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研究生： 陳姿秀
Chen, Zih-Siou
論文名稱： 高動態範圍影像的色調再現應用於自然影像與乳房X光影像之研究
A Study of Tone Reproduction for High Dynamic Range Images with Applications to Natural Images and Mammograms
指導教授： 戴顯權
Tai, Shen-Chuan
學位類別： 博士
Doctor
系所名稱： 電機資訊學院 - 電腦與通信工程研究所
Institute of Computer & Communication Engineering
論文出版年： 2015
畢業學年度： 104
語文別： 英文
論文頁數： 118
中文關鍵詞： 高動態範圍影像 、色調再現 、中心環繞retinex 、乳房X光影像 、電腦輔助偵測系統 、光密度影像
外文關鍵詞： high dynamic range images, tone reproduction, center-surround retinex, mammograms, computer-aided detection system, optical density image
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    • 本論文研究內容包含兩種色調再現的方法，其中一個提出的目的是為了具有高動態範圍自然影像的顯示，另一個是用於改善高動態範圍乳房X光影像上的病灶檢出率。第一個色調再現技術是基於retinex理論，用來將高動態範圍影像呈現在8位元深度的顯示裝置上。本論文對於人類視覺提出修正的中心環繞retinex理論，並且基於此理論基礎創建出一個區域色調運算子，用來調整影像的區域對比， 中心環繞retinex理論的校正是藉由本論文提出的3區直方圖機制來清楚地定義表觀反射率，進而結合全域與區域資訊以建立光照圖。該演算法的優點在於只使用單一尺度的中心環繞retinex運算子，便可得到整體對比度和區域細節平衡的呈現， 實驗結果顯示，提出的演算法在處理各種高動態範圍影像具有強健性，並且產生的影像在視覺上是令人滿意的。此外，本論文提出另一個色調再現的方法，光密度影像，用來幫助電腦輔助偵測系統提升乳房X光影像上乳癌病灶的檢出率。本論文提出了兩個乳房X光的電腦輔助偵測系統，在系統中將感興趣區域轉換成光密度影像，用來檢測腫塊與微鈣化。結果顯示，這兩個系統具有高靈敏度與高特異性並且達到令人滿意的效能， 並且，實驗結果也證實，本論文提出的光密度影像有利於提高偵測性能。

    This thesis proposes two tone reproduction approaches, one for the displaying of natural high dynamic range (HDR) images and another for improving the detection rate of lesions in HDR mammograms. The first proposed tone reproduction technique is based on retinex theory to render HDR images with 8 bit-depth. A local tone operator is constructed to adjust the local contrast of images according to the proposed corrected center-surround retinex theory for human visual system. The center-surround retinex theory is corrected by combining global and local information to build an illumination map with a proposed 3-zone histogram scheme that clarifies apparent reflectance.
    The merit of the proposed algorithm lies in the balanced presentation of both contrast and details in an image produced by a single scale center-surround retinex operator. Experimental results show that the proposed algorithm is robust for processing various HDR images and the resulting images are visually pleasing. Moreover, optical density image, another tone reproduction approach, is proposed to help computer-aided detection (CADe) systems improve the detection rate of breast cancer lesions in HDR mammograms. This thesis proposed two mammographic CADe systems with transforming a region of interest (ROI) into an optical density image for mass and micro-calcifications (MC) detection. Experiments show that the proposed systems both achieve high sensitivity with high specificity and delivers satisfactory performance. Furthermore, the experimental results also prove that the optical density image helps to improve the detection performance.

