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局部特征与多示例学习结合的超声图像分类方法

DOI: 10.3724/SP.J.1004.2013.00861, PP. 861-867

Keywords: 图像分类,局部特征,多示例学习,超声图像

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Abstract:

?利用全局特征对超声图像进行描述具有一定的局限性,而且对图像进行手工标注的成本过高,为解决上述问题,本文提出了一种利用局部特征描述超声图像,并结合多示例学习对超声图像进行分类的新方法.粗略定位图像中的感兴趣区域(Regionofinterest,ROI),并提取局部特征,将感兴趣区域看作由局部特征构成的示例包,采用自组织映射(Self-organizingmap,SOM)的方法对示例特征进行矢量量化,采用Bagofwords方法将示例特征映射到示例包空间,进而采用传统的支持向量机对示例包进行分类.本文提出的方法在临床超声图像上进行了实验,实验结果表明,该方法具有良好的泛化能力和较高的准确性.

References

[1]  Cheng H D, Shan J, Ju W, Guo Y H, Zhang L. Automated breast cancer detection and classification using ultrasound images: a survey. Pattern Recognition, 2010, 43(1): 299-317
[2]  Moon W K, Shen Y W, Huang C S, Chiang L R, Chang R F. Computer-aided diagnosis for the classification of breast masses in automated whole breast ultrasound images. Ultrasound in Medicine and Biology, 2011, 37(4): 539-548
[3]  Sahiner B, Chan H P, Roubidoux M A, Hadjiiski L M, Helvie M A, Paramagul C, Bailey J, Nees A V, Blane C. Malignant and benign breast masses on 3D US volumetric images: effect of computer-aided diagnosis on radiologist accuracy. Radiology, 2007, 242(3): 716-724
[4]  Dietterich T G, Lathrop R H, Lozano-Perez T. Solving the multiple-instance problem with axis-parallel rectangles. Artificial Inteligence, 1997, 89(1-2): 31-71
[5]  Garcia S, Derrac J, Cano J R, Herrera F. Prototype selection for nearest neighbor classification: taxonomy and empirical study. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2012, 34(3): 417-435
[6]  Zhu L, Zhao B, Gao Y. Multi-class multi-instance learning for lung cancer image classification based on bag feature selection. In: Proceedings of the 5th International Conference on Fuzzy Systems and Knowledge Discovery. Shandong, China: IEEE, 2008. 487-492
[7]  Li J, Xu D, Gao W. Removing label ambiguity in learning-based visual saliency estimation. IEEE Transactions on Image Processing, 2012, 21(4): 1513-1525
[8]  Ding J R, Cheng H D, Ning C P, Huang J H, Zhang Y T. Quantitative measurement for thyroid cancer characterization based on elastography. Journal of Ultrasound in Medicine, 2011, 30(9): 1259-1266
[9]  Wang J, Zucker J D. Solving the multiple-instance problem: a lazy learning approach. In: Proceedings of the 17th International Conference on Machine Learning. San Francisco: Morgan Kaufmann, 2000. 1119-1126
[10]  Li F F, Perona P. A Bayesian hierarchical model for learning natural scene categories. In: Proceedings of the 2005 IEEE Computer Vision and Pattern Recognition. San Diego, CA, USA: IEEE, 2005. 524-531
[11]  Chen D R, Lai H W. Three-dimensional ultrasonography for breast malignancy detection. Expert Opinion on Medical Diagnostics, 2011, 5(3): 253-261
[12]  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. Ultrasonic Imaging, 1993, 15(4): 267-285
[13]  Ren J C. ANN vs. SVM: which one performs better in classification of MCCs in mammogram imaging. Knowledge-Based Systems, 2012, 26: 144-153
[14]  Wagner R F, Smith S W, Sandrik J M, Lopez H. Statistics of speckle in ultrasound B-scans. IEEE Transactions on Sonics and Ultrasonics, 1983, 30(3): 156-163
[15]  Liu B, Cheng H D, Huang J H, Tian J W, Tang X L, Liu J F. Fully automatic and segmentation-robust classification of breast tumors based on local texture analysis of ultrasound images. Pattern Recognition, 2010, 43(1): 280-298
[16]  Bergeron C, Moore G, Zaretzki J, Breneman C M, Bennett K P. Fast bundle algorithm for multiple-instance learning. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2012, 34(6): 1068-1079
[17]  Zhou Z H, Zhang M L. Solving multi-instance problems with classifier ensemble based on constructive clustering. Knowledge and Information Systems, 2007, 11(2): 155-170
[18]  Zhang Q, Goldman S A. EM-DD: an improved multi-instance learning technique. In: Proceedings of the 2002 Neural Information Processing Systems. Cambridge: MIT Press, 2002, 14: 1073-1080
[19]  Weidmann N, Frank E, Pfahringer B. A two-level learning method for generalized multi-instance problems. In: Proceedings of the 2003 European Conference Machine Learning. Cavtat-Dubrovnik, Croatia. 2003. 468-479

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