zbMATH — the first resource for mathematics

Fully automatic and segmentation-robust classification of breast tumors based on local texture analysis of ultrasound images. (English) Zbl 1187.68472
Summary: Region of interest (ROI) is a region used to extract features. In breast ultrasound (BUS) image, the ROI is a breast tumor region. Because of poor image quality (low SNR (signal/noise ratio), low contrast, blurry boundaries, etc.), it is difficult to segment the BUS image accurately and produce a ROI which precisely covers the tumor region. Due to the requirement of accurate ROI for feature extraction, fully automatic classification of BUS images becomes a difficult task. In this paper, a novel fully automatic classification method for BUS images is proposed which can be divided into two steps: “ROI generation step” and “ROI classification step”. The ROI generation step focuses on finding a credible ROI instead of finding the precise tumor location. The ROI classification step employs a novel feature extraction and classification strategy. First, some points in the ROI are selected as the “classification checkpoints” which are evenly distributed in the ROI, and the local texture features around each classification checkpoint are extracted. For each ROI, all the classification checkpoints are classified. Finally, the class of the BUS image is determined by analyzing every classification checkpoint in the corresponding ROI. Both steps were implemented by utilizing a supervised texture classification approach. The experiments demonstrate that the proposed method is very robust to the segmentation of BUS images, and very effective and useful for classifying breast tumors.

68T10 Pattern recognition, speech recognition
68U99 Computing methodologies and applications
Full Text: DOI
[1] Luo, Y.; Zhang, J.; Liu, Y.; Shaw, A.C.; Wang, X.; Wu, S.; Zeng, X.; Chen, J.; Gao, Y.; Zheng, D., Comparative proteome analysis of breast cancer and normal breast, Mol. biotechnol., 29, 233-244, (2005)
[2] Jemal, A.; Murray, T.; Ward, E.; Samuels, A.; Tiwari, R.C.; Ghafoor, A.; Feuer, E.J.; Thun, M.J., Cancer statistics, 2005, CA, Cancer J. clin., 55, 10-30, (2005)
[3] Bothorel, S.; Meunier, B.B.; Muller, S.A., Fuzzy logic based approach for semilogical analysis of microcalification in mammographic images, Int. J. intell. systems, 12, 819-848, (1997)
[4] Kaul, K.; Daguilh, F.M., Early detection of breast cancer: is mammography enough?, Hosp. Physician, 9, 49-55, (2002)
[5] Paci, E., Mammography and beyond: developing technologies for the early detection of breast cancer, Breast cancer res., 4, 123-125, (2002)
[6] Gisvold, J.J.; Martin, K.J., Prebiopsy localization of nonpalpable breast lesions, Ajr, 143, 477-481, (1984)
[7] Rosenberg, A.L.; Schwartz, G.F.; Feig, S.A.; Patchefsky, A.S., Clinically occult breast lesions: localization and significance, Radiology, 162, 167-170, (1987)
[8] Basset, L.W.; Liu, T.H.; Giuliano, A.J.; Gold, R.H., The prevalence of carcinoma in palpable vs impalpable, mammographically detected lesions (comment), Ajr, 158, 688-689, (1992)
[9] Drukker, K.; Giger, M.L.; Vyborny, C.J.; Mendelson, E.B., Computerized detection and classification of cancer on breast ultrasound, Acad. radiol., 11, 526-535, (2004)