    中文摘要. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . i Abstract . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . ii Acknowledgements . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . iv Contents . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . vi List of Tables . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . ix List of Figures . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . x List of Abbreviations . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . xiv List of Symbols . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . xviii 1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1 2 Background . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3 2.1 Natural High Dynamic Range Images . . . . . . . . . . . . . . . . . . . 3 2.1.1 Dynamic Range of Scenes and Displays . . . . . . . . . . . . . . 3 2.1.2 Tone Reproduction . . . . . . . . . . . . . . . . . . . . . . . . . 3 2.2 High Dynamic Range Mammograms . . . . . . . . . . . . . . . . . . . 5 2.2.1 Mammograms . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5 2.2.2 Mammographic Computer Aided Detection Systems . . . . . . . 7 3 Related Works . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 10 3.1 Global Tone Reproduction Methods . . . . . . . . . . . . . . . . . . . . 10 3.2 Local Tone Reproduction Methods . . . . . . . . . . . . . . . . . . . . 10 3.2.1 Center-surround Retinex Model . . . . . . . . . . . . . . . . . . 12 3.3 Related Technologies for Mammographic Computer Aided Detection . . 14 3.3.1 Suspicious Region Detection . . . . . . . . . . . . . . . . . . . . 14 3.3.2 Feature Extraction Methods . . . . . . . . . . . . . . . . . . . . 16 3.3.3 Classification Methods . . . . . . . . . . . . . . . . . . . . . . . 19 3.4 Computer Aided Detection Systems for Mammograms . . . . . . . . . . 20 3.4.1 Mass Detection . . . . . . . . . . . . . . . . . . . . . . . . . . . 20 3.4.2 Micro-calcification Detection . . . . . . . . . . . . . . . . . . . . 21 4 The Retinex-based Tone Reproduction Method for High Dynamic Range Images 23 4.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 23 4.2 Luminance Extraction . . . . . . . . . . . . . . . . . . . . . . . . . . . 25 4.3 Global Adaptation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 25 4.4 Local Tone Reproduction . . . . . . . . . . . . . . . . . . . . . . . . . . 26 4.5 Histogram Scaling . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 30 4.6 Color Restoration . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 32 4.7 Results . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 32 4.7.1 Objective Assessment . . . . . . . . . . . . . . . . . . . . . . . . 34 4.7.2 Subjective Assessment . . . . . . . . . . . . . . . . . . . . . . . 40 4.7.3 Other Applications . . . . . . . . . . . . . . . . . . . . . . . . . 41 4.8 Discussions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 45 4.9 Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 47 5 Automatic Computer Aided Detections for High Dynamic Range Mammograms with Tone Reproduction through Optical Density Image . . . . . . . . 48 5.1 Optical Density Image for Regions of Interest in High Dynamic Range Mammograms . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 48 5.2 The Mass Detection System . . . . . . . . . . . . . . . . . . . . . . . . 50 5.2.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 50 5.2.2 Preprocessing . . . . . . . . . . . . . . . . . . . . . . . . . . . . 51 5.2.3 Detection of Suspicious Mass Regions . . . . . . . . . . . . . . . 55 5.2.4 Feature Extraction Module . . . . . . . . . . . . . . . . . . . . . 58 5.2.5 Classification . . . . . . . . . . . . . . . . . . . . . . . . . . . . 59 5.2.6 Results . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 59 5.2.7 Discussions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 74 5.2.8 Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 75 5.3 The Micro-calcification Detection System . . . . . . . . . . . . . . . . . 76 5.3.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 76 5.3.2 Preprocessing . . . . . . . . . . . . . . . . . . . . . . . . . . . . 77 5.3.3 Detection of Suspicious Micro-calcification Regions . . . . . . . 79 5.3.4 Feature Extraction Module . . . . . . . . . . . . . . . . . . . . . 83 5.3.5 Classification . . . . . . . . . . . . . . . . . . . . . . . . . . . . 84 5.3.6 Results . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 86 5.3.7 Discussions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 94 5.3.8 Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 95 5.4 Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 95 6 Conclusions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 97 References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 99 Vita . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 114 Human Study Approval . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 115 List of Publications . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 116 研究計劃案. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 118

    [1] E. Alanis-Reyes, J. Hernandez-Cruz, J. Cepeda, C. Castro, H. Terashima-Marin, and S. Conant-Pablos, “Analysis of machine learning techniques applied to the classification of masses and microcalcification clusters in breast cancer computer-aided detection,” Journal of Cancer Therapy, vol. 3, pp. 1020-1028, 2012.
