Automatic organ segmentation on torso CT images by using content- based image retrieval
2012
This paper presents a fast and robust segmentation scheme that automatically identifies and extracts a massive-organ
region on torso CT images. In contrast to the conventional algorithms that are designed empirically for segmenting a
specific organ based on traditional image processing techniques, the proposed scheme uses a fully data-driven approach
to accomplish a universal solution for segmenting the different massive-organ regions on CT images. Our scheme
includes three processing steps: machine-learning-based organ localization, content-based image (reference) retrieval,
and atlas-based organ segmentation techniques. We applied this scheme to automatic segmentations of heart, liver,
spleen, left and right kidney regions on non-contrast CT images respectively, which are still difficult tasks for traditional
segmentation algorithms. The segmentation results of these organs are compared with the ground truth that manually
identified by a medical expert. The Jaccard similarity coefficient between the ground truth and automated segmentation
result centered on 67% for heart, 81% for liver, 78% for spleen, 75% for left kidney, and 77% for right kidney. The
usefulness of our proposed scheme was confirmed.
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