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14 February 2012 Automatic organ segmentation on torso CT images by using content-based image retrieval
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Abstract
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.
© (2012) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
Xiangrong Zhou, Atsuto Watanabe, Xinxin Zhou, Takeshi Hara, Ryujiro Yokoyama, Masayuki Kanematsu, and Hiroshi Fujita "Automatic organ segmentation on torso CT images by using content-based image retrieval", Proc. SPIE 8314, Medical Imaging 2012: Image Processing, 83143E (14 February 2012); https://doi.org/10.1117/12.912359
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