    [2] S. M. Astley, “Computer-based detection and prompting of mammographic abnormalities,” British Journal of Radiology, vol. 77, no. suppl 2, pp. S194-S200, 2004. [Online]. Available: http://bjr.birjournals.org/content/77/suppl 2/S194.abstract
    [3] T. O. Aydin, R. Mantiuk, K. Myszkowski, and H.-P. Seidel, “Dynamic range independent image quality assessment,” ACM Transactions on Graphics, vol. 27, no. 3, pp. 69:1-69:10, Aug. 2008. [Online]. Available: http://doi.acm.org/10.1145/1360612.1360668
    [4] K.-H. Bae and B. K. Park, “Compact approach for high dynamic range imaging in mobile digital camera,” in IEEE International Conference on Consumer Electronics, Jan 2015, pp. 339-342.
    [5] N. Bansal and S. Raman, “Regularized tone mapping using edge preserving filters,” in 2015 Twenty First National Conference on Communications, Feb 2015,pp. 1-6.
    [6] K. Barnard and B. Funt, “Investigations into multi-scale retinex,” in Color Imaging in Multimedia. Technology, Wiley, 1999, pp. 9-17.
    [7] R. Bellotti, F. De Carlo, S. Tangaro, G. Gargano, G. Maggipinto, M. Castellano, R. Massafra, D. Cascio, F. Fauci, G. Magro, R .and Raso, G. Lauria, A.and Forni, S. Bagnasco, P. Cerello, E. Zanon, S. Cheran, E. Lopez Torres, U. Bottigli, G. Masala, P. Oliva, M. Retico, A .and Fantacci, R. Cataldo, I. De Mitri,
    and G. De Nunzio, “A completely automated cad system for mass detection in a large mammo- graphic database.” Medical Physics, vol. 33, no. 8, pp. 3066-3075, August 2006.
    [8] M. Bevk and I. Kononenko, “A statistical approach to texture description of medical images: a preliminary study,” in 15th IEEE Symposium on Computer-BasedMedical Systems (CBMS), 2002, pp. 239-244.
    [9] V. Bhateja and S. Devi, “A novel framework for edge detection of microcalcifications using a non-linear enhancement operator and morphological filter,” in 3rd International Conference on Electronics Computer Technology (ICECT), vol. 5, April 2011, pp. 419-424.
    [10] H. Chan, D. Wei, M. Helvie, B. Sahiner, D. Adler, M. Goodsitt, and N. Petrick, “Computer-aided classification of mammographic masses and normal tissue: Linear discriminant analysis in texture feature space,” Physics in Medicine and Biology, vol. 40, no. 5, pp. 857-876, May 1995.
    [11] H. Cheng, X. Cai, X. Chen, L. Hu, and X. Lou, “Computer-aided detection and classification of microcalcifications in mammograms: a survey,” Pattern Recognition, vol. 36, no. 12, pp. 2967 - 2991, 2003. [Online]. Available: http://www.sciencedirect.com/science/article/pii/S0031320303001924
    [12] H. Cheng, X. Shi, R. Min, L. Hu, X. Cai, and H. Du, “Approaches for automated detection and classification of masses in mammograms,” Pattern Recognition, vol. 39, no. 4, pp. 646-668, June 2006.
    [13] J. Y. Choi, D. H. Kim, K. Plataniotis, and Y. M. Ro, “Combining multiple feature representations and adaboost ensemble learning for reducing false-positive detections in computer-aided detection of masses on mammograms,” in Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), 2012, pp. 4394-4397.
    [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 IEEE-EMBS International Conference on Biomedical and Health Informatics (BHI), 2012, pp. 495-498.
