Multi-scale and shape constrained localized region-based active contour segmentation of uterine fibroid ultrasound images in HIFU therapy

PLoS ONE

Volumn 9, Issue 7, 2014, Pages

  Liao, Xiangyun ^a   Yuan, Zhiyong ^a   Zheng, Qi ^a   Yin, Qian ^b   Zhang, Dong ^a   Zhao, Jianhui ^a  

^a WUHAN UNIVERSITY   (China)

^b BEIJING NORMAL UNIVERSITY   (China)

Author keywords

[No Author keywords available]

Indexed keywords

ARTICLE; CONTROLLED STUDY; HIGH INTENSITY FOCUSED ULTRASOUND; IMAGE ANALYSIS; IMAGE DISPLAY; IMAGE SEGMENTATION; IMAGING AND DISPLAY; INTERMETHOD COMPARISON; MATHEMATICAL COMPUTING; MATHEMATICAL MODEL; MEASUREMENT ACCURACY; MULTISCALE AND SHAPE CONSTRAINED LOCALIZED REGION BASED ACTIVE CONTOUR MODEL; UTERUS MYOMA; ALGORITHM; ECHOGRAPHY; FEMALE; HUMAN; IMAGE PROCESSING; LEIOMYOMA; PROCEDURES; UTERINE NEOPLASMS;

ALGORITHMS; FEMALE; HIGH-INTENSITY FOCUSED ULTRASOUND ABLATION; HUMANS; IMAGE PROCESSING, COMPUTER-ASSISTED; LEIOMYOMA; UTERINE NEOPLASMS;

EID: 84904988800     PISSN: None     EISSN: 19326203     Source Type: Journal    
DOI: 10.1371/journal.pone.0103334     Document Type: Article

References (39)

