title: Automatic thickness estimation for skeletal muscle in ultrasonography: evaluation of two enhancement methods.
abstract: 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 ± 0.48 and 1.38 ± 0.56 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.
authors: Shenzhen Institutes of Advanced Technology, Chinese Academy of Science, Shenzhen, China.
authors: Pan
authors: P
authors: Han
authors: Ye
authors: Y
authors: Chen
authors: Lijuan
authors: L
authors: Ao
authors: Gaosheng
authors: G
authors: Xie
authors: Huihui
authors: H
authors: Li
authors: Lei
authors: L
authors: Wang
authors: Yongjin
authors: Y
authors: Zhou
mesh_terms: D000328:Adult; D000368:Aged; D015992:Body Mass Index; D001835:Body Weight; D002544:Cerebral Infarction / Q000503:physiopathology; D003198:Computer Simulation; D016208:Databases, Factual; D006801:Humans; D007090:Image Interpretation, Computer-Assisted / Q000379:methods*; D008297:Male; D009119:Muscle Contraction / Q000502:physiology; D018482:Muscle, Skeletal / Q000000981:diagnostic imaging* / Q000503:physiopathology; D014463:Ultrasonography