在乳房攝影，複雜的形態特徵及模糊的邊緣，使腫瘤的判斷成為一項艱鉅且辛苦的工作。因此，電腦輔助診斷系統因應而生，提供初步意見予放射科醫師。這些電腦輔助診斷系統僅能做為參考，最終的結果必須由放射科醫師來決斷。在本論文中，應用一些不同尺寸視窗的特徵以利於增強腫瘤判斷的效能。
在腫瘤偵測系統中，分為四個步驟。首先，本研究僅著重在乳房上的判斷，因此先除去胸大肌。因為胸大肌在紋理特徵上與腫瘤有些相似之處，去除胸大肌可以減少一些偽陽率。第二步，利用階層式的模板比對方法來找尋可疑的區域。根據可疑區域的大小，定義出適應性的矩形感興趣區域 (ROI)。接著，將ROI進行光密度的轉換，找出包含Weber局部描述特徵、局部二元描述特徵及Haralick紋理特徵，共86個描述此ROI的特徵。最後，利用兩種不同的分類器，包含支持向量機和逐步方法之線性區別分析，找出向量機模型及區別函數來區分腫瘤與正常組織。
從數位乳房攝影資料庫中找出92個案例來進行實驗。當靈敏度$96.2\%$時每張影像的偽陽數為4.4個，而ROC曲線下的面積為$0.966pm0.006$。實驗結果顯示，本論文所提出的腫瘤偵測方法效能是令人滿意的而且在靈敏度及每張影像的偽陽數間取得平衡。

The mass detection of mammograms is a difficult and exhausting work owing to complicated morphological characteristics and ambiguous margins. Therefore, the Computer-Aided Detection (CAD) systems are developed to resolve the situation, which provide opinions to radiologists. The system acts as a second reader while the radiologists make the final judgment. In the thesis, some features with different sized windows are applied to improve the performance of mass detection.
The proposed algorithm consists of four steps. First, the pectoral muscle is removed to focus on the breast region. Because texture characteristics of masses are similar to the pectoral muscle, false positives can be reduced when the pectoral muscle is eliminated. Second, a hierarchical template matching method is used to find the suspicious areas. Adaptive square regions of interest (ROIs) are extracted according to sizes of suspicious areas. Then, in total of 86 descriptors including Weber local descriptors, local binary patterns and Haralick's texture features are introduced to describe the characteristics of each ROI after optical density transformation. Finally, two kinds of classifiers, SVM and linear discriminant analysis (LDA) based on stepwise method, are utilized to determine the model and discriminant function to distinguish between masses and normal regions.
In the proposed experiment, 92 cases from the Digital Database for Screening Mammography are conducted. The sensitivity is $96.2\%$ with 4.4 false positives per image and the area under ROC curve is $0.966pm0.006$. The results show that the proposed algorithm of mass detection achieves satisfactory performance and preferable compromises between sensitivity and false positives per image.

[1] “Image Retrieval in Medical Applications.”[Online]. Available: http://irma-project.org.
[2] “NCI Cancer Fact Sheets. (2012).”[Online]. Available: http://www.cancer.gov/cancertopics/types/breast
[3] “WHO Cancer Fact Sheets. (2012).” [Online]. Available: http://www.who.int/mediacentre/factsheets/fs297/en/index.html
[4] “Advances in breast imaging.” Harvard Women’s Health Watch, vol. 17, no. 9, pp. 1 – 3, 2010. [Online]. Available: http://search.ebscohost.com/login.aspx? direct=true&db=rzh&AN=2010628199&site=ehost-live
[5] I. Bankman, Ed., Handbook of Medical Image Processing and Analysis, 2008, vol. 2.
[6] M. Berbar, Y. Reyad, and M. Hussain, “Breast mass classification using statistical and local binary pattern features,”in Information Visualisation (IV), 2012 16th International Conference on, july 2012, pp. 486 –490.
[7] C. Berman,“Recent advances in breast-specific imaging,”Cancer Control, vol. 14, no. 4, pp. 338–349, October 2007.
