期刊名称：International Journal of Computer Science and Network Security
印刷版ISSN：1738-7906
出版年度：2019
卷号：19
期号：5
页码：230-236
出版社：International Journal of Computer Science and Network Security
摘要：Medical imaging is vital to modern clinical practice, enabling clinicians to examine tissues inside the human body non-invasively. Its value depends on accuracy, resolution, and the image property (e.g., density). Various new scanning techniques are aimed at producing elasticity images related to mechanical properties (e.g., sti?ness) to which conventional forms of ultrasound, X-ray and magnetic resonance imaging are insensitive. Elastography, palpography or strain imaging has been under development for almost two decades. Elasticity images are produced by estimating and analysing quasistatic deformations that occur between the acquisitions of multiple ultrasound images. Likely applications include improved diagnosis of breast cancer (which often presents as a sti? lump), but the technique can be unreliable and di?cult to perform. In this paper a novel algorithm is proposed to find out the stress distribution from the strain distribution and stress value on the top surface of the Region of Interest (ROI). The comparison is also shown between the classical and the proposed method. Practical imaging is based on freehand scanning, i.e., the ultrasound probe is moved manually over the surface of the tissue. This requires that elasticity images are calculated fast to provide a live display, and the images need to present meaningful elasticity data despite the poorly controlled properties of the deformations. Stress distribution reconstruction is vital to find out the true value of the modulus distribution. Stress distribution only can predict based on different parameters. The modulus prediction error is minimized with a less number of iteration by using the proposed algorithm. It shows that only 6 iterations make the prediction error very close to 5% where as in conventional method it shows more than 12%.
关键词：Modulus reconstruction; simultaneous data; distribution
