An effective method for ultrasound thyroid nodules segmentation

Ultrasound thyroid nodules segmentation is a much tough task due to the speckle noise, intensity heterogeneity, and low contrast. In this paper, we introduce an effective segmentation method to make the problem better. Our works are composed of pre-processing, extracting boundaries, postprocessing. First, we suppress the speckle noise by speckle reducing anisotropic diffusion method. Second, we introduce a reasonable and effective speed stopping term based local phase symmetry feature which is theoretically intensity invariant, and the new speed stop term is incorporated into distance regularized level set evolution. Then, to remedy the emerging problems, we do some effective post-processing. Qualitative and quantitative comparative results demonstrate the superiority of our proposed method.

[1]  Tony F. Chan,et al.  Active contours without edges , 2001, IEEE Trans. Image Process..

[2]  Michalis A. Savelonas,et al.  Active Contours Guided by Echogenicity and Texture for Delineation of Thyroid Nodules in Ultrasound Images , 2009, IEEE Transactions on Information Technology in Biomedicine.

[3]  M. Sundaresan,et al.  Survey of image segmentation algorithms on ultrasound medical images , 2013, 2013 International Conference on Pattern Recognition, Informatics and Mobile Engineering.

[4]  Michael Felsberg,et al.  The monogenic signal , 2001, IEEE Trans. Signal Process..

[5]  Nikos Dimitropoulos,et al.  Variable Background Active Contour Model for Computer-Aided Delineation of Nodules in Thyroid Ultrasound Images , 2007, IEEE Transactions on Information Technology in Biomedicine.

[6]  Ahror Belaid,et al.  Phase based level set segmentation of ultrasound images , 2009, 2009 9th International Conference on Information Technology and Applications in Biomedicine.

[7]  Michael Brady,et al.  On the Choice of Band-Pass Quadrature Filters , 2004, Journal of Mathematical Imaging and Vision.

[8]  Xianglong Tang,et al.  Probability density difference-based active contour for ultrasound image segmentation , 2010, Pattern Recognit..

[9]  Adel Hafiane,et al.  Phase-based probabilistic active contour for nerve detection in ultrasound images for regional anesthesia , 2014, Comput. Biol. Medicine.

[10]  Chunming Li,et al.  Distance Regularized Level Set Evolution and Its Application to Image Segmentation , 2010, IEEE Transactions on Image Processing.

[11]  Jaime B. Santos,et al.  A new level set based segmentation method for the four cardiac chambers , 2010, 5th Iberian Conference on Information Systems and Technologies.

[12]  Savita Gupta,et al.  Survey of Computer-Aided Diagnosis of Thyroid Nodules in Medical Ultrasound Images , 2012, ACITY.

[13]  Paula Martins,et al.  Phase Symmetry Approach Applied to Children Heart Chambers Segmentation: A Comparative Study , 2011, IEEE Transactions on Biomedical Engineering.

[14]  Peter Kovesi,et al.  Symmetry and Asymmetry from Local Phase , 1997 .

[15]  Scott T. Acton,et al.  Speckle reducing anisotropic diffusion , 2002, IEEE Trans. Image Process..