[8] R. Boss, K. Thangavel, and D. Daniel, “Mammogram image segmentation using fuzzy clustering,” in Pattern Recognition, Informatics and Medical Engineering (PRIME), 2012 International Conference on, march 2012, pp. 290 –295.
[9] J. Bozek, M. Mustra, K. Delac, and M. Grgic, “A survey of image processing algorithms in digital mammography,” Mult. Sig. Process. and Commun, vol. 231, pp. 631–657, 2009.
[10] C. J. Burges, “A tutorial on support vector machines for pattern recognition,”Data Mining and Knowledge Discovery, vol. 2, pp. 121–167, 1998.
[11] J. Chen, S. Shan, C. He, G. Zhao, M. Pietika?inen, X. Chen, and W. Gao, “Wld: A robust local image descriptor,” Pattern Analysis and Machine Intelligence, IEEE Transactions on, vol. 32, no. 9, pp. 1705 –1720, sept. 2010.
[12] J. Chen, S. Shan, G. Zhao, X. Chen, W. Gao, and M. Pietikainen, “A robust descriptor based on weber’s law,” in Computer Vision and Pattern Recognition, 2008. CVPR 2008. IEEE Conference on, june 2008, pp. 1 –7.
[13] H. Cheng, X. Shi, R. Min, L. Hu, X. Cai, and H. Du, “Approaches for auto- mated detection and classification of masses in mammograms,” Pattern Recogni- tion, vol. 39, no. 4, pp. 646–668, June 2006.
[14] J.-Y. Choi, D. H. Kim, and Y.-M. Ro, “Combining multiresolution local binary pattern texture analysis and variable selection strategy applied to computer-aided detection of breast masses on mammograms,” in Biomedical and Health Informat- ics (BHI), 2012 IEEE-EMBS International Conference on, Jan., pp. 495–498.
[15] S. Ciatto, N. Houssami, D. Gur, R. Nishikawa, R. Schmidt, C. Metz, J. Ruiz, S. Feig, R. Birdwell, M. Linver, J. Fenton, W. Barlow, and J. Elmore, “Computer- aided screening mammography,” New England J. Med, vol. 357, no. 1, pp. 83–85, July 2007.
[16] R. W. Conners, M. M. Trivedi, and C. A. Harlow, “Segmentation of a high-resolution urban scene using texture operators,” Computer Vision, Graphics, and Image Processing, vol. 25, no. 3, pp. 273 – 310, 1984. [Online]. Available: http://www.sciencedirect.com/science/article/pii/0734189X8490197X
[17] A. Dominguez and A. Nandi, “Enhanced multi-level thresholding segmentation and rank based region selection for detection of masses in mammograms,” in Acoustics, Speech and Signal Processing, 2007. ICASSP 2007. IEEE International Conference on, vol. 1, April 2007, pp. I–449 –I–452.
[18] A. Y. El-Bastawissi, E. White, M. T. Mandelson, and S. Taplin, “Variation in mammographic breast density by race,” Annals of Epidemiology, vol. 11, no. 4, pp. 257 – 263, 2001. [Online]. Available: http://www.sciencedirect.com/science/ article/pii/S1047279700002258
[19] I. El-Naqa, Y. Yang, M. Wernick, N. Galatsanos, and R. Nishikawa, “A support vector machine approach for detection of microcalcifications,” Medical Imaging, IEEE Transactions on, vol. 21, no. 12, pp. 1552–1563, 2002.
[20] N. Eltonsy, G. Tourassi, and A. Elmaghraby, “A concentric morphology model for the detection of masses in mammography,” Medical Imaging, IEEE Transactions on, vol. 26, no. 6, pp. 880 –889, June 2007.
[21] R. C. Gonzalez and R. E. Woods, Digital Image Processing, 3rd ed. Prentice Hall, 2008.