    [15] J. E. E. de Oliveira, A. M. C. Machado, G. C. Chavez, A. P. B. Lopes, T. M. Deserno, and A. d. A. Arafiujo, “Mammosys: A content-based image retrieval system using breast density patterns,” Computer Methods and Programs in Biomedicine, vol. 99, no. 3, pp. 289-297, Sep. 2010. [Online]. Available: http://dx.doi.org/10.1016/j.cmpb.2010.01.005
    [16] A. Dominguez and A. Nandi, “Enhanced multi-level thresholding segmentation and rank based region selection for detection of masses in mammograms,” in IEEE International Conference on Acoustics, Speech and Signal Processing, vol. 1, April 2007, pp. I-449 -I-452.
    [17] F. Durand and J. Dorsey, “Fast bilateral filtering for the display of high-dynamic-range images,” ACM Transactions on Graphics, vol. 21, no. 3, pp. 257-266, Jul. 2002. [Online]. Available: http://doi.acm.org/10.1145/566654.566574
    [18] N. Eltonsy, G. Tourassi, and A. Elmaghraby, “A concentric morphology model for the detection of masses in mammography,” IEEE Transactions on Medical Imaging, vol. 26, no. 6, pp. 880 -889, June 2007.
    [19] R. Fattal, D. Lischinski, and M. Werman, “Gradient domain high dynamic range compression,” ACM Transactions on Graphics, vol. 21, no. 3, pp. 249-256, Jul. 2002. [Online]. Available: http://doi.acm.org/10.1145/566654.566573
    [20] L. Fausett, Ed., Fundamentals of Neural Networks: Architectures, Algorithms, and Applications. Upper Saddle River, NJ, USA: Prentice-Hall, Inc., 1994.
    [21] S. A. Feig, “The importance of supplementary mammographic views to diagnostic accuracy.” American journal of roentgenology, pp. 40-1, 1988. [Online]. Available: http://www.biomedsearch.com/nih/importance-supplementary-mammographic-views-to/3259818.html
    [22] R. Ferrari, R. Rangayyan, J. Desautels, R. Borges, and A. Frere, “Automatic identification of the pectoral muscle in mammograms,” IEEE Transactions onMedicalImaging, vol. 23, no. 2, pp. 232 -245, feb. 2004.
    [23] J. M. Fitzpatrick and M. Sonka, “Handbook of Medical Imaging, Volume 2. Medical Image Processing and Analysis (SPIE Press Monograph Vol. PM80)”, 1st ed. SPIE-The International Society for Optical Engineering, Jun. 2000. [Online]. Available: http://www.amazon.com/exec/obidos/redirect?tag=citeulike07-20&path=ASIN/0819436224
    [24] M. Fraschini, “Mammographic masses classification: novel and simple signal analysis method,” Electronics Letters, vol. 47, no. 1, pp. 14 -15, 6 2011.
    [25] X. Gao, Y. Wang, X. Li, and D. Tao, “On combining morphological component analysis and concentric morphology model for mammographic mass detection,” IEEE Transactions on Information Technology in Biomedicine, vol. 14, no. 2, pp. 266 -273, March 2010.
    [26] A. GarciaManso, C. GarciaOrellana, H. GonzalezVelasco, R. GallardoCaballero, and M. M. Macias, “Consistent performance measurement of a system to detect masses in mammograms based on blind feature extraction,” BioMedical Engineering OnLine, vol. 12, no. 1, p. 2, 2013. [Online]. Available: http://www.biomedical-engineering-online.com/content/12/1/2
    [27] R. C. Gonzalez and R. E. Woods, Digital Image Processing, 3rd ed. Prentice Hall, 2008.
    [28] J. Grim, P. Somol, M. Haindl, and J. Danefis, “Computer-aided evaluation of screening mammograms based on local texture models,” IEEE Transactions on Image Processing, vol. 18, no. 4, pp. 765-773, Apr. 2009. [Online]. Available: http://dx.doi.org/10.1109/TIP.2008.2011168
    [29] R. M. Haralick, K. Shanmugam, and I. Dinstein, “Textural features for image classification,” IEEE Transactions on Systems, Man and Cybernetics, Vol. SMC-3, no. 6, pp. 610-621, Nov. 1973. [Online]. Available: http://dx.doi.org/10.1109/tsmc.1973.4309314
    [30] R. Haralick, “Statistical and structural approaches to texture,” Proceedings of the IEEE, vol. 67, no. 5, pp. 786 - 804, May 1979.
