Lecture Notes in Computer Science, vol 6064. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-13318-3_21
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DOI: https://doi.org/10.1007/978-3-642-13318-3_21
Publisher Name: Springer, Berlin, Heidelberg
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