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Automatic seed point selection in ultrasound echography images of breast using texture features

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Warianty tytułu
Języki publikacji
EN
Abstrakty
EN
Automatic segmentation of breast lesions in 2D ultrasound B-scan images via active contours, require a seed point to be selected inside the breast lesion. The grey levels on an ultrasound image of the breast show intensity information. The fat tissue is hypo echoic relative to the surrounding glandular tissue. The glandular parenchyma tissue usually appears homogeneously echogenic as compared with fat lobules. Simple cysts are anechoic. Malignant solid masses are usually heterogeneous, hypo echoic and tend to look intensely black compared to surrounding isoechoic fat. Benign solid masses tend to appear on ultrasound with intense and uniform hyper echogenicity. Texture features represent changes in grey level intensities. This paper proposes a method that can automatically identify a seed point based on texture features and allow automatic contour initialization for level set segmentation. This seed point plotted on an US B-scan image is mapped on to its corresponding elastogram pair. The proposed approach is applied to 199 ultrasound B-scan images of which 52 are benign solid masses, 84 malignant solid masses and 63 simple and complex cysts. The seed point obtained using this approach is mapped to its corresponding elastogram pair in 62 US B-scan and US elastography image pairs. Quantitative experiment results show that our proposed approach can successfully find proper seed points based on texture values, in ultrasound B-scan images and therefore in elastography images, with an overall accuracy of 86.93%. This approach is effective and makes segmentation of breast lesions computationally easier, more accurate and fast.
Twórcy
autor
  • Center for Medical Electronics, College of Engineering, Anna University, Chennai 600025, India
  • Center for Medical Electronics, College of Engineering, Anna University, Chennai 600025, India
Bibliografia
  • [1] Cheng HD, Juan S, Wen J, Yanhui G, Ling Z. Automated breast cancer detection and classification using ultrasound images: a survey. Pattern Recogn 2010;43(1):299–317.
  • [2] Chandraratne MR, Samarasinghe S, Kulasiri D, Bickerstaffe R. Prediction of lamb tenderness using image surface texture features. J Food Eng 2006;77(3):492–9.
  • [3] Renbo X, Weijun L, et al. An optimal initialization technique for improving the segmentation performance of Chan-Vese model. IEEE Int Conf Autom Logistics 2007;18:411–5.
  • [4] Yongjian Y, Scott TA. Speckle reducing anisotropic diffusion. IEEE Trans Image Process 2002;11:1260–70.
  • [5] Otsu N. A threshold selection method from gray-level histograms. IEEE Trans Syst Man Cybern 1979;9(1):62–6.
  • [6] Osher S, Sethian JA. Fronts propagating with curvature-dependent speed: algorithms based on Hamilton–Jacobi formulations. J Comput Phys 1988;79:12–49.
  • [7] Selvan S, Kavitha M, Shenbaga devi S, Suresh S. Fuzzy based classification of breast lesions using ultrasound echography and elastography. Ultrasound Q 2012;28(3):159–67.
  • [8] Selvan S, Kavitha M, Shenbaga Devi S, Suresh S. Automatic segmentation and feature extraction of breast lesions. Int J Comput Intell Healthc Inform 2010;3:65–9.
  • [9] Selvan S, Kavitha M, Shenbaga Devi S, Suresh S. Feature extraction for characterization of breast lesions in ultrasound echography and elastography. J Comput Sci 2010;6(1):67–74.
  • [10] Li C, Xu C, Gui C, Fox M. Level set evolution without reinitialization: a new variational formulation. Proc IEEE Conf on Computer Vision and Pattern Recognition, CVPR2005; 2005. pp. 430–6.
  • [11] Doshi DJ, March DE, Crisi GM, Coughlin BF. Complex cystic breast masses: diagnostic approach and imaging-pathologic correlation. Radiographics 2007;27(1):53–64.
  • [12] Madabhushi A, Metaxas DN. Combining low-, high-level and empirical domain knowledge for automated segmentation of ultrasonic breast lesions. IEEE Trans Med Imaging 2003;22(2):155–69.
  • [13] Jung I-S, Thapa D, Wang G-N. Automatic segmentation and diagnosis of breast lesions using morphology method based on ultrasound. In: Wang L, Jin Y, editors. 2nd Int. Conf. Fuzzy Systems and Knowledge Discovery. 2005.
  • [14] Shan J, Cheng HD, Wang Y. A novel automatic seed point selection algorithm for breast ultrasound images. 19th Int. Conf. Pattern Recognition. 2008. pp. 1–4.
  • [15] Chan TF, Vese LA. Active contours without edges. IEEE Trans Image Process 2001;10(February (2)).
  • [16] Cremers D, Rousson M, Deriche R. A review of statistical approaches to level set segmentation: integrating color, texture, motion and shape. Int J Comput Vis 2007;72(2):195–215.
  • [17] Ramsay DT, Kent JC, Hartmann RA, Hartmann PE. Anatomy of the lactating human breast redefined with ultrasound imaging. J Anat 2005;206:525–34.
Typ dokumentu
Bibliografia
Identyfikator YADDA
bwmeta1.element.baztech-c518827a-0739-4cd5-9a8c-c59a53cdb477
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