Liver segmentation based on deformable registration and multi-layer segmentation
Hossein Badakhshannoory, Parvaneh Saeedi, Karim A. Qayumi · 2010
This paper describes a semi-automatic algorithm for extracting liver masks of CT scan volumes. The proposed method relies on two types of information: liver's shape and its intensity characteristics. Here the liver shape information is retained by measuring the shape similarities between consecutive slices of the liver's CT scans. This is done through a deformable registration scheme. The liver intensity is utilized by a multi-layer image segmentation algorithm that emphasizes on the true boundaries of the liver. The proposed algorithm is tested for MICCAI 2007 grand challenge workshop dataset. The average results for volumetric overlap error and relative volume difference is 11.12% and 2.21% respectively.