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A Fully Automatic Method for Breast Lesions Segmentation in Ultrasound Images
Abstract
Due to inherent speckle noise, poor quality data, low
contrast, and large shape variations of ultrasound lesions, automatic
segmentation in breast ultrasound images is still a challenging task. In
this paper, we propose a novel algorithm to segment automatically
breast lesion in ultrasound images. The images are first filtered with a
Speckle Reducing Anisotropic Diffusion algorithm to remove speckle
noise. After that we propose the use of some morphological operators
namely the contrast enhancement, the adaptive thresholding, the
suppression of small regions and regions connected to the image
border, the erosion and dilation, and finally the edge detection
technique for initial lesion localization. Finally, we apply the
Muti-scale Vector Field Convolution snake for boundary lesion
segmentation. A comparative study with previous state of the art
algorithms of active contours using both qualitative and quantitative
measures for real breast ultrasound images are presented in this study
in order to evaluate the performance of our method. Experiments
demonstrate the ability to detect breast lesions automatically, quickly,
efficiently and with high accuracy.
Keywords
References
Y. H. Chou, C. M. Tiu, G. S. Hung, S. C. Wu, T. Y. Chang, et al.,
“Stepwise logistic regression analysis of tumor contour features for breast
ultrasound diagnosis,” Ultrasound Med. biol., vol. 27, no. 11, pp.
-1498, September, 2001.
C. Chen, Y. Chou, K. Han, G. Hung, C. Tiu, et al., “Breast lesions on
sonograms: computer-aided diagnosis with nearly setting-independent
features and artificial neural networks,” Radiology, vol. 226, no. 2, pp.
-514, February 2003.
W. C. Shen, R. F. Chang, and W. K. Moon, “Computer-aided
classification system for breast ultrasound based on breast imaging
reporting and data system (BI-RADS),” Ultrasound Med. biol., vol. 33,
no. 11, pp. 1688-1698, August 2007.
W. C. Shen, R. F. Chang, W. K. Moon, Y. H. Chou, and C. S. Huang,
“Breast ultrasound computer-aided diagnosis using BI-RADS features,”
Acad. Radiol., vol. 14, no. 8, pp. 928-939, August 2007.
C. Y. Chen, H. J. Chiou, S. Y. Chou, S. Y. Chiou, H. K. Wang, et al.,
“Computer aided diagnosis of soft tissue using sonographic
morphological and texture features,” Acad. Radiol., vol. 16, no. 12, pp.
-1538, December 2009.
S. Joo, Y. S. Yang, W. K. Moon, and H. C. Kim, “Computer-aided
diagnosis of solid breast nodules: use of an artificial neural network based
on multiple sonographic features,” IEEE Trans. Med. Imaging, vol. 23,
no. 10, pp. 1292-1300, October 2004.
K. Horsch, M. L. Giger, L. A. Venta, and C. J. Vyborny, “Automatic
segmentation of breast lesions on ultrasound,” Med. Phys., vol. 28, no. 8,
pp. 1652-1659, 2001.
K. Horsch, M. L. Giger, L. A. Venta, and C. J. Vyborny, “Computerized
diagnosis of breast lesions on ultrasound,” Med. Phys., vol. 29, no. 2, pp.
-164, 2002.
S. Poonguzhali and G. Ravindran, “A complete automatic region growing
method for segmentation of masses on ultrasound images,” in Int. Conf.
Biomed. Pharma. Eng., 2006, pp. 88-92.
J. Shan, “A fully automatic segmentation method for breast ultrasound
images,” M.S. Thesis in Computer Science, Utah State University, Logan,
Utah, 2011.
K. V. Mogatadakala, K. D. Donohue, C. W. Piccoli, and F. Forsberg,
“Detection of breast lesion regions in ultrasound images using wavelets
and order statistics,” Med. Phys., vol. 33, no. 4, pp. 840-849, March 2006.
