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.
In this paper, a new method of body fat and its distribution testing is proposed based on CT image processing. As it is more sensitive to slight differences in attenuation than standard radiography, CT depicts the soft tissues with better clarity. And body fat has a distinct grayness range compared with its neighboring tissues in a CT image. An effective multi-thresholds image segmentation method based on potential function clustering is used to deal with multiple peaks in the grayness histogram of a CT image. The CT images of abdomens of 14 volunteers with different fatness are processed with the proposed method. Not only can the result of total fat area be got, but also the differentiation of subcutaneous fat from intra-abdominal fat has been identified. The results show the adaptability and stability of the proposed method, which will be a useful tool for diagnosing obesity.