Lesion Extraction from B-type Ultrasound Image Using Subordinate Degree Region Level Set Method_Journal of Biomedical Engineering

Authors：

 YANGYi ¹ , YUDekuang ¹ , SHENHong ²

1. School of Biomedical Engineering, Southern Medical University, Guangzhou 510515, China;
2. School of Basic Medicine, Southern Medical University, Guangzhou 510515, China;

Corresponding author：

YANGYi, Email: yiyang20110130@163.com

Keywords：

level set method; region classification by subordinate degree; B-type ultrasound image; lesion segmentation

DOI：

10.7507/1001-5515.20150142

Video：

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Abstract Full text Figures/Tables Video References Cited by

B-type ultrasound images have important applications in medical diagnosis. However, the widely spread intensity inhomogeneity, low-scale contrast, constructed defect, noise and blurred edges all make it difficult to implement automatic segmentation of lesion in the images. Based on region level set method, a subordinate degree region level set model was proposed, in which subordinate degree probability of each pixel was defined to reflect the pixel subjection grade to target and background respectively. Pixels were classified to either target or background by calculation of their subordinate degree probabilities, and edge contour was obtained by region level set iterations. In this paper, lesion segmentation is regarded as local segmentation of specific area, and the calculation is restrained to the local sphere abide by the contour, which greatly reduce the calculation complexity. Experiments on B-type ultrasound images showed improved results of the proposed method compared to those of some popular level set methods.

Citation： YANGYi, YUDekuang, SHENHong. Lesion Extraction from B-type Ultrasound Image Using Subordinate Degree Region Level Set Method. Journal of Biomedical Engineering, 2015, 32(4): 779-787. doi: 10.7507/1001-5515.20150142 Copy

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1. SAINI K,DEWAL M L,ROHIT M.Ultrasound imaging and image segmentation in the area of ultrasound:a review[J].International Journal of Advanced Science and Technology,2010,24:41-60.
2. CREMERS D,ROUSSON M,DERICHE R.A review of statistical approaches to level set segmentation:integrating color,texture,motion and shape[J].Int J Comput Vis,2007,72(2):195-215.
3. CHAN T F,VESE L A.Active contours without edges[J].IEEE Trans Imag Proc,2001,10(2):266-277.
4. WANG Bin,GAO Xinbo,TAO Dacheng,et al.A unified tensor level set for image segmentation[J].IEEE Trans Syst Man Cybern B Cybern,2010,40(3):857-867.
5. LI Chunming,KAO C Y,GORE J C,et al.Minimization of region-scalable fitting energy for image segmentation[J].IEEE Trans Image Process,2008,17(10):1940-1949.
6. ZHANG Kaihua,ZHANG Lei,SONG Huihui,et al.Active contours with selective local or global segmentation:a new formulation and level set method[J].Image Vis Comput,2010,28(4):668-676.
7. WANG Li,HE Lei,MISHRA A,et al.Active contours driven by local Gaussian distribution fitting energy[J].Signal Processing,2009,89(12):2435-2447.
8. 张建伟,葛琦.基于局部统计信息的快速CV模型MR图像分割[J].中国图象图形学报A,2010,15(1):69-74.
9. SHI Yonggang,KARL W C.A real-time algorithm for the approximation of level-set-based curve evolution[J].IEEE Trans Image Process,2008,17(5):645-656.
10. TAI Xuecheng,WU Chunlin.Augmented Lagrangian method,dual methods and split Bregman iteration for ROF model[C]//Proceedings of the 2nd International Conference on Scale Space and Variational Methods in Computer Vision.Voss,Norway:2009:502-513.
11. ESTELLERS V,ZOSSO D,LAI Rongjie,et al.Efficient algorithm for level set method preserving distance function[J].IEEE Trans Image Process,2012,21(12):4722-4734.
12. LIU Jun,CHEN Jianxun,LIU Xiaoming,et al.Mass segmentation using a combined method for cancer detection[J].BMC Syst Biol,2011,5(Suppl 3):S6.

Journal of Biomedical Engineering

Lesion Extraction from B-type Ultrasound Image Using Subordinate Degree Region Level Set Method

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