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.-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.