A Two-Step Segmentation Method for Breast Ultrasound Masses Based on Multi-resolution Analysis

Ultrasound in Medicine and Biology

Volumn 41, Issue 6, 2015, Pages 1737-1748

  Rodrigues, Rafael ^a   Braz, Rui ^a   Pereira, Manuela ^a   Moutinho, José ^a   Pinheiro, Antonio M G ^a  

^a UNIVERSITY OF BEIRA INTERIOR   (Portugal)

Author keywords

Breast ultrasound; Image analysis; Image segmentation; Scale space analysis

Indexed keywords

ADAPTIVE BOOSTING; DISCRIMINANT ANALYSIS; IMAGE ANALYSIS; MEDICAL IMAGING; PIXELS; STRUCTURAL ANALYSIS; ULTRASONIC APPLICATIONS;

BREAST CANCER DETECTION; BREAST ULTRASOUND; BREAST ULTRASOUND IMAGES; INITIAL SEGMENTATION; SCALE-SPACE ANALYSIS; SEGMENTATION METHODS; SEGMENTATION RESULTS; SIMILARITY COEFFICIENTS;

IMAGE SEGMENTATION;

ACCURACY; ALGORITHM; ANALYTIC METHOD; ARTICLE; AUTOMATION; BREAST EXAMINATION; CLASSIFIER; DIGITAL FILTERING; DISCRIMINANT ANALYSIS PIXEL CLASSIFICATION; ECHOMAMMOGRAPHY; HUMAN; IMAGE PROCESSING; PRIORITY JOURNAL; SUPPORT VECTOR MACHINE; TWO STAGE BREAST MASS SEGMENTATION; AUTOMATED PATTERN RECOGNITION; BREAST TUMOR; COMPUTER ASSISTED DIAGNOSIS; ECHOGRAPHY; FEMALE; PROCEDURES; REPRODUCIBILITY;

ALGORITHMS; BREAST NEOPLASMS; FEMALE; HUMANS; IMAGE INTERPRETATION, COMPUTER-ASSISTED; IMAGE PROCESSING, COMPUTER-ASSISTED; PATTERN RECOGNITION, AUTOMATED; REPRODUCIBILITY OF RESULTS; ULTRASONOGRAPHY, MAMMARY;

EID: 84929508416     PISSN: 03015629     EISSN: 1879291X     Source Type: Journal    
DOI: 10.1016/j.ultrasmedbio.2015.01.012     Document Type: Article

References (36)