[10] Stavros, A.T.; Thickman, D.; Rapp, C.L.; Dennis, M.A.; Parker, S.H.; Sisney, G.A., Solid breast nodules: use of sonography to distinguish between benign and malignant lesions, Radiology, 196, 123-134, (1995)
[11] Laine, H.; Rainio, J.; Arko, H.; Tukeva, T., Comparison of breast structure and findings by X-ray mammography, ultrasound, cytology and histology: a retrospective study, Eur. J. ultrasound, 2, 107-115, (1995)
[12] Jackson, V.P., The role of ultrasound in breast imaging, Radiology, 177, 305-311, (1990)
[13] 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., 28, 1301-1310, (2002)
[14] Chang, R.F.; Wu, W.J.; Moon, W.K.; Chen, D.R., Improvement in breast tumor discrimination by support vector machines and Speckle-emphasis texture analysis, Ultrasound med. biol., 29, 679-686, (2003)
[15] Huang, Y.L.; Chen, D.R., Support vector machines in sonography application to decision making in the diagnosis of breast cancer, Clin. imag., 29, 179-184, (2005)
[16] Chen, D.R.; Chang, R.F.; Chen, C.J.; Ho, M.F.; Kuo, S.J.; Chen, S.T.; Hung, S.J.; Moon, W.K., Classification of breast ultrasound images using fractal feature, Clin. imag., 29, 235-245, (2005)
[17] Garra, B.S.; Krasner, B.H.; Horii, S.C.; Ascher, S.; Mun, S.K.; Zeman, R.K., Improving the distinction between benign and malignant breast lesions: the value of sonographic texture analysis, Ultrasound imag., 15, 267-285, (1993)
[18] Zheng, K.; Wang, T.F.; Lin, J.L.; Li, D.Y., Recognition of breast ultrasound images using a hybrid method, Int. conf. complex. med. eng., 640-643, (2007)
[19] Piliouras, N.; Kalatzis, I.; Dimitropoulos, N.; Cavouras, D., Development of the cubic least squares mapping linear-kernel support vector machine classifier for improving the characterization of breast lesions on ultrasound, Comput. med. imag. graphics, 28, 247-255, (2004)
[20] Levebvre, F.; Meunier, M.; Thibault, F.; Laigier, P.; Berger, G., Computerized ultrasound B-scan characterization of breast nodules, Ultrasound med. biol., 26, 1421-1428, (2000)
[21] Sivaramakrishna, R.; Powell, K.A.; Lieber, M.L.; Chilcote, W.A.; Shekar, R., Texture analysis of lesions in breast ultrasound images, Comput. med. imag. graphics, 26, 302-307, (2002)
[22] Horsch, K.; Giger, M.L.; Venta, L.A.; Vyborny, C.J., Computerized diagnosis of breast lesions on ultrasound, Med. phys., 29, 157-164, (2002)
[23] P.S. Rodrigues, G.A. Graldi, M. Provenzano, M.D. Faria, R.F. Chang, J.S. Suri, A new methodology based on q-entropy for breast lesion classification in 3-D ultrasound images, in: Proceedings of the 28th IEEE EMBS Annual International Conference, 2006, pp. 1048-1051.
[24] A.V. Alvarenga, W.C.A. Pereira, A.F.C. Infantosi, C.M. Azevedo, Classification of breast tumours on ultrasound images using morphometric parameters, in: IEEE International Workshop on Intelligent Signal Processing, 2005, pp. 206-210.
[25] S.Y. Joo, W.K. Moon, H.C. Kim, Computer-aided diagnosis of solid breast nodules on ultrasound with digital image processing and artificial neural network, in: Proceedings of the 26 IEEE EMBS Annual International Conference, 2004, pp. 1397-1400.
