臺灣博碩士論文加值系統

In recent years, the computer-aided diagnostic (CAD) is developed gradually providing second opinion for radiologists'' diagnosis. The CAD system should produce well segmentation result of tumor to precisely evaluate the tumor size, and the tumor can be further classified into benign and malignant by the extracted feature of shape. Concerning the previous techniques of the semi-automatic segmentation (e.g., level-set), the seed usually needs to be manually initialized at the appropriate position for the better segmentation result. Besides, most of the segmentation systems consider the detection procedure as the preceding step, and the segmentation approach is subsequently employed on the located region of interest. In this study, a fully automatic system integrating the tumor detection and segmentation steps is proposed, and a set of representative seeds are computerized for the whole image segmentation based on the level-set method. Meanwhile, the novel multi-seed mechanism assists the level set segmentation in acquiring the satisfactory result. The proposed system consists of three phases. First, a mean-shift clustering method and the affinity approach are applied to generate and extract the most representative seeds. Next, the level-set method based on the selected seeds is employed to obtain several suspected regions. Finally, the features of all the suspected regions are calculated and further analyzed by support vector machine (SVM) classifier to extract the target tumor from the normal ones. 120 cases (68 for benign cases and 52 for malignant cases) are used for evaluating the proposed system. The sensitivity is 95% (114/120) with the benign cases equal to 0.91 and the malignant cases to 1.00. In summary, the experimental results demonstrate the high efficiency and robustness of the proposed method.

1 INTRODUCTION 1
2 MATERIALS 5
2.1 Patients and Lesion Characters . . . . . . . . . . . . . . . . . . . . . . . 5
2.2 Data Acquisition . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5
3 PROPOSED METHODS 7
3.1 Seeds Extraction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 8
3.1.1 Seed Generation via Mean-shift . . . . . . . . . . . . . . . . . . 10
3.1.2 Seeds elimination . . . . . . . . . . . . . . . . . . . . . . . . . . 11
3.1.3 Representative Seed Extraction via Affinity Propagation . . . . . 14
3.2 Suspected Tumor Segmentation . . . . . . . . . . . . . . . . . . . . . . 16
3.2.1 Sigmoid Filter . . . . . . . . . . . . . . . . . . . . . . . . . . . 17
3.2.2 Gradient Magnitude Filter . . . . . . . . . . . . . . . . . . . . . 18
3.2.3 Level Set Method . . . . . . . . . . . . . . . . . . . . . . . . . . 18
3.2.4 Postprocessing with Opening Operator . . . . . . . . . . . . . . 20
3.3 Tumor Selection . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 21
3.3.1 Feature Extraction . . . . . . . . . . . . . . . . . . . . . . . . . 22
3.3.2 Support Vector Machine for Suspected Tumor Classification . . . 23
3.4 Performance Evaluation of Tumor Segmentation . . . . . . . . . . . . . 25
4 EXPERIMENTAL RESULTS AND DISCUSSION 27
4.1 Experimental Results . . . . . . . . . . . . . . . . . . . . . . . . . . . . 27
4.1.1 Tumor Detection Performance . . . . . . . . . . . . . . . . . . . 28
4.1.2 Segmentation Performance . . . . . . . . . . . . . . . . . . . . . 30
4.2 Discussion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 31
5 CONCLUSION AND FUTURE WORK 41
REFERENCES 43

[1]C. Mathers, D. M. Fat, J. T. Boerma, and World Health Organization., The global burden of disease : 2004 update. Geneva, Switzerland: World Health Organization, 2008.
[2]J. A. Noble and D. Boukerroui, "Ultrasound image segmentation: a survey," IEEE Transactions on Medical Imaging, vol. 25, pp. 987-1010, 2006.
[3]K. Drukker, M. L. Giger, C. J. Vyborny, and E. B. Mendelson, "Computerized detection and classification of cancer on breast ultrasound," Academic Radiology, vol. 11, pp. 526-535, 2004.
[4]S. Gupta, R. Chauhan, and S. Sexana, "Wavelet-based statistical approach for speckle reduction in medical ultrasound images," Medical and Biological Engineering and Computing, vol. 42, pp. 189-192, 2004.
[5]D. Boukerroui, A. Baskurt, J. A. Noble, and O. Basset, "Segmentation of ultrasound images––multiresolution 2D and 3D algorithm based on global and local statistics," Pattern Recognition Letters, vol. 24, pp. 779-790, 2003.
