Please use this identifier to cite or link to this item: http://hdl.handle.net/11455/90713
標題: 影像正規化對腕骨切割之評估
An Estimation on the Carpal Bone Extraction Using Intensity Normalization
作者: Ting-Jyun Peng
彭莛鈞
關鍵字: z transform
CROI
Bone Age
Image Segmentation
z轉換
CROI
骨齡
影像切割
引用: [1] D. B Darling, 'Radiography of Infants and Children,' 1st ed. Springfield, IL: Charles C Thomas, Ch. 6, pp. 370–372, 1979. [2] R. D. Milner, M. A. Preece, and J. M. Tanner, 'Growth in height compared with advancement in skeletal maturity in patients treated with human growth hormone,' Arch. Dis. Child., Vol. 55, no. 6, pp. 461–466, Jun. 1980. [3] D.R. Kirks, 'Practical Pediatric Imaging. Diagnostic Radiology of Infants and Children,' 1st ed. Boston, MA: Little, Brown & Co., 1984. [4] W.W. Greulich and S.I. Pyle, 'Radiographic Atlas of Skeletal Development of Hand Wrist (ed2),' Stanford, CA: Standford University Press, 1971. [5] D. G. King, D. M. Steventon, M. P. O'Sullivan, A. M. Cook, V.P. Hornsby and I. G. Jefferson, 'Reproducibility of bone ages when performed by radiology registrars: An audit of Tanner and Whitehouse II versus Greulich and Pyle methods,' Br. J. Radiol. Vol. 67, No. 8, pp. 848–851, 1994. [6] A.F. Roch, C.G. Rochman and G.H.Davila, 'Effect of training on replicability of assessments of skeletal maturity (Greulich-Pyle),' Amer. J. Roentgenol. Vol. 108, pp. 511–515, 1970. [7] J.M. Tanner, M.J.R. Healy, H. Goldstein, N. Cameron, 'Assessment of Skeletal Maturity and Prediction of Adult Height (TW3method),' WB Saunders, London, 2001. [8] J.M. Tanner and R.H. Whitehouse, 'Assessment of skeletal maturity and prediction of adult height (TW2 Method),' London: Academic Press., 1975. [9] A. Ortega, 'Comparison of TW2 and TW3 skeletal age differences in a Brazilian population,' J. Appl. Oral Sci., Vol. 14, no. 2, pp. 142–146, Apr. 2006. [10] F. E. Johnston and S. B. Jahina, 'The contribution of the carpal bones to the assessment of skeletal age,' Amer. J, Phys. Anrhrop., Vol. 23, pp. 349-354, 1965. [11] Hsieh CW, Chu BC, Jong TL et al., 'Bone age classification using fuzzy concept with support vector machine neural network,' In: ICBME 2005, 12th International Conference on Biomedical Engineering, Singapore, 1A4-05. [12] E. Pietka, A. Gertych, S. Pospiech et al., 'Computer-assisted bone age assessment: image preprocessing and epiphyseal/metaphyseal ROI extraction,' IEEE Trans. on Med. Imag, Vol. 20, pp. 715–729, 2001. [13] E. Pietka, A. Gertych, S. Pospiech, F. Cao, HK. Huang, and V. Gilsanz, 'Computer-assisted bone age assessment: graphical user interface for image processing and comparison,' J. Digit. Imaging Vol. 17, pp. 175–188, 2004. [14] E. Pietka, L. Kaabi, M. Kuo, and H. Huang, 'Feature extraction in carpal bone analysis,' IEEE Trans. Med. Imag, Vol. 12, no. 1, pp. 44–49, 1993. [15] J. Liu, J. Qi, Z. Liu, Q. Ning, X. Luo, 'Automatic bone age assessment based on intelligent algorithms and comparison with TW3 method,' Comput Med Imaging Graph Vol. 32, pp. 678–884, 2008. [16] P. Lin, F. Zhang, Y. Yang, C. Zheng, 'Carpal-bone feature extraction analysis in skeletal age assessment based on deformable model,' JCS&T , Vol. 4, pp. 152–156, 2004. [17] P. Lin, C. Zheng, F. Zhang, Y. Yang, 'X-ray carpal-bone image boundary feature analysis using region statistical feature based level set method for skeletal age assessment application,' Optica Applicata, Vol. 2, pp. 283–294, 2005. [18] C. C. Ko, CW. Mao, CJ. Lin, YN. Sun, 'Image analysis for skeletal evaluation of carpal bones,' Proc SPIE, Vol. 2501, pp. 951–61, 1995. [19] A. K. Poznanski, R. J. Hernandez, and K. E. Guire, 'Carpal length in children-A useful measurement in the diagnosis of rheumatoid arthritis and some congenital malformation syndromes,' Radiology, Vol. 129, pp. 661-668, 1978. [20] J. W. Pryor, 'Time of ossification of the bones of the hand of the male and female and union of epiphyses with the diaphyses,' Amer. J. Phys. Anthrop, Vol. 33, pp. 401410, 1975. [21] D. Giordano, C. Spampinato, G. Scarciofalo, and R. Leonardi, 'An Automatic System for Skeletal Bone Age Measurement by Robust Processing of Carpal and Epiphysial / Metaphysial Bones,' IEEE Trans. on Instrumentation and Measurement, Vol. 59, No. 10, Oct. 2010. [22] D.J. Michael and A.C. Nelson, HANDX, 'A model-based system for automatic segmentation of bones from digital hand radiographs,' IEEE Trans. on Medical Imaging, Vol. 8, No. 1, pp. 64–69, 1989. [23] E. Pietka, McNitt-Gray MF, ML. Kuo et al., 'Computer assisted phalangeal analysis in skeletal age assessment,' IEEE Trans Med Imaging, Vol. 10, pp. 616-620, 1991. [24] E. Pietka, L. Kaabi, ML. Kuo et al., 'Feature extraction in carpal-bone analysis,' IEEE Trans Med Imaging, Vol. 12, pp. 44-49, 1993. [25] Pietka E (1995) Computer-assisted bone age assessment based on features automatically extracted from a hand radiograph. Comput Med Imaging Graph 19:251–259. [26] Pietka E, Huang HK (1995) Epiphyseal fusion assessment based on wavelets decomposition analysis. Comput Med Imaging Graph 19:465–472. [27] Fan BC, Hsieh CW, Jong TL et al (2001) Automatic bone age estimation based on carpal-bone image: a preliminary report. Zhonghua Yi Xue Za Zhi (Taipei) 64:203–208 [28] Hsieh CW, Chu BC, Jong TL et al (2005) Bone age classification using fuzzy concept with support vector machine neural network. In: ICBME 2005, 12th International Conference on Biomedical Engineering, Singapore, 1A4-05. [29] A. Zhang, A. Gertych, B. J. Liu, 'Automatic bone age assessment for young children from newborn to 7-year-old using carpal bones,' Computerized Medical Imaging and Graphics (2007) 31:299–310. [30] Liu J., Qi J., Liu Z., Ning Q., Luo X., 'Automatic bone age assessment based on intelligent algorithms and comparison with TW3 method,' Computerized Medical Imaging and Graphics 32 (2008) 678–684. [31] CW Hsieh, TC Liu, TL Jong, CM Tiu, 'A fuzzy-based growth model with principle component analysis,' Med. Biol. Eng. Comput., Vol. 48, no. 6, pp.579–588, JUN. 2010. [32] M. F. McNitt-Gray, E. Pietka, and H. K. Huang, 'Image preprocessing for a picture archiving and communication system,' Invest. Radiol., Vol. 27,no. 7, pp. 529–535, Jul. 1992. [33] J. Zhang and H. Huang, 'Automatic background recognition and removal (ABRR) in computed radiography images,' IEEE