[26] Seghal, C.M.; Cary, T.W.; Kangas, S.A.; Weinstein, S.P.; Schltz, S.M.; Arger, P.H.; Conant, E.F., Computer-based margin analysis of breast sonography for differentiating malignant and benign masses, J. ultrasound med., 23, 1201-1209, (2004)
[27] Sohn, C.; Hamper, U.M.; Blohmer, J.U., Breast ultrasound: A systematic approach to technique and image interpretation, (1999), Thieme New York
[28] Burckhardt, C.B., Speckle in ultrasound B-mode scans, IEEE trans. sonics ultrason., 25, 1-6, (1978)
[29] Wagner, R.F.; Smith, S.W.; Sandrik, J.M.; Lopez, H., Statistics of Speckle in ultrasound B-scans, IEEE trans. sonics ultrason., 30, 156-163, (1983)
[30] Madabhushi, A.; Metaxas, D.N., Combining low-, high-level and empirical domain knowledge for automated segmentation of ultrasonic breast lesions, IEEE trans. med. imag., 22, 155-169, (2003)
[31] Boukerroui, D.; Baskurt, A.; Noble, J.A.; Basset, O., Segmentation of ultrasound images—multiresolution 2D and 3D algorithm based on global and local statistics, Pattern recognition lett., 24, 779-790, (2003)
[32] Xiao, G.F.; Brady, M.; Noble, J.A.; Zhang, Y.Y., Segmentation of ultrasound B-mode images with intensity inhomogeneity correction, IEEE trans. med. imag., 21, 48-57, (2002)
[33] Abolmaesumi, P.; Sirouspour, M.R., An interacting multiple model probabilistic data association filter for cavity boundary extraction from ultrasound images, IEEE trans. med. imag., 23, 772-784, (2004)
[34] Huang, Y.L.; Chen, D.R., Watershed segmentation for breast tumor in 2-D sonography, Ultrasound med. biol., 30, 625-632, (2004)
[35] Chen, C.M.; Chou, Y.H.; CHEN, C.S.K., Cell-competition: a new segmentation algorithm for multiple objects with irregular boundaries in ultrasound images, Ultrasound med. biol., 31, 1647-1664, (2005)
[36] Wu, H.M.; Lu, H.H., Iterative sliced inverse regression for segmentation of ultrasound and MR images, Pattern recognition, 40, 3492-3502, (2007) · Zbl 1122.68720
[37] Chen, D.R.; Chang, R.F.; Wu, W.J.; Moon, W.K.; Wu, W.L., 3-D breast ultrasound segmentation using active contour model, Ultrasound med. biol., 29, 1017-1026, (2003)
[38] 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, 1571-1581, (2003)
[39] Noble, J.A.; Boukerroui, D., Ultrasound image segmentation: a survey, IEEE trans. med. imag., 25, 987-1010, (2006)
[40] Frank, F.M.J.; Thijssen, J.M., Characterization of echographic image texture by cooccurrence matrix parameters, Ultrasound med. biol., 23, 559-571, (1997)
[41] B. Liu, H.D. Cheng, J. Huang, J. Tian, J. Liu, X. Tang, Automated segmentation of ultrasonic breast lesions using statistical texture classification and active contour based on probability distance, Ultrasound Med. Biol., in press.
[42] Cheng, H.D.; Li, J.G., Fuzzy homogeneity and scale space approach to color image segmentation, Pattern recognition, 35, 373-393, (2002)
[43] Pal, S.K.; Majumder, D., Fuzzy mathematical approach to pattern recognition, (1986), Wiley New York · Zbl 0603.68091
[44] Gonzales, R.C.; Woodz, R.E., Digital image processing, (2002), Prentice-Hall Englewood Cliffs, NJ
[45] Cheng, H.D.; Shi, X.J.; Min, R.; Hu, L.M.; Cai, X.P.; Du, H.N., Approaches for automated detection and classification of masses in mammograms, Pattern recognition, 39, 646-668, (2006)
[46] Haralick, R.M.; Shanmugam, H.K.; Dinstein, I., Texture parameters for image classification, IEEE trans. systems man cybernet., 3, 610-621, (1973)
[47] Vapnik, V., The nature of statistical learning theory, (1995), Springer New York · Zbl 0833.62008
[48] Vapnik, V., Statistical learning theory, (1998), Wiley-Interscience New York · Zbl 0935.62007
[49] Y. Guo, H.D. Cheng, J. Tian, Y. Zhang, A novel approach to breast ultrasound image segmentation based on the characteristics of breast tissue and particle swarm optimization, in: 11th Joint Conference on Information Science, 2008.
This reference list is based on information provided by the publisher or from digital mathematics libraries. Its items are heuristically matched to zbMATH identifiers and may contain data conversion errors. It attempts to reflect the references listed in the original paper as accurately as possible without claiming the completeness or perfect precision of the matching.