[6]K. Drukker, C. A. Sennett, and M. L. Giger, "Automated Method for Improving System Performance of Computer-Aided Diagnosis in Breast Ultrasound," IEEE Transactions on Medical Imaging, vol. 28, pp. 122-128, 2009.
[7]H. D. Cheng, X. H. Jiang, Y. Sun, and J. Wang, "Color image segmentation: advances and prospects," Pattern Recognition, vol. 34, pp. 2259-2281, 2001.
[8]E. Littmann and H. Ritter, "Adaptive color segmentation-a comparison of neural and statistical methods," IEEE Transactions on Medical Imaging, vol. 8, pp. 175-185, 1997.
[9]Z. Dokur and T. Olmez, "Segmentation of ultrasound images by using a hybrid neural network," Pattern Recognition Letters, vol. 23, pp. 1825-1836, 2002.
[10]M. N. Kurnaz, Z. Dokur, and T. Olmez, "An incremental neural network for tissue segmentation in ultrasound images," Computer Methods and Programs in Biomedicine, vol. 85, pp. 187-195, 2007.
[11]X. Guofang, M. Brady, J. A. Noble, and Z. Yongyue, "Segmentation of ultrasound B-mode images with intensity inhomogeneity correction," IEEE Transactions on Medical Imaging, vol. 21, pp. 48-57, 2002.
[12]J.-Z. Cheng, Y.-H. Chou, C.-S. Huang, Y.-C. Chang, C.-M. Tiu, K.-W. Chen, and C.-M. Chen, "Computer-aided US Diagnosis of Breast Lesions by Using Cell-based Contour Grouping," Radiology, vol. 255, pp. 746-754, June 2010.
[13]M. Kass, A. Witkin, and D. Terzopoulos, "Snakes: Active contour models," International Journal of Computer Vision, vol. 1, pp. 321-331, 1988.
[14]D. Cremers, M. Rousson, and R. Deriche, "A Review of Statistical Approaches to Level Set Segmentation: Integrating Color, Texture, Motion and Shape," Int. J. Comput. Vision, vol. 72, pp. 195-215, 2007.
[15]A. Sarti, C. Corsi, E. Mazzini, and C. Lamberti, "Maximum likelihood segmentation with Rayleigh distribution of ultrasound images," in Computers in Cardiology, 2004, 2004, pp. 329-332.
[16]R. Malladi, J. A. Sethian, and B. C. Vemuri, "Shape modeling with front propagation: a level set approach," Pattern Analysis and Machine Intelligence, IEEE Transactions on, vol. 17, pp. 158-175, 1995.
[17]K. Horsch, M. L. Giger, C. J. Vyborny, and L. A. Venta, "Performance of computer-aided diagnosis in the interpretation of lesions on breast sonography," Academic Radiology, vol. 11, pp. 272-280, 2004.
[18]D. Comaniciu and P. Meer, "Mean shift analysis and applications," in Computer Vision, 1999. The Proceedings of the Seventh IEEE International Conference on, 1999, pp. 1197-1203 vol.2.
[19]P. M. D. Comaniciu, "Mean Shift: A Robust Approach Toward Feature Space Analysis," IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. vol. 24, p. 17, May 2002.
[20]B. J. Frey and D. Dueck, "Clustering by Passing Messages Between Data Points," Science, vol. 315, pp. 972-976, February 16, 2007 2007.
[21]Y. Fujiwara, G. Irie, and T. Kitahara, Fast Algorithm for Affinity Propagation, 2011.
[22]S. Osher and J. A. Sethian, "Fronts propagating with curvature-dependent speed: algorithms based on Hamilton-Jacobi formulations," J. Comput. Phys., vol. 79, pp. 12-49, 1988.
[23]C.-C. Chang and C.-J. Lin, "LIBSVM: A library for support vector machines," ACM Trans. Intell. Syst. Technol., vol. 2, pp. 1-27, 2011.
[24]V. N. Vapnik, The Nature of Statistical Learning Theory: Springer, 2000.
[25]J. Deng and H. T. Tsui, "A fast level set method for segmentation of low contrast noisy biomedical images," Pattern Recognition Letters, vol. 23, pp. 161-169, 2002.