D. R. Chen, R. F. Chang, W. J. Kuo, M. C. Chen, and Y. L. Huang,
“Diagnosis of breast tumors with sonographic texture analysis using
wavelet transform and neural networks,” Ultrasound Med. biol., vol. 28,
no. 10, pp. 1301-1310, October 2002.
K. Drukker, M. L. Giger, K. Horsch, M. A. Kupinski, C. J. Vyborny, et al.,
“Computerized lesion detection on breast ultrasound,” Med. Phys., vol.
, no. 7, pp. 1438-1446, 2002.
Y. L. Huang and D. R. Chen, “Watershed segmentation for breast tumor
in 2-D sonography,” Ultrasound Med. biol., vol. 30, no. 5, pp. 625-632,
W. J. Wu and W. K. Moon, “Ultrasound breast tumor image
computer-aided diagnosis with texture and morphological features,”
Acad. Radiol., vol. 15, no. 7, pp. 873-880, July 2008.
J. Cui, B. Sahiner, H. P. Chan, A. Nees, C. Paramagul, et al., “A new
automated method for the segmentation and characterization
of breast masses on ultrasound images,” Med. Phys., vol. 36, no. 5, pp.
-1565, May 2009.
A. K. Jumaat, “Segmentation and characterization of masses in breast
ultrasound images using parametric active contour,” Master of Science,
University Teknologi MARA, 2011.
R. F. Chang, W. J. Wu, W. K. Moon, and D. R Chen, “Automatic
ultrasound segmentation and morphology based diagnosis of solid breast
Tumors,” Breast Cancer Res. Tr., vol. 89, no. 2, pp. 179-185, January
H. D. Cheng, J. Shan, W. Ju, Y. Guo, L. Zhang, “Automated breast cancer
detection and classification using ultrasound images: a survey,” Pattern
Recogn., vol. 43, no. 1, pp. 299-317, January 2010.
J. A. Noble and D. Boukerroui, “Ultrasound image segmentation: a
survey,” IEEE Trans. Med. Imaging, vol. 25, no. 8, pp. 987-1010, 2006.
K. Saini, M. L. Dewal, and M. Rohit, “Ultrasound imaging and image
segmentation in the area of ultrasound: a review,” Int. J. Adv. Sci. Tech.,
vol. 24, pp. 41-59, November 2010.
B. Liu, H. D. Cheng, J. Huang, J. Tian, J. Liu, et al., “Automated
segmentation of ultrasonic breast lesions using statistical texture
classification and active contour based on probability distance,”
Ultrasound Med. biol., vol. 35, no. 8, pp. 1309-1324, May 2009.
A. Madabhushi, and D. N. Metaxas, “Combining low-, high-level and
empirical domain knowledge for automated segmentation of ultrasonic
breast lesions,” IEEE Trans. Med. Imaging, vol. 22, no. 2, pp. 155-169,
February 2003.
O. B. Sassi, L. Sellami, M. B. Slima, K. Chtourou, and A. B. Hamida,
“Advanced multi-scale vector field convolution dedicated to breast cancer
ultrasound images’ segmentation,” Imaging Sci. J., submitted for
publication.
M. Kass, A. Witkin, and D. Terzopoulos, “Snakes-active contour
models,” Int. J. Comput. Vision, vol. 1, no. 4, pp. 321-331, January 1987.
L. D. Cohen and I. Cohen, “Finite-element methods for active contour
models and balloons for 2-D and 3-D images,” IEEE Trans. Pattern. Anal.
Machine. Intell., vol. 15, no. 11, pp. 1131-1147, November 1993.
L. D. Cohen, “On active contour models and balloons,” CVGIP: Image
Understand, vol. 53, no. 2, pp. 211-218, March 1991.
C. Xu and J. L. Prince, “Snakes, Shapes, And Gradient Vector Flow,”
IEEE Trans. Image. Process., vol. 7, no. 3, pp. 359-369, March 1998.