    [31] M. Hussain and N. Khan, “Automatic mass detection in mammograms using multiscale spatial weber local descriptor,” in 19th International Conference on Systems, Signals and Image Processing (IWSSIP), 2012, pp. 288-291.
    [32] M. Hussain, S. Khan, G. Muhammad, and G. Bebis, “A comparison of difierent gabor features for mass classification in mammography,” in Eighth International Conference on Signal Image Technology and Internet Based Systems (SITIS), 2012, pp. 142-148.
    [33] M. Hussain, S. Wajid, A. Elzaart, and M. Berbar, “A comparison of svm kernel functions for breast cancer detection,” in Eighth International Conference onComputer Graphics, Imaging and Visualization (CGIV), 2011, pp. 145-150.
    [34] W. Jian, X. Sun, and S. Luo, “Computer-aided diagnosis of breast microcalcifications based on dual-tree complex wavelet transform,” BioMedical Engineering OnLine, 2012.
    [35] A. Karahaliou, I. Boniatis, S. Skiadopoulos, F. Sakellaropoulos, N. Arikidis, E. A. Likaki, G. Panayiotakis, and L. Costaridou, “Breast cancer diagnosis: Analyzing texture of tissue surrounding microcalcifications.” IEEE Transactions on Information Technology in Biomedicine, vol. 12, no. 6, pp. 731-738, 2008. [Online]. Available: http://dblp.uni-trier.de/db/journals/titb/titb12.html
    [36] D. H. Kim, J. Y. Choi, and Y. M. Ro, “Region based stellate features for classification of mammographic spiculated lesions in computer-aided detection,” in 19th IEEE International Conference on Image Processing (ICIP), 2012, pp. 2821-2824.
    [37] J. K. Kim and H. Park, “Statistical textural features for detection of microcalcifications in digitized mammograms,” IEEE Transactions on Medical Imaging, vol. 18, no. 3, pp. 231-238, March 1999.
    [38] K. Kim, J. Bae, and J. Kim, “Natural hdr image tone mapping based on retinex,” IEEE Transactions on Consumer Electronics, vol. 57, no. 4, pp. 1807-1814, November 2011.
    [39] R. Kimmel, M. Elad, D. Shaked, R. Keshet, and I. Sobel, “A variational framework for retinex,” 2003.
    [40] I. Kitanovski, B. Jankulovski, I. Dimitrovski, and S. Loskovska, “Comparison of feature extraction algorithms for mammography images,” in 4th International Congress on Image and Signal Processing (CISP), vol. 2, 2011, pp. 888-892.
    [41] G. Kom, A. Tiedeu, and M. Kom, “Automated detection of masses in mammograms by local adaptive thresholding,” Computers in Biology and Medicine, vol. 37, no. 1, pp. 37-48, January 2007.
    [42] D. Kopans, Breast Imaging. Lippincott, 1989. [Online]. Available: http://books.google.com.tw/books?id=00-zQgAACAAJ
    [43] J. Kuang, G. M. Johnson, and M. D. Fairchild, “icam06: A refined image appearance model for hdr image rendering,” Journal of Visual Communication and Image Representation, vol. 18, no. 5, pp. 406 - 414, 2007, special issue on High Dynamic Range Imaging. [Online]. Available: http://www.sciencedirect.com/science/article/pii/S1047320307000533
    [44] E. H. Land, John, and J. Mccann, “Lightness and retinex theory,” Journal of the Optical Society of America, pp. 1-11, 1971.
    [45] J. W. Lee, R.-H. Park, and S. Chang, “Local tone mapping using the k-means algorithm and automatic gamma setting,” IEEE Transactions on Consumer Electronics, vol. 57, no. 1, pp. 209-217, February 2011.
