Breast Cancer Segmentation Method in Ultrasound Images
The most common type of cancer among women is breast cancer. The early diagnosis is crucial in a treatment process. The radiology support system in the diagnostic process allows faster and more accurate radiographic contouring. The aim of the paper is to present a new method for ultrasound image segmentation of breast lesions. The segmentation technique is based on active contour models whereas anisotropic diffusion is used for preprocessing. The Dice Index calculated in most of analyzed cases was greater than 80%. Delineation of the tumor can also be used to calculate the size and volume automatically, and shortened the time of the diagnosis.
KeywordsUS image segmentation active contour breast cancer
Unable to display preview. Download preview PDF.
This research was supported by the Polish Ministry of Science and Silesian University of Technology statutory financial support for young researchers BKM-510/RAu-3/2017. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the abstract.
- 1.Ferlay, J., Hery, C., Autier, P., Sankaranarayanan, R.: Global burden of breast cancer. Breast cancer epidemiology. Springer New York, 1–19 (2010)Google Scholar
- 2.Dennis, M., Parker, S., Klaus, A., Stavros, A., Kaske, T., Clark, S.: Breast Biopsy Avoidance: The Value of Normal Mammograms and Normal Sonograms in the Setting of a Palpable Lump 1. Radiology 219, 1, 186–191 (2001)Google Scholar
- 3.Jackson, V., Hendrick, R., Feig, S., Kopans, D.: Imaging of the radiographically dense breast. Radiology 188(2), 297–301 (1993)Google Scholar
- 4.Huang, Y., Chen, D.: Watershed segmentation for breast tumor in 2-D sonography. Ultrasound in Medicine & Biology 30(5), 625–632 (2004)Google Scholar
- 5.Parveen, N.: Segmenting tumors in ultrasound images. International Conference on Computing, Communication and Networking, St. Thomas, VI, 1–5 (2008)Google Scholar
- 6.Shi, X., Cheng, H.D., Hu, L., Ju, W., Tian, J.: Detection and classification of masses in breast ultrasound images. Digital Signal Processing 20(3), 824–836 (2010)Google Scholar
- 7.Gomez, W., Rodriguez, A., Pereira, W., Infantosi, A.: Feature selection and classifier performance in computer-aided diagnosis for breast ultrasound. Emerging Technologies for a Smarter World (CEWIT), 10th International Conference and Expo on. IEEE (2013)Google Scholar
- 8.Sasikala, S., Kirthika, B., Malathi, P.: Feature Extraction and Analysis of Breast Lesion in Ultrasound B Mode and Elastography. International Journal of Advanced Research in Computer Science and Software Engineering 4(1), 355–359 (2014)Google Scholar
- 9.Chang, R., Wu, W., Moon, W., Chen, W., Lee, W., Chen, D.R.: Segmentation of breast tumor in three-dimensional ultrasound images using three-dimensional discrete active contour model. Ultrasound in Medicine & Biology 29(11), 1571–1581 (2003)Google Scholar
- 10.Lo, C., Chen, R., Chang, Y., Yang, Y., Hung, M., Huang, C., Chang, R.: Multi-dimensional tumor detection in automated whole breast ultrasound using topographic watershed. IEEE Transactions on Medical Imaging 33(7), 1503–1511 (2014)Google Scholar
- 11.Yu, Y., Acton, S.: Speckle reducing anisotropic diffusion. IEEE Transactions on Image Processing 11(11), 1260–1270 (2002)Google Scholar
- 12.Perona, P., Malik, J.: Scale-space and edge detection using anisotropic diffusion. IEEE Transactions on Pattern Analysis and Mmachine Intelligence 12(7), 629–639 (1990)Google Scholar
- 13.Kass, M., Witkin, A., Terzopoulos, D.: Snakes: Active contour models. International Journal of Computer Vision 1(4), 321–331 (1988)Google Scholar
- 14.Seghier, M., Ramlackhansingh, A., Crinion, J., Leff, A., Price, C.: Lesion identification using unified segmentation-normalisation models and fuzzy clustering. Neuroimage 41(4), 1253–1266 (2008)Google Scholar
- 15.Huttenlocher, D., Klanderman, G., Rucklidge,W.: Comparing images using the Hausdorff distance. IEEE Transactions on Pattern Analysis and Machine Intelligence 15(9), 850–863 (1993)Google Scholar