[22] J. S. Grove, M. J. Goodman, F. Gilbert, and D. Glyde, “Factors associated with breast structure in breast cancer patients,” Cancer, vol. 43, no. 5, pp. 1895–1899, 1979. [Online]. Available: http://dx.doi.org/10.1002/1097-0142(197905)43:5(1895::AID-CNCR2820430546)3.0.CO;2-R
[23] D. Gur,“Digital mammography: Do we need to convert now?1,” Radiology, vol. 245, no. 1, pp. 10–11, October 2007. [Online]. Available: http://radiology.rsna.org/content/245/1/10.short
[24] I. Guyon and A. Elisseeff, “An introduction to variable and feature selection,” J. Mach. Learn. Res., vol. 3, pp. 1157–1182, Mar. 2003. [Online]. Available: http://dl.acm.org/citation.cfm?id=944919.944968
[25] R. M. Haralick, K. Shanmugam, and I. Dinstein, “Textural features for image classification,” Systems, Man and Cybernetics, IEEE Transactions on, vol. SMC-3, no. 6, pp. 610 –621, nov. 1973.
[26] B. L. Hart, R. T. Steinbock, F. A. Mettler, D. R. Pathak, and S. A. Bartow, “Age and race related changes in mammographic parenchymal patterns,” Cancer, vol. 63, no. 12, pp. 2537–2539, 1989. [Online]. Available: http://dx.doi.org/10.1002/1097-0142(19890615)63:12(2537::AID-CNCR2820631230)3.0.CO;2-0
[27] M. Heath, K. Bowyer, D. Kopans, R. Moore, and P. K. Jr., “The digital database for screening mammography,” 2000.
[28] L. Ke, Y. Chen, N. Li, and Y. Kang624690, “Infiltrative breast cancer initial detection based on double-scale sech template matching,” in Information and Au- tomation (ICIA), 2012 International Conference on, june 2012, pp. 887 –890.
[29] I. Kitanovski, B. Jankulovski, I. Dimitrovski, and S. Loskovska, “Comparison of feature extraction algorithms for mammography images,” in Image and Signal Processing (CISP), 2011 4th International Congress on, vol. 2, Oct., pp. 888–892.
[30] R. Kohavi, “A study of cross-validation and bootstrap for accuracy estimation and model selection.” Morgan Kaufmann, 1995, pp. 1137–1143.
[31] G. Kom, A. Tiedeu, and M. Kom, “Automated detection of masses in mam- mograms by local adaptive thresholding,” Computers in Biology and Medicine, vol. 37, no. 1, pp. 37–48, January 2007.
[32] C. H. Lee, “Screening mammography: proven benefit, continued controversy.”Radiol. Clinics North Am, vol. 40, no. 3, pp. 395–407, May 2002.
[33] X. Llad´o, A. Oliver, J. Freixenet, R. Mart´ı, and J. Mart´ı, “A textural approach for mass false positive reduction in mammography,” Computerized Medical Imaging and Graphics, vol. 33, no. 6, pp. 415 – 422, 2009. [Online]. Available: http://www.sciencedirect.com/science/article/pii/S089561110900038X
[34] C. E. Metz, “Basic principles of roc analysis,” Seminars in Nuclear Medicine, vol. 8, no. 4, pp. 283 – 298, 1978. [Online]. Available: http://www.sciencedirect.com/science/article/pii/S0001299878800142
[35] A. C. of Radiology, ACR BI-RADS–Mammography, Ultrasound & Magnetic Res- onance Imaging. 4th ed. American College of Radiology (Reston, VA, 2003).
[36] T. Ojala, M. Pietik¨ainen, and D. Harwood, “A comparative study of texture measures with classification based on featured distributions,” Pattern Recognition, vol. 29, no. 1, pp. 51 – 59, 1996. [Online]. Available: http://www.sciencedirect.com/science/article/pii/0031320395000674
[37] T. Ojala, M. Pietik¨ainen, and T. M¨aenp¨a¨a, “A generalized local binary pattern operator for multiresolution gray scale and rotation invariant texture classifica- tion,” in in Proc. of Second Inter. Conf. on Advances in Pattern Recognition, Rio de Janeiro, 2001, pp. 397–406.
[38] E. D. Pisano, C. Gatsonis, E. Hendrick, M. Yaffe, J. K. Baum, S. Acharyya, E. F. Conant, L. L. Fajardo, L. Bassett, C. D’Orsi, R. Jong, and M. Rebner, “Diagnostic performance of digital versus film mammography for breast-cancer screening,” New England J. Med, vol. 353, no. 17, pp. 1773–1783, October 2005.