1. Kennedy JE (2005) High-intensity focused ultrasound in the treatment of solid tumours. Nat. Rev. Cancer 5(4): 321-327.
2. Orsi F, Arnone P, Chen W, Zhang L (2010) High intensity focused ultrasound ablation: A new therapeutic option for solid tumors. J Cancer Res Ther 6(4): 414-420.
3. Kim Y, Kim J H, Rhim H (2012) Volumetric MR-guided high-intensity focused ultrasound ablation with a one-layer strategy to treat large uterine fibroids: initial clinical outcomes. Radiology 263(2): 600-609.
4. Wang W, Wang Y, Wang T (2012) Safety and efficacy of US-guided high-intensity focused ultrasound for treatment of submucosal fibroids. European radiology 22(11): 2553-2558.
5. Noble J A, Boukerroui D (2006) Ultrasound image segmentation: a survey. Medical Imaging, IEEE Transactions on 25(8): 987-1010. (Pubitemid 44192172)
6. Zhang L, Wang Z B (2010) High-intensity focused ultrasound tumor ablation: review of ten years of clinical experience. Frontiers of medicine in China 4(3): 294-302.
7. Wang X, Qin J, Wang L (2013) Effect of high-intensity focused ultrasound on sexual function in the treatment of uterine fibroids: comparison to conventional myomectomy. Archives of gynecology and obstetrics 288(4): 851-858.
8. Al-Bataineh O, Jenne J, Huber P (2012) Clinical and future applications of high intensity focused ultrasound in cancer. Cancer treatment reviews 38(5): 346-353.
9. Cheng H D, Shan J, Ju W (2010) Automated breast cancer detection and classification using ultrasound images: A survey. Pattern Recognition 43(1): 299-317.
10. Ghose S, Oliver A, Martí R (2012) A survey of prostate segmentation methodologies in ultrasound, magnetic resonance and computed tomography images. Computer methods and programs in biomedicine 108(1): 262-287.
11. Li C, Huang R, Ding Z (2011) A level set method for image segmentation in the presence of intensity inhomogeneities with application to MRI. Image Processing, IEEE Transactions on 20(7): 2007-2016.
12. Kass M, Witkin A, Terzopoulos D (1988) Snakes: Active contour models. International journal of computer vision 1(4): 321-331.
13. Caselles V, Kimmel R, Sapiro G (1997) Geodesic active contours. International journal of computer vision 22(1): 61-79. (Pubitemid 127507390)
14. Li C, Xu C, Gui C (2005) Level set evolution without re-initialization: a new variational formulation. CVPR 2005. IEEE Computer Society Conference on IEEE 1: 430-436.
15. Ronfard R (1994) Region-based strategies for active contour models. International Journal of Computer Vision 13(2): 229-251.
16. Chan T F, Vese L A (2001) Active contours without edges. Image processing, IEEE transactions on 10(2): 266-277.
17. Vese L A, Chan T F (2002) A multiphase level set framework for image segmentation using the Mumford and Shah model. International journal of computer vision 50(3): 271-293. (Pubitemid 35377416)
18. Li C, Kao C Y, Gore J C (2008) Minimization of region-scalable fitting energy for image segmentation. Image Processing, IEEE Transactions on 17(10): 1940-1949.
19. Lankton S, Tannenbaum A (2008) Localizing region-based active contours. Image Processing, IEEE Transactions on 17(11): 2029-2039.
20. Xu C, Prince J L (1998) Snakes, shapes, and gradient vector flow. Image Processing, IEEE Transactions on 7(3): 359-369. (Pubitemid 128745354)
21. Li C, Kao C Y, Gore J C (2007) Implicit active contours driven by local binary fitting energy. CVPR'07. IEEE Conference on IEEE: 1-7.
22. Zhang K, Song H, Zhang L (2010) Active contours driven by local image fitting energy. Pattern recognition 43(4): 1199-1206.
23. Wang L, Li C, Sun Q (2009) Active contours driven by local and global intensity fitting energy with application to brain MR image segmentation. Computerized Medical Imaging and Graphics 33(7): 520-531.
24. Wu H, Appia V, Yezzi A (2013) Numerical conditioning problems and solutions for nonparametric iid. statistical active contours. Pattern Analysis and Machine Intelligence, IEEE Transactions on 35(6): 1298-1311.
25. Appia V, Yezzi A (2011) Active geodesics: Region-based active contour segmentation with a global edge-based constraint. IEEE International Conference on Computer Vision(ICCV) 1975-1980.
26. Leventon M E, Grimson W E L, Faugeras O (2000) Statistical shape influence in geodesic active contours. Computer Vision and Pattern Recognition, Proceedings. IEEE Conference on 1: 316-323.
27. Chen Y, Tagare H D, Thiruvenkadam S (2002) Using prior shapes in geometric active contours in a variational framework. International Journal of Computer Vision 50(3): 315-328. (Pubitemid 35377418)
28. Chen Y, Huang F, Tagare H D (2007) A coupled minimization problem for medical image segmentation with priors. International journal of computer vision 71(3): 259-272. (Pubitemid 44610049)
29. Bresson X, Vandergheynst P, Thiran J P (2006) A variational model for object segmentation using boundary information and shape prior driven by the Mumford-Shah functional. International Journal of Computer Vision 68(2): 145-162. (Pubitemid 43724243)
30. Wu P, Liu Y, Li Y (2013) TRUS image segmentation with non-parametric kernel density estimation shape prior. Biomedical Signal Processing and Control 8(6): 764-771.
31. Huang Q, Bai X, Li Y (2014) Optimized graph-based segmentation for ultrasound images. Neurocomputing 129: 216-224.
32. Yu H, Li L, Xu W (2010) A multi-scale approach to mass segmentation using active contour models. SPIE Medical Imaging 762343-762343-8.
33. Kim W, Kim C (2012) Total variation flow-based multiscale framework for unsupervised surface defect segmentation. Optical Engineering 51(12): 127-201.
34. Zhou J, Jin Z (2013) A new framework for multiscale saliency detection based on image patches. Neural processing letters 38(3): 361-374.
35. Wang W, Zhu L, Qin J (2014) Multiscale geodesic active contours for ultrasound image segmentation using speckle reducing anisotropic diffusion. Optics and Lasers in Engineering 54: 105-116.
36. Mumford D, Shah J (1989) Optimal approximations by piecewise smooth functions and associated variational problems. Communications on pure and applied mathematics 42(5): 577-685.
37. Dietenbeck T, Alessandrini M, Friboulet D, Bernard O (2010) CREASEG: a free software for the evaluation of image segmentation algorithms based on level-set. In IEEE International Conference On Image Processing. Hong Kong, China.
38. Wang L, He L, Mishra A (2009) Active contours driven by local Gaussian distribution fitting energy. Signal Processing 89(12): 2435-2447.
39. Bernard O, Friboulet D, Thévenaz P (2009) Variational B-Spline level-set: A linear filtering approach for fast deformable model evolution. Image Processing, IEEE Transactions on 18(6): 1179-1191.