[26]A. Madabhushi and D. N. Metaxas, "Combining low-, high-level and empirical domain knowledge for automated segmentation of ultrasonic breast lesions," Medical Imaging, IEEE Transactions on, vol. 22, pp. 155-169, 2003.
[27]M. Fashing and C. Tomasi, "Mean shift is a bound optimization," Pattern Analysis and Machine Intelligence, IEEE Transactions on, vol. 27, pp. 471-474, 2005.
[28]A. Yilmaz, "Object Tracking by Asymmetric Kernel Mean Shift with Automatic Scale and Orientation Selection," in Computer Vision and Pattern Recognition, 2007. CVPR ''07. IEEE Conference on, 2007, pp. 1-6.
[29]D. Comaniciu and P. Meer, "Robust analysis of feature spaces: color image segmentation," in Computer Vision and Pattern Recognition, 1997. Proceedings., 1997 IEEE Computer Society Conference on, 1997, pp. 750-755.
[30]e. a. R. C. Gonzalez, "Digital image processing, third ed. Upper Saddle River," ed, 2008.
[31]J. A. Hartigan and M. A. Wong, "Algorithm AS 136: A K-Means Clustering Algorithm," Journal of the Royal Statistical Society. Series C (Applied Statistics), vol. 28, pp. 100-108, 1979.
[32]J. A. Sethian, Level Set Methods and Fast Marching Methods: Evolving Interfaces in Computational Geometry, Fluid Mechanics, Computer Vision, and Materials Science: Cambridge University Press, 1999.
[33]M. L. Giger, H. Al-Hallaq, Z. Huo, C. Moran, D. E. Wolverton, C. W. Chan, and W. Zhong, "Computerized analysis of lesions in US images of the breast," Academic Radiology, vol. 6, pp. 665-674, 1999.
[34]J. S. Suri, R. F. Chang, and C. Kathuria, Advances in Diagnostic and Therapeutic Ultrasound Imaging: Artech House, 2008.
[35]M. Aleman-Flores, L. Alvarez, and V. Caselles, "Texture-Oriented Anisotropic Filtering and Geodesic Active Contours in Breast Tumor Ultrasound Segmentation," J. Math. Imaging Vis., vol. 28, pp. 81-97, 2007.
[36]Y.-L. Huang and D.-R. Chen, "Watershed segmentation for breast tumor in 2-D sonography," Ultrasound in Medicine and Biology, vol. 30, pp. 625-632, 2004.
[37]J. Shan, H. D. Cheng, and Y. Wang, "Completely Automated Segmentation Approach for Breast Ultrasound Images Using Multiple-Domain Features," Ultrasound in Medicine and Biology, vol. 38, pp. 262-275, 2012.
[38]C.-M. Chen, Y.-H. Chou, C. S. K. Chen, J.-Z. Cheng, Y.-F. Ou, F.-C. Yeh, and K.-W. Chen, "Cell-competition algorithm: A new segmentation algorithm for multiple objects with irregular boundaries in ultrasound images," Ultrasound in Medicine and Biology, vol. 31, pp. 1647-1664, 2005.
[39]B. Liu, H. D. Cheng, J. Huang, J. Tian, J. Liu, and X. Tang, "Automated Segmentation of Ultrasonic Breast Lesions Using Statistical Texture Classification and Active Contour Based on Probability Distance," Ultrasound in Medicine and Biology, vol. 35, pp. 1309-1324, 2009.
[40]W. K. Moon, Y.-W. Shen, C.-S. Huang, L.-R. Chiang, and R.-F. Chang, "Computer-Aided Diagnosis for the Classification of Breast Masses in Automated Whole Breast Ultrasound Images," Ultrasound in Medicine and Biology, vol. 37, pp. 539-548, 2011.
[41]Y. Wang, S. Jiang, H. Wang, Y. H. Guo, B. Liu, Y. Hou, H. Cheng, and J. Tian, "CAD Algorithms for Solid Breast Masses Discrimination: Evaluation of the Accuracy and Interobserver Variability," Ultrasound in Medicine and Biology, vol. 36, pp. 1273-1281, 2010.
[42]Q. Zhu, S. You, Y. Jiang, J. Zhang, M. Xiao, Q. Dai, and Q. Sun, "Detecting Angiogenesis in Breast Tumors: Comparison of Color Doppler Flow Imaging With Ultrasound-Guided Diffuse Optical Tomography," Ultrasound in Medicine and Biology, vol. 37, pp. 862-869, 2011.