1. Boukerroui D., Baskurt A., Noble J., Basset O. Segmentation of ultrasound images: Multiresolution 2D and 3D algorithm based on global and local statistics. Pattern Recog Lett 2003, 24:779-790.
2. Boukerroui D., Basset O., Guerin N., Baskurt A. Multiresolution texture based adaptive clustering algorithm for breast lesion segmentation. Eur J Ultrasound 1998, 8:135-144.
3. Chan T., Vese L. Active contours without edges. IEEE Trans Image Processing 2001, 10:266-277.
4. Chang R.F., Wu W.J., Moon K., Chen D.R. Automatic ultrasound segmentation and morphology based diagnosis of solid breast tumors. Breast Cancer Res Treat 2005, 89:179-185.
5. Chang WL, Chang CC, Chen WM. Computer-aided diagnosis system for variance estimation of 3D ultrasonography based on Gabor filter. In: Proceedings, 2010 International Conference on Biomedical Engineering and Computer Science (ICBECS), 2010:1-4.
6. Chen D.R., Chang R.F., Huang Y.L. Computer-aided diagnosis applied to us of solid breast nodules by using neural networks. Radiology 1999, 213:407-412.
7. Chen D.R., Chang R.F., Kuo W.J., Chen M.C., Huang Y.L. Diagnosis of breast tumors with sonographic texture analysis using wavelet transform and neural networks. Ultrasound Med Biol 2002, 28:1301-1310.
8. Cheng H., Shan J., Ju W., Guo Y., Zhang L. Automated breast cancer detection and classification using ultrasound images: A survey. Pattern Recog 2010, 43:299-317.
9. DeSantis C., Center M.M., Siegel R., Jemal A. Breast cancer facts & figures2009-2010 2010, American Cancer Society, Atlanta, GA, USA.
10. Drukker K., Giger M.L., Horsch K., Kupinski M.A., Vyborny C.J., Mendelson E.B. Computerized lesion detection on breast ultrasound. Med Phys 2002, 29:1438-1446.
11. Freund Y., Schapire R.E. Ashort introduction to boosting. JJpn Soc Artif Intell 1999, 14:771-780.
12. Horsch K., Giger M.L., Venta L.A., Vyborny C.J. Computerized diagnosis of breast lesions on ultrasound. Med Phys 2002, 29:157-164.
13. Huang Y.L., Chen D.R. Watershed segmentation for breast tumor in 2-D sonography. Ultrasound Med Biol 2004, 30:625-632.
14. Huang Y.L., Jiang Y.R., Chen D.R., Moon W. Level set contouring for breast tumor in sonography. JDigital Imaging 2007, 20:238-247.
15. Izquierdo E, Ghanbari M. Texture smoothing and object segmentation using feature-adaptive weighted gaussian filtering. In: SBT/IEEE International Telecommunications Symposium, ITS '98 Proceedings, Sao Paolo, Brazil, 9-13 August 1998. New York: IEEE, 1998;2:650-655.
16. Joo S., Yang Y.S., Moon W.K., Kim H.C. Computer-aided diagnosis of solid breast nodules: Use of an artificial neural network based on multiple sonographic features. IEEE Trans Med Imaging 2004, 23:1292-1300.
17. Jumaat A, Rahman W, Ibrahim A, Mahmud R. Segmentation and characterization of masses in breast ultrasound images using active contour. In: 2011 IEEE International Conference on Signal and Image Processing Applications (ICSIPA), Kuala Lumpur, Malaysia, 16-18 November 2011. New York: IEEE, 2011:404-409.
18. Liu B., Cheng H.D., 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. Pattern Recog 2010, 43:280-298.
19. Madabhushi A., Metaxas D.N. Combining low-, high-level and empirical domain knowledge for automated segmentation of ultrasonic breast lesions. IEEE Trans Med Imaging 2003, 22:155-169.
20. McLachlan G. Discriminant analysis and statistical pattern recognition 1992, Wiley, New York.
21. Noble J.A., Boukerroui D. Ultrasound image segmentation: a survey. IEEE Trans Med Imaging 2006, 25:987-1010.
22. Perona P., Malik J. Scale-space and edge detection using anisotropic diffusion. IEEE Trans Pattern Anal Mach Intell 1990, 12:629-639.
23. Sant M., Francisci S., Capocaccia R., Verdecchia A., Allemani C., Berrino F. Time trends of breast cancer survival in Europe in relation to incidence and mortality. Int J Cancer 2006, 119:2417-2422.
24. Sarpe AI. Image segmentation with clustering k-means and watershed transform. In: Proceedings, 2010 Second International Conference on Advances in Multimedia (MMEDIA), Athens, Greece, 13-19 June 2010. New York: IEEE, 2010:13-17.
25. Sehgal C., Weinstein S., Arger P., Conant E. Areview of breast ultrasound. JMammary Gland Biol Neoplasia 2006, 11:113-123.
26. Shan J., Cheng H., Wang Y. Completely automated segmentation approach for breast ultrasound images using multiple-domain features. Ultrasound Med Biol 2012, 38:262-275.
27. Shyu J.J., Pei S.C., Huang Y.D. Design of variable two-dimensional fir digital filters by McClellan transformation. IEEE Trans Circuits Syst I Regul Pap 2009, 56:574-582.
28. Smola A.J., Schölkopf B. Atutorial on support vector regression. Statistics Computing 2004, 14:199-222.
29. Soares I, Castelo-Branco M, Pinheiro A. Curvature detection and segmentation of retinal exudates. In: Proceedings, 2012 9th IEEE International Symposium on Biomedical Imaging (ISBI), Barcelona, Spain, 2-5 May 2012. New York: IEEE, 2012:1719-1722.
30. Su Y., Wang Y., Jiao J., Guo Y. Automatic detection and classification of breast tumors in ultrasonic images using texture and morphological features. Open Med Inform J 2011, 5:26-37.
31. Viola P, Jones M. Rapid object detection using a boosted cascade of simple features. In: Proceedings of the 2001 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, Kauai, Hawaii, USA, 8-14 December 2001. New York: IEEE, 2001;1:I-511-I-518 vol.1.
32. Voci F., Eiho S., Sugimoto N., Sekibuchi H. Estimating the gradient in the Perona-Malik equation. IEEE Signal Process Mag 2004, 21:39-65.
33. Xiao G., Brady M., Noble J., Zhang Y. Segmentation of ultrasound B-mode images with intensity inhomogeneity correction. IEEE Trans Med Imaging 2002, 21:48-57.
34. Yap M., Edirisinghe E., Bez H. Anovel algorithm for initial lesion detection in ultrasound breast images. JAppl Clin Med Phys 2008, 9:2741.
35. Yeh C.K., Chen Y.S., Fan W.Z., Chen W.S. Anovel method for automatic contour extraction of ultrasonic breast lesions. Proc IEEE Ultrason Symp 2005, 2:1384-1387.
36. Zhang L, Ren Y, Huang C, Liu F. A novel automatic tumor detection for breast cancer ultrasound images. In: Proceedings, 2011 IEEE Eighth International Conference on Fuzzy Systems and Knowledge Discovery (FSKD), Shanghai, China, 26-28 July 2011. 2011;1:401-404.