Automatic thickness estimation for skeletal muscle in ultrasonography: evaluation of two enhancement methods

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• 作者：Pan Han (1) (2)
  Ye Chen (1) (2) (4)
  Lijuan Ao (3)
  Gaosheng Xie (1) (2)
  Huihui Li (1) (2)
  Lei Wang (1) (2)
  Yongjin Zhou (1) (2)
  
• 关键词：Ultrasonography/Ultrasound imaging ; Image enhancement ; Gabor filtering ; Multiscale vessel enhancement filtering ; Muscle thickness
• 刊名：BioMedical Engineering OnLine
• 出版年：2013
• 出版时间：December 2013
• 年：2013
• 卷：12
• 期：1
• 全文大小：540 KB
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• 作者单位：Pan Han (1) (2)
  Ye Chen (1) (2) (4)
  Lijuan Ao (3)
  Gaosheng Xie (1) (2)
  Huihui Li (1) (2)
  Lei Wang (1) (2)
  Yongjin Zhou (1) (2)
  
  1. Shenzhen Institutes of Advanced Technology, Chinese Academy of Science, Shenzhen, China
  2. The Shenzhen Key Laboratory for Low-cost Healthcare, Shenzhen, China
  4. School of Information Engineering, Nanchang Hangkong University, Nanchang, China
  3. Second Affiliated Hospital of Kunming Medical University, Kunming, China
  
• ISSN：1475-925X

文摘

Background Ultrasonography is a convenient technique to investigate muscle properties and has been widely used to look into muscle functions since it is non-invasive and real-time. Muscle thickness, a quantification which can effectively reflect the muscle activities during muscle contraction, is an important measure for musculoskeletal studies using ultrasonography. The traditional manual operation to read muscle thickness is subjective and time-consuming, therefore a number of studies have focused on the automatic estimation of muscle fascicle orientation and muscle thickness, to which the speckle noises in ultrasound images could be the major obstacle. There have been two popular methods proposed to enhance the hyperechoic regions over the speckles in ultrasonography, namely Gabor Filtering and Multiscale Vessel Enhancement Filtering (MVEF). Methods A study on gastrocnemius muscle is conducted to quantitatively evaluate whether and how these two methods could help the automatic estimation of the muscle thickness based on Revoting Hough Transform (RVHT). The muscle thickness results obtained from each of the two methods are compared with the results from manual measurement, respectively. Data from an aged subject with cerebral infarction is also studied. Results It’s shown in the experiments that, Gabor Filtering and MVEF can both enable RVHT to generate comparable results of muscle thickness to those by manual drawing (mean?±?SD, 1.45?±-.48 and 1.38?±-.56 mm respectively). However, the MVEF method requires much less computation than Gabor Filtering. Conclusions Both methods, as preprocessing procedure can enable RVHT the automatic estimation of muscle thickness and MVEF is believed to be a better choice for real-time applications.