ICONIP'12 Proceedings of the 19th international conference on Neural Information Processing - Volume Part IV
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In this paper, we proposed an automatic lung segmentation method. We designed a ROI based method to estimate a proper initial lung boundary for ASM deformation by deriving the translation and the scaling parameters from the lung ROI. An adaptive ASM, using k-means clustering and silhouette-based cluster validation technique, was proposed to adapt to the lung shape change so that the lung shape variation among people can be overwhelmed. The experiments indicated that the segmentation performance of the adaptive ASM is superior to the traditional ASM approaches.