TY - GEN
T1 - Pre-processing for muscle motion analysis
T2 - 35th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2013
AU - Chen, Ye
AU - Zhou, Yongjin
AU - Ivanov, Kamen
AU - Li, Jizhou
AU - Shu, Yuanzhong
AU - Wang, Lei
PY - 2013
Y1 - 2013
N2 - Skeletal muscle is an important tissue of human body, and its contractions control and regulate body motions. Muscle contraction results in morphological changes of the related muscles. Ultrasound imaging is an effective tool for studying muscle architectures and monitoring the morphological changes of muscles. The latter process can be realized with a motion estimation algorithm. However, ultrasound images are usually corrupted by speckle noises and performance of motion estimation methods can be significantly affected by the noises. To get a better performance in motion analysis, in this paper, as a pre-processing step, an adaptive filter named adaptive guided image filtering (AGF) is suggested to reduce speckle noises. We first transformed the multiplicative noise model into an additive one by taking the logarithm of the original speckled data, then performed AGF to obtain the filtered image, and finally took the tackled image back into exponent. Experimental results showed that AGF had a better performance in terms of noise attenuation and edge preservation compared with other standard filters. In quantitative results, the filtered images also had the highest Peak-Signal-to-Noise Ratio (PSNR) using AGF. It's believed that AGF is a good choice for the pre-processing stage of muscle motion analysis.
AB - Skeletal muscle is an important tissue of human body, and its contractions control and regulate body motions. Muscle contraction results in morphological changes of the related muscles. Ultrasound imaging is an effective tool for studying muscle architectures and monitoring the morphological changes of muscles. The latter process can be realized with a motion estimation algorithm. However, ultrasound images are usually corrupted by speckle noises and performance of motion estimation methods can be significantly affected by the noises. To get a better performance in motion analysis, in this paper, as a pre-processing step, an adaptive filter named adaptive guided image filtering (AGF) is suggested to reduce speckle noises. We first transformed the multiplicative noise model into an additive one by taking the logarithm of the original speckled data, then performed AGF to obtain the filtered image, and finally took the tackled image back into exponent. Experimental results showed that AGF had a better performance in terms of noise attenuation and edge preservation compared with other standard filters. In quantitative results, the filtered images also had the highest Peak-Signal-to-Noise Ratio (PSNR) using AGF. It's believed that AGF is a good choice for the pre-processing stage of muscle motion analysis.
KW - adaptive guided image filtering
KW - muscle motion
KW - optical flow
KW - speckle reduction
KW - Ultrasound image
UR - http://www.scopus.com/inward/record.url?scp=84886452634&partnerID=8YFLogxK
UR - https://www.scopus.com/record/pubmetrics.uri?eid=2-s2.0-84886452634&origin=recordpage
U2 - 10.1109/EMBC.2013.6610428
DO - 10.1109/EMBC.2013.6610428
M3 - RGC 32 - Refereed conference paper (with host publication)
C2 - 24110615
AN - SCOPUS:84886452634
T3 - Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS
SP - 4026
EP - 4029
BT - 2013 35th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC)
PB - IEEE
Y2 - 3 July 2013 through 7 July 2013
ER -