title: Multi-scale and shape constrained localized region-based active contour segmentation of uterine fibroid ultrasound images in HIFU therapy.
abstract: OBJECTIVE
  To overcome the severe intensity inhomogeneity and blurry boundaries in HIFU (High Intensity Focused Ultrasound) ultrasound images, an accurate and efficient multi-scale and shape constrained localized region-based active contour model (MSLCV), was developed to accurately and efficiently segment the target region in HIFU ultrasound images of uterine fibroids.
  
  
  METHODS
  We incorporated a new shape constraint into the localized region-based active contour, which constrained the active contour to obtain the desired, accurate segmentation, avoiding boundary leakage and excessive contraction. Localized region-based active contour modeling is suitable for ultrasound images, but it still cannot acquire satisfactory segmentation for HIFU ultrasound images of uterine fibroids. We improved the localized region-based active contour model by incorporating a shape constraint into region-based level set framework to increase segmentation accuracy. Some improvement measures were proposed to overcome the sensitivity of initialization, and a multi-scale segmentation method was proposed to improve segmentation efficiency. We also designed an adaptive localizing radius size selection function to acquire better segmentation results.
  
  
  RESULTS
  Experimental results demonstrated that the MSLCV model was significantly more accurate and efficient than conventional methods. The MSLCV model has been quantitatively validated via experiments, obtaining an average of 0.94 for the DSC (Dice similarity coefficient) and 25.16 for the MSSD (mean sum of square distance). Moreover, by using the multi-scale segmentation method, the MSLCV model's average segmentation time was decreased to approximately 1/8 that of the localized region-based active contour model (the LCV model).
  
  
  CONCLUSIONS
  An accurate and efficient multi-scale and shape constrained localized region-based active contour model was designed for the semi-automatic segmentation of uterine fibroid ultrasound (UFUS) images in HIFU therapy. Compared with other methods, it provided more accurate and more efficient segmentation results that are very close to those obtained from manual segmentation by a specialist.
authors: School of Computer, Wuhan University, Wuhan, Hubei, China.
authors: Xiangyun
authors: X
authors: Liao
authors: School of Computer, Wuhan University, Wuhan, Hubei, China.
authors: Zhiyong
authors: Z
authors: Yuan
authors: School of Computer, Wuhan University, Wuhan, Hubei, China.
authors: Qi
authors: Q
authors: Zheng
authors: College of Information Science and Technology, Beijing Normal University, Beijing, China.
authors: Qian
authors: Q
authors: Yin
authors: School of Physics and Technology, Wuhan University, Wuhan, Hubei, China.
authors: Dong
authors: D
authors: Zhang
authors: School of Computer, Wuhan University, Wuhan, Hubei, China.
authors: Jianhui
authors: J
authors: Zhao
mesh_terms: D000465:Algorithms; D005260:Female; D057086:High-Intensity Focused Ultrasound Ablation / Q000379:methods*; D006801:Humans; D007091:Image Processing, Computer-Assisted / Q000379:methods*; D007889:Leiomyoma / Q000000981:diagnostic imaging / Q000628:therapy*; D014463:Ultrasonography; D014594:Uterine Neoplasms / Q000000981:diagnostic imaging / Q000628:therapy*