B. Li and S. T. Acton, “Active contour external force using vector field
convolution for image segmentation,” IEEE Trans. Image. Process., vol.
, no. 8, pp. 2096-2106, August 2007.
R. Malladi, J. A. Sethian, and B. C. Vemuri, “Shape modeling with front
propagation: a level set approach,” IEEE Trans. Pattern. Anal. Machine.
Intell., vol.17, no. 2, pp. 158-175, February 1995.
V. Caselles, R. Kimmel, and G. Sapiro, “Geodesic active contours,” Int. J.
Comput. Vis., vol. 22, no. 1, pp. 61-79, February 1997.
S. Lankton and A. Tannenbaum, “Localizing region-based active
contours,” IEEE Trans. Image. Process., vol. 17, no. 11, pp. 2029-2039,
November 2008.
T. F. Chan and L. A. Vese, “Active contours without edges,” IEEE Trans.
Image. Process., vol. 10, no. 2, pp. 266-277, February 2001.
C. Li, C. Y. Kao, J. C. Gore, and Z. Ding, “Minimization of
region-scalable fitting energy for image segmentation,” IEEE Trans.
Image. Process., vol. 17, no. 10, pp. 1940-1949, November 2008.
O. Bernard, D. Friboulet, P. Thevenaz, and M. Unser, “Variational
B-spline level-set: a linear filtering approach for fast deformable model
evolution,” IEEE Trans. Image. Process., vol. 18, no. 6, pp. 1179-1191,
June 2009.
D. T. Kuan, A. A. Sawchuk, T. C. Strand, and P. Chavel, “Adaptive noise
smoothing filter for images with signal dependant noise,” IEEE Trans.
Pattern. Anal. Machine. Intell., vol. PAMI-7, no. 2, pp. 165-177, March
J. S. Lee, “Digital image enhancement and noise filtering by use of local
statistics,” IEEE Trans. Pattern. Anal. Machine. Intell., vol. PAMI-2, no.
, pp. 165-168, March 1980.
V. S. Frost, J. A. Stiles, K. S. Shanmugan, and J. C. Holtzman, “A model
for radar images and its application to adaptive digital filtering of
multiplicative noise,” IEEE Trans. Pattern. Anal. Machine. Intell., vol.
PAMI-4, no. 2,pp. 157–166, March 1982.
H. H. Arsenault and M. Levesque, “Combined homomorphic and local
statistics processing for restoration of image degraded by signal
dependent noise,” Appl. optics, vol. 23, no. 6, pp. 845-850, March 1984.
A. Lopes, E. Nezry, R. Touzi, and H. Laur, “Maximum a posteriori
speckle filtering and first order texture models in SAR images,” in 10th
ann. Int. Conf. Geosci. Remote Sens. Sym., 1990, pp. 2409-2412.
S. Gupta, R. C. Chauhan, and S. C. Saxena, “Locally adaptive wavelet
domain bayesian processor for denoising medical ultrasound images
using speckle modelling based on rayleigh distribution,” IEE P-Vis.
Image Sign. Process., vol. 152, no. 1, pp. 129-135, February 2005.
P. Perona and J. Malik, “Scale space and edge detection using anisotropic
diffusion,” IEEE Trans. Pattern. Anal. Machine. Intell., vol. 12, no. 7, pp.
-639, July 1990.
Y. Yu and S. T. Acton, “Speckle reducing anisotropic diffusion,” IEEE
Trans. Image. Process., vol. 11, no. 11, pp.1260-1270, November 2002.
D. P. Huttenlocher, G. A. Klanderman and W. J. Rucklidge, “Comparing
images using the hausdorff distance,” IEEE Trans. Pattern. Anal.
Machine. Intell., vol. 15, no. 9, pp.850-863, September 1993.
L. R. Dice, “Measures of the amount of ecologic association between
species,” Ecol., vol. 26, no. 3, pp.297-302, July 1945.
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