| Summary: | Measuring human subcutaneous fat is useful for assessing health risks due to obesity and for monitoring athletes’ health status, body shapes and weight for various sports competitions such as gymnastics and wrestling. Our aim is to investigate the use of ultrasound imaging in automatically measuring human subcutaneous fat thickness. We proposed to use the spectrum properties extracted from the raw radio frequency (RF) signals of ultrasound for the purpose of fat boundary detection. Our fat detection framework consists of four main steps. The first step is capturing RF data from 11 beam steering angles and at four focal positions. Secondly, two spectrum properties (spectrum variance and integrated backscatter coefficient) are calculated from the local spectrum of RF data using the short time Fourier transform and moment analysis. The values of the spectrum properties are encoded as gray-scale parametric images. Thirdly, spatial compounding is used to reduce speckle noise in the parametric images and improve the visualization of the subcutaneous fat layer. Finally, we apply Rosin’s thresholding and Random Sample Consensus boundary detection on the parametric images to extract the fat boundary. The detection framework was tested on 36 samples obtained at the suprailiac, thigh and triceps of nine human participants in vivo. When compared to manual boundary detection on ultrasound images, the best result was obtained from segmenting the spatial compounded spectrum variance values averaged over multiple focuses. A reasonable result could also be obtained by using a single focus. Further, our automatic detection results were compared with the results using skinfold caliper measurements. We found that the correlation is high between our automatic detection and skinfold caliper measurement, and is similar to the previous studies which are not automatic. Our work has shown that the spatial compounded spectrum properties of RF data can be used to segment the subcutaneous fat layer. Based on our results, it is feasible to detect fat at the suprailiac, thigh and triceps sites using the spectrum variance. The values of spectrum variance change more rapidly in the fat tissue than the non-fat tissue. === Applied Science, Faculty of === Electrical and Computer Engineering, Department of === Graduate
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