Abstract
Breast tumour is a leading cause for woman mortality. While cancer screening is mostly performed by the use of mammography, 3D ultrasound seems better suited for the purpose. It gives 3D view of the breast structure, is less painful and can be considered less invasive, as the patient is not exposed to x-ray radiation. Therefore, the development of automatic algorithms that remove from the diagnostician the tedious and time consuming task of finding suspicious regions in large volumetric images is of key importance. The paper concludes a preliminary study for the development of an automatic method for breast tumour segmentation in ultrasound volumetric images. The method is based on multiscale blob detector, watershed transform with the final precise segmentation performed by an active contour approach. The method has been evaluated using 16 volumes acquired from a breast phantom containing nodules. The obtained results reached up to 94.68% sensitivity, 100.00% specificity, 92.63% Dice index, 99.95% Accuracy, 92.61% Cohen’s Kappa index and 86.28% Jaccard index.
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References
Cancer fact sheets: Breast cancer. http://gco.iarc.fr/today/fact-sheets-cancers?cancer=15&type=0&sex=2
Chan, T.F., Vese, L.A.: Active contours without edges. Trans. Img. Proc. 10(2), 266–277 (2001). https://doi.org/10.1109/83.902291
Chang, R.F., Wu, W.J., Moon, W.K., Chen, D.R.: Automatic ultrasound segmentation and morphology based diagnosis of solid breast tumors. Breast Cancer Res. Treat. 89(2), 179 (2005). https://doi.org/10.1007/s10549-004-2043-z
Chang, R.F., Wu, W.J., Moon, W.K., Chen, W.M., Lee, W., Chen, D.R.: Segmentation of breast tumor in three-dimensional ultrasound images using three-dimensional discrete active contour model. Ultrasound Med. Biol. 29(11), 1571–1581 (2003). https://doi.org/10.1016/S0301-5629(03)00992-X
Cheng, H., Shan, J., Ju, W., Guo, Y., Zhang, L.: Automated breast cancer detection and classification using ultrasound images: a survey. Patt. Recogn. 43(1), 299–317 (2010). https://doi.org/10.1016/j.patcog.2009.05.012
Cohen, J.: A coefficient of agreement for nominal scale. Educ. Psychol. Measur. 20, 37–46 (1960)
Frangi, A.F., Niessen, W.J., Vincken, K.L., Viergever, M.A.: Multiscale vessel enhancement filtering. In: Wells, W.M., Colchester, A., Delp, S. (eds.) Medical Image Computing and Computer-Assisted Intervention – MICCAI 1998, pp. 130–137. Springer, Heidelberg (1998)
Galińska, M., Ogiegło, W., Wijata, A., Juszczyk, J., Czajkowska, J.: Breast cancer segmentation method in ultrasound images. In: Gzik, M., Tkacz, E., Paszenda, Z., Piętka, E. (eds.) Innovations in Biomedical Engineering, pp. 23–31. Springer, Cham (2018)
Gu, P., Lee, W.M., Roubidoux, M.A., Yuan, J., Wang, X., Carson, P.L.: Automated 3D ultrasound image segmentation to aid breast cancer image interpretation. Ultrasonics 65, 51–58 (2016). https://doi.org/10.1016/j.ultras.2015.10.023
Huang, Q., Yang, F., Liu, L., Li, X.: Automatic segmentation of breast lesions for interaction in ultrasonic computer-aided diagnosis. Inf. Sci. 314, 293–310 (2015). https://doi.org/10.1016/j.ins.2014.08.021
Huang, Y.L., Chen, D.R.: Watershed segmentation for breast tumor in 2-D sonography. Ultrasound Med. Biol. 30(5), 625–632 (2004). https://doi.org/10.1016/j.ultrasmedbio.2003.12.001
Jiang, P., Peng, J., Zhang, G., Cheng, E., Megalooikonomou, V., Ling, H.: Learning-based automatic breast tumor detection and segmentation in ultrasound images. In: 2012 9th IEEE International Symposium on Biomedical Imaging (ISBI), pp. 1587–1590 (2012). https://doi.org/10.1109/ISBI.2012.6235878
Kim, J.H., Cha, J.H., Kim, N., Chang, Y., Ko, M.S., Choi, Y.W., Kim, H.H.: Computer-aided detection system for masses in automated whole breast ultrasonography: development and evaluation of the effectiveness. Ultrasonography 33(2), 105–115 (2014). https://doi.org/10.14366/usg.13023
Landis, J., Koch, G.: The measurement of observer agreement for categorical data. Biometrics 33(1), 159–174 (1977)
Lasso, A., Heffter, T., Rankin, A., Pinter, C., Ungi, T., Fichtinger, G.: PLUS: Open-source toolkit for ultrasound-guided intervention systems. IEEE Trans. Biomed. Eng. 61(10), 2527–2537 (2014). https://doi.org/10.1109/TBME.2014.2322864
Liu, B., Cheng, H., Huang, J., Tian, J., Tang, X., Liu, J.: Fully automatic and segmentation-robust classification of breast tumors based on local texture analysis of ultrasound images. Patt. Recogn. 43(1), 280–298 (2010). https://doi.org/10.1016/j.patcog.2009.06.002
Meyer, F.: Topographic distance and watershed lines. Sig. Process. 38(1), 113–125 (1994). https://doi.org/10.1016/0165-1684(94)90060-4
Minavathi, M., Murali, S., Dinesh, M.S.: Classification of mass in breast ultrasound images using image processing techniques. Int. J. Comput. Appl. 42(10), 29–36 (2012). Full text available
Moon, W.K., Shen, Y.W., Bae, M.S., Huang, C.S., Chen, J.H., Chang, R.F.: Computer-aided tumor detection based on multi-scale blob detection algorithm in automated breast ultrasound images. IEEE Trans. Med. Imaging 32(7), 1191–1200 (2013). https://doi.org/10.1109/TMI.2012.2230403
Rodtook, A., Makhanov, S.S.: Multi-feature gradient vector flow snakes for adaptive segmentation of the ultrasound images of breast cancer. J. Vis. Commun. Image Representation 24(8), 1414–1430 (2013). https://doi.org/10.1016/j.jvcir.2013.09.009
Shi, X., Cheng, H., Hu, L., Ju, W., Tian, J.: Detection and classification of masses in breast ultrasound images. Dig. Sig. Process. 20(3), 824–836 (2010). https://doi.org/10.1016/j.dsp.2009.10.010
Xian, M., Zhang, Y., Cheng, H.: Fully automatic segmentation of breast ultrasound images based on breast characteristics in space and frequency domains. Patt. Recogn. 48(2), 485–497 (2015). https://doi.org/10.1016/j.patcog.2014.07.026
Acknowledgement
This research was supported by the Polish National Centre for Research and Development (NCBR), grant no. STRATEGMED2/267398/ 3/NCBR/2015. The authors would also like to thank Andre Woloshuk for his English language corrections.
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Wieclawek, W., Rudzki, M., Wijata, A., Galinska, M. (2019). Preliminary Development of an Automatic Breast Tumour Segmentation Algorithm from Ultrasound Volumetric Images. In: Pietka, E., Badura, P., Kawa, J., Wieclawek, W. (eds) Information Technology in Biomedicine. ITIB 2018. Advances in Intelligent Systems and Computing, vol 762. Springer, Cham. https://doi.org/10.1007/978-3-319-91211-0_7
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