    [46] S. Lee, “An eficient content-based image enhancement in the compressed domain using retinex theory,” IEEE Transactions on Circuits and Systems for Video Technology, vol. 17, no. 2, pp. 199-213, Feb. 2007. [Online]. Available: http://dx.doi.org/10.1109/TCSVT.2006.887078
    [47] B. J. Leiner, V. Q. Lorena, T. M. Cesar, and M. V. Lorenzo, “Microcalcifications detection system through discrete wavelet analysis and contrast enhancement techniques,” Electronics, Robotics and Automotive Mechanics Conference, vol. 0, pp.
    272-276, 2008.
    [48] Z. Li and J. Zheng, “Visual-salience-based tone mapping for high dynamic range images,” IEEE Transactions on Industrial Electronics, vol. 61, no. 12, pp. 7076-7082, Dec 2014.
    [49] X. Lladfio, A. Oliver, J. Freixenet, R. Martfifi, and J. Martfifi, “A textural approach for mass false positive reduction in mammography,” Computerized Medical Imaging and Graphics, vol. 33, no. 6, pp. 415-422, Sep. 2009. [Online]. Available:http://dx.doi.org/10.1016/j.compmedimag.2009.03.007
    [50] B. B. Mandelbrot, The fractal geometry of nature, 1st ed. W.H. Freeman, Aug. 1982. [Online]. Available: http://www.amazon.com/exec/obidos/redirect?tag=citeulike07-20&path=ASIN/0716711869
    [51] R. Mantiuk, S. Daly, K. Myszkowski, and H. peter Seidel, “Predicting visible difierences in high dynamic range images - model and its calibration,” in 17th Annual Symposium on Electronic Imaging Human Vision and Electronic Imaging, 2005, pp. 204-214.
    [52] L. Meylan and S. Susstrunk, “High dynamic range image rendering with a retinex-based adaptive filter,” IEEE Transactions on Image Processing, vol. 15, no. 9, pp. 2820-2830, Sep. 2006. [Online]. Available: http://dx.doi.org/10.1109/TIP.2006.877312
    [53] L. Meylan, S. Daly, and S. Susstrunk, “The reproduction of specular highlights on high dynamic range displays,” in Proceedings of The 14th Color Imaging Conference, 2006.
    [54] D. K. R. M. Michael Heath, Kevin Bowyer and W. P. Kegelmeyer, “The digital database for screening mammography,” 2001, pp. 212-218.
    [55] J. Mohanalin, P. K. Kalra, and N. Kumar, “Fuzzy based micro calcification segmentation,” in International Conference on Electrical and Computer Engineering
    (ICECE), Dec 2008, pp. 49-52.
    [56] M. Muttarak, Film-screen mammography:Text-Atlas. Bangkok , PB. Book Center, 1995.
    [57] D. S. M. N. Gargouri, A. Dammak Masmoudi and R. Abid, “A new glld operator for mass detection in digital mammograms,” International Journal of Biomedical Imaging, vol. Volume 2012, p. 13, 2012.
    [58] R. Nithya and B. Santhi, “Article: Classification of normal and abnormal patterns in digital mammograms for diagnosis of breast cancer,” International Journal of Computer Applications, vol. 28, no. 6, pp. 21-25, August 2011, published by Foundation of Computer Science, New York, USA.
    [59] J. E. E. Oliveira, M. O. Gueld, A. D. A. Arafiujo, B. Ott, and T. M. Deserno, “Toward a standard reference database for computer-aided mammography,” Proceedings SPIE, vol. 6915, pp. 69 151Y-69 151Y-9, 2008. [Online]. Available: http://dx.doi.org/10.1117/12.770325
    [60] A. Papadopoulos, D. Fotiadis, and L. Costaridou, “Improvement of microcalcification cluster detection in mammography utilizing image enhancement techniques,”
    Computers in Biology and Medicine, vol. 38, no. 10, pp. 1045 - 1055, 2008. [Online]. Available: http://www.sciencedirect.com/science/article/pii/S0010482508001042
    [61] R. Parekh, “Using texture analysis for medical diagnosis,” IEEE Transactions on MultiMedia, vol. 19, no. 2, pp. 28 -37, feb. 2012.
