Segmentation of computed tomography image with potential function clustering for assessing body fat

Lixin Zhang; Yu Zhou; Baikun Wan; Min Lin; Yizhong Wang

doi:10.1117/12.576988

8 February 2005 Segmentation of computed tomography image with potential function clustering for assessing body fat

Lixin Zhang, Yu Zhou, Baikun Wan, Min Lin, Yizhong Wang

Proceedings Volume 5637, Electronic Imaging and Multimedia Technology IV; (2005) https://doi.org/10.1117/12.576988
Event: Photonics Asia, 2004, Beijing, China

Abstract

CT scans are thin cross-sectional, radiographic images that can be obtained at any body level. CT images can describe the soft tissues with better clarity because it is more sensitive to slight differences in attenuation than standard radiography. Image segmentation is the key process to identify body fat in CT images. CT images at different body levels have different structures and hence different grayness histogram. Furthermore, the grayness histogram itself, in one CT image, has multiple peaks. Therefore, three segmentation methods, automatic threshold segmentation, morphological reconstruction segmentation, and potential function clustering segmentation, are used in this paper. Body fat contents and distributions are got according to segmented CT images. Experiment results show the effectiveness and stability of the multi-thresholds image segmentation method based on potential function clustering.

Citation Download Citation

Lixin Zhang, Yu Zhou, Baikun Wan, Min Lin, and Yizhong Wang "Segmentation of computed tomography image with potential function clustering for assessing body fat", Proc. SPIE 5637, Electronic Imaging and Multimedia Technology IV, (8 February 2005); https://doi.org/10.1117/12.576988

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