[39] E. D. Pisano, C. A. Gatsonis, M. J. Yaffe, R. E. Hendrick, A. N. A. Tosteson, D. G. Fryback, L. W. Bassett, J. K. Baum, E. F. Conant, R. A. Jong, M. Rebner, and C. J. D’Orsi, “American college of radiology imaging network digital mammographic imaging screening trial: Objectives and methodology,” Radiology, vol. 236, no. 2, pp. 404–412, 2005. [Online]. Available: http://radiology.rsna.org/content/236/2/404.abstract
[40] R. M. Rangayyana, F. bio J. Ayresa, and J. L. Desautels, “A review of computer- aideddiagnosis of breast cancer: Toward the detection of subtle signs,” Journal of the Franklin Institute, vol. 344, no. 3-4, pp. 312–348, July 2007.
[41] B. Sahiner, H.-P. Chan, N. Petrick, R. F. Wagner, and L. M. Hadjiiski, “Stepwise linear discriminant analysis in computer-aided diagnosis: the effect of finite sample size,” pp. 499–510, 1999. a[Online]. Available: http://dx.doi.org/10.1117/12.348606
[42] M. Sameti, R. Ward, J. Morgan-Parkes, and B. Palcic, “Image feature extraction in the last screening mammograms prior to detection of breast cancer,” Selected Topics in Signal Processing, IEEE Journal of, vol. 3, no. 1, pp. 46 –52, feb. 2009.
[43] M. P. Sampat, M. K. Markey, and A. C. Bovik, Computer-aided detection and di- agnosis in mammography, in handbook of image and video processing ed., A. C. B. 2nd, Ed., New York: Academic, 2005.
[44] R. Siegel, D. Naishadham, and A. Jemal, “Cancer statistics, 2013,” CA: A Cancer Journal for Clinicians, vol. 63, no. 1, pp. 11–30, 2013. [Online]. Available: http://dx.doi.org/10.3322/caac.21166
[45] J. S. Suri and R. M. Rangayyan, Recent advances in breast imaging, mammog- raphy, and computer-aided diagnosis of breast cancer. Society for Optical & Quantum, 2006.
[46] J. Tang, R. Rangayyan, J. Xu, I. El Naqa, and Y. Yang, “Computer-aided de- tection and diagnosis of breast cancer with mammography: Recent advances,” Information Technology in Biomedicine, IEEE Transactions on, vol. 13, no. 2, pp. 236 –251, March 2009.
[47] R. M. L. Warren and W. Duffy, “Comparison of single reading with double reading of mammograms, and change in effectiveness with experience,” British Journal of Radiology, vol. 68, no. 813, pp. 958–962, 1995. [Online]. Available: http://bjr.birjournals.org/content/68/813/958.abstract
[48] J. Wei, B. Sahiner, L. M. Hadjiiski, H.-P. Chan, N. Petrick, M. A. Helvie, M. A. Roubidoux, J. Ge, and C. Zhou, “Computer-aided detection of breast masses on full field digital mammograms,” Medical Physics, vol. 32, no. 9, pp. 2827–2838, 2005. [Online]. Available: http://link.aip.org/link/?MPH/32/2827/1
[49] X. Xianchuan and Z. Qi, “Medical image retrieval using local binary patterns with image euclidean distance,” in Information Engineering and Computer Science, 2009. ICIECS 2009. International Conference on, dec. 2009, pp. 1 –4.
[50] S. Xu and C. Pei, “Hierarchical matching for automatic detection of masses in mammograms,” in Electrical and Control Engineering (ICECE), 2011 Interna- tional Conference on, Sept., pp. 4523–4526.
[51] J. Yang and J. Guo, “Image texture feature extraction method based on regional average binary gray level difference co-occurrence matrix,” in Virtual Reality and Visualization (ICVRV), 2011 International Conference on, nov. 2011, pp. 239–242.
[52] H. J. Yoon, B. Zheng, and D. P. Chakraborty, “Evaluating computer-aided detec- tion algorithms,” Med. Phys., vol. 34, no. 6, pp. 2024–2048, June 2007.