    [62] S. Paris and F. Durand, “A fast approximation of the bilateral filter using a signal processing approach,” International Journal of Computer Vision, vol. 81, no. 1, pp. 24-52, Jan. 2009. [Online]. Available: http://dx.doi.org/10.1007/s11263-007-0110-8
    [63] S. Park, B. Kim, J. Lee, J. M. Goo, and Y.-G. Shin, “Ggo nodule volume-preserving nonrigid lung registration using glcm texture analysis,” IEEE Transactions on Biomedical Engineering, vol. 58, no. 10, pp. 2885 -2894, oct. 2011.
    [64] S. Peleg, J. Naor, R. Hartley, and D. Avnir, “Multiple resolution texture analysis and classification,” IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. PAMI-6, no. 4, pp. 518-523, July 1984.
    [65] W. Ping, L. Junli, Z. Shanxu, L. Dongming, and C. Gang, “A method of detection micro-calcifications in mammograms using wavelets and adaptive thresholds,” in The 2nd International Conference on Bioinformatics and Biomedical Engineering (ICBBE), May 2008, pp. 2361-2364.
    [66] G. Rabottino, A. Mencattini, M. Salmeri, F. Caselli, and R. Lojacon, “Mass contour extraction in mammographic images for breast cancer identification,” in 16th Symposium Exploring New Frontiers of Instrumentation and Methods for Electrical and Electronic Measurement, July 2008.
    [67] E. Reinhard, “Parameter estimation for photographic tone reproduction,” Journal of Graphics Tools, vol. 7, no. 1, pp. 45-52, Nov. 2002. [Online]. Available: http://dx.doi.org/10.1080/10867651.2002.10487554
    [68] E. Reinhard, T. Pouli, T. Kunkel, B. Long, A. Ballestad, and G. Damberg, “Calibrated image appearance reproduction,” ACM Transactions on Graphics, vol. 31, no. 6, pp. 201:1-201:11, Nov. 2012. [Online]. Available: http://doi.acm.org/10.1145/2366145.2366220
    [69] E. Reinhard, M. Stark, P. Shirley, and J. Ferwerda, “Photographic tone reproduction for digital images,” ACM Transactions on Graphics, vol. 21, no. 3, pp. 267-276, Jul. 2002. [Online]. Available: http://doi.acm.org/10.1145/566654.566575
    [70] E. Reinhard, G. Ward, S. Pattanaik, and P. Debevec, High Dynamic Range Imaging: Acquisition, Display, and Image-Based Lighting (The Morgan Kaufmann Series in Computer Graphics). San Francisco, CA, USA: Morgan Kaufmann Publishers Inc., 2005.
    [71] M. Sameti, R. Ward, J. Morgan-Parkes, and B. Palcic, “Image feature extraction in the last screening mammograms prior to detection of breast cancer,” IEEE Journal of Selected Topics in Signal Processing, vol. 3, no. 1, pp. 46-52, Feb 2009.
    [72] S. Shin, S. Lee, and I. D. Yun, “Classification based micro-calcification detection using discriminative restricted boltzmann machine in digitized mammograms,” vol. 9035, 2014, pp. 90 351L-90 351L-6. [Online]. Available: http://dx.doi.org/10.1117/12.2043316
    [73] S.-C. Tai, Z.-S. Chen, and W.-T. Tsai, “An automatic mass detection system in mammograms based on complex texture features,” IEEE Journal of Biomedical and Health Informatics, vol. 18, no. 2, pp. 618-627, March 2014.
    [74] S. Timp, C. Varela, and N. Karssemeijer, “Temporal change analysis for characterization of mass lesions in mammography,” IEEE Transactions on Medical Imaging, vol. 26, no. 7, pp. 945 -953, july 2007.
