Description: Abstract—In this study, we propose a novel approach for accurate
3-D organ segmentation in the CT scan volumes. Instead of
using the organ’s prior information directly in the segmentation
process, here we utilize the knowledge of the organ to validate a
large number of potential segmentation outcomes that are generated
by a generic segmentation process. For this, an organ space
is generated based on the principal component analysis approach
using which the fidelity of each segment to the organ is measured.
We detail applications of the proposed method for the 3-D segmentation
of human kidney and liver in computed tomography scan
volumes. For evaluation, the public database of theMICCAI’s 2007
grand challenge workshop has been incorporated. Implementation
results show an average Dice similarity measure of 0.90 for the segmentation
of the kidney. For the liver segmentation, the proposed
algorithm achieves an average volume overlap error of 8.7 and
an average surface distance of 1.51 mm.
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