    [75] S. Timp and N. Karssemeijer, “Interval change analysis to improve computer aided detection in mammography,” Medical Image Analysis, vol. 10, no. 1, pp. 82 - 95, 2006. [Online]. Available: http://www.sciencedirect.com/science/article/pii/S1361841505000484
    [76] N.-C. Tsai, H.-W. Chen, and S.-L. Hsu, “Computer-aided diagnosis for early-stage breast cancer by using wavelet transform,” Computerized Medical Imaging and Graphics, vol. 35, no. 1, pp. 1 - 8, 2011. [Online]. Available: http://www.sciencedirect.com/science/article/pii/S0895611110000856
    [77] Z. ur Rahman and G. A. Woodell, “Retinex processing for automatic image enhancement,” Journal of Electronic Imaging, vol. 13, pp. 100-110, 2004.
    [78] C. Varelaa, P. G. Tahocesb, A. J. Mfiendez, M. Soutoa, and J. J. Vidala, “Computerized detection of breast masses in digitized mammograms,” Computers inBiology and Medicine, vol. 37, no. 2, pp. 214-226, February 2007.
    [79] D. Wang, L. Shi, and P. A. Heng, “Automatic detection of breast cancers in mammograms using structured support vector machines,” Neurocomputing, vol. 72, pp. 3296 - 3302, 2009. [Online]. Available: http://www.sciencedirect.com/science/article/pii/S0925231209000873
    [80] S. Wang, J. Zheng, H.-M. Hu, and B. Li, “Naturalness preserved enhancement algorithm for non-uniform illumination images,” IEEE Transactions on Image Processing, vol. 22, no. 9, pp. 3538-3548, Sept 2013.
    [81] X. Wang, N. Georganas, and E. Petriu, “Fabric texture analysis using computer vision techniques,” IEEE Transactions on Instrumentation and Measurement, vol. 60, no. 1, pp. 44 -56, jan. 2011.
    [82] Z. Wang, E. Simoncelli, and A. Bovik, “Multiscale structural similarity for image quality assessment,” in Conference Record of the Thirty-Seventh Asilomar Conference on Signals, Systems and Computers, vol. 2, Nov 2003, pp. 1398-1402 Vol.2.
    [83] Z. Wang, A. Bovik, H. Sheikh, and E. Simoncelli, “Image quality assessment: from error visibility to structural similarity,” IEEE Transactions on Image Processing, vol. 13, no. 4, pp. 600-612, April 2004.
    [84] 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. 2827-2837, September 2005.
    [85] Q. Wu, Z. Zhou, H. Leng, J. Cao, J. Wang, Z. Gong, X. Fan, and H. Guo, “A novel real-time method for high dynamic range image tone mapping,” in 4th IEEE International Conference on Information Science and Technology (ICIST), April 2014, pp. 433-436.
    [86] J. Xiao, W. Li, G. Liu, S.-L. Shaw, and Y. Zhang, “Hierarchical tone mapping based on image colour appearance model,” IET Computer Vision, vol. 8, no. 4, pp. 358-364, August 2014.
    [87] S. Xu and C. Pei, “Hierarchical matching for automatic detection of masses in mammograms,” in International Conference on Electrical and Control Engineering (ICECE), September 2011, pp. 4523 -4526.
    [88] H. Yeganeh and Z. Wang, “Objective quality assessment of tone-mapped images.” IEEE Transactions on Image Processing, vol. 22, no. 2, pp. 657-667, 2013. [Online]. Available: http://dblp.uni-trier.de/db/journals/tip/tip22.html#YeganehW13
    [89] Y. Zhang, N. Tomuro, J. Furst, and D. Raicu, “Building an ensemble system for diagnosing masses in mammograms,” International Journal of Computer Assisted Radiology and Surgery, vol. 7, no. 2, pp. 323-329, 2012. [Online]. Available: http://dx.doi.org/10.1007/s11548-011-0628-7

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