Trans. Med Imag, Vol. 16, no. 6, pp. 762–771, Dec. 1997. [34] G. W. Zack, W. E. Rogers, and S. A. Latt, 'Automatic measurement of sister chromatid exchange frequency,' J. Histochem. Cytochem. Vol. 25, no. 7, pp. 741–753, 1977. [35] Kreyszig E., 'Applied Mathematics(Fourth Edition 1979),' Wiley Press, pp. 880. [36] Hsieh CW, Jong TL, Chou YH, Tiu CM ,'Computerized geometric features of carpal bone for bone age estimation.', Chin Med J (Engl), Vol. 120, no. 9, pp. 767-70, May 2007. [37] K. Somkantha, N. Theera-Umpon, and S. Auephanwiriyakul, 'Bone Age Assessment in Young Children Using Automatic Carpal Bone Feature Extraction and Support Vector Regression,' J Digit Imaging (2011) , Vol. 24, pp. 1044–1058, Mar. 1993. [38] M. F. McNitt-Gray, E. Pietka, and H. K. Huang, 'Image preprocessing for a picture archiving and communication system,' Invest. Radiol., Vol. 27, no. 7, pp. 529–535, Jul. 1992. [39] H. H. Lin, S. G. Shu, S. W. Kuo, C. H. Wang, Y. P. Chan and S. S. Yu, ,'An α-Gamma Equalization Enhanced Radiographic Hand Image Segmentation Scheme,' Optical Engineering, Vol. 48, No. 10, pp. 107001-1-107001-9, 2009. [40] S. G. Shu, H. H. Lin, S. W. Kuo and S. S. Yu, 'Excluding Background Initial Segmentation for Radiographic Image Segmentation,' IJICIC, Vol. 5, No. 11(A), pp. 3849-3860, 2009. [41] H. H. Lin, W. C. Chiang, S. G. Shu, L. M. Shih and S. S. Yu, 'The Effect of ROI Normalization for Hand Radiographic Image Segmentation,' IJICIC, 2010. [42] Haralick, M. Robert, and L. G. Shapiro, 'Computer and Robot Vision, Volume I,' Addison-Wesley, pp. 28-48, 1992. [43] P. Perona and J. Malik, 'Scale-Space and Edge Detection Using Anisotropic Diffusion,' IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol. 12, no. 7, pp. 629-639, Jul. 1990. [44] G. Grieg, O. Kubler, R. Kikinis, and F. A. Jolesz, 'Nonlinear Anisotropic Filtering of MRI Data,' IEEE Transactions on Medical Imaging, Vol.11, no. 2, pp. 221-232, Jun. 1992. [45] C. Tsiotsios and M. Petrou, 'On the choice of the parameters for anisotropic diffusion in image processing,' Pattern Recognition, Vol. 46, Issue 5, pp. 1369–1381, May 2013. K. H. Brodersen, Cheng Soon Ong, [46] K. E. Stepen, and J. M. Bumann, 'The Balanced Accuracy and Its Posterior Distribution,' Int. J. Comput. Vision Vol. 57, no. 2, pp. 3121-3124, Aug. 2010. [47] D. S. O'Keeffe, 'Denoising of Carpal Bones for Computerised Assessment of Bone Age,' thesis, University of Canterbury, Christchurch, New Zealand, 2010. [48] R. C. Gonzalez, R. E. Woods, 'Digital Image Processing,' Prentice Hall, pp. 85, 2007. [49] H. J. Johnson, M. McCormick, L. Iba'nez, and the Insight Software Consortium, 'The ITK Software Guide Third Edition Updated for ITK version 4.5,' December 17, 2013. [50] Han, Jun, Morag, and Claudio, 'The influence of the sigmoid function parameters on the speed of backpropagation learning,' Mira, Jose; Sandoval, Francisco, Natural to Artificial Neural Computation, pp. 195–201, 1995. [51] R. C. Gonzalez, R. E. Woods, 'Digital Image Processing 3rd edition,' Prentice Hall, pp. 124, 2008. [52] S. M. Pizer, E. P. Amburn, J. D. Austin, et al., 'Adaptive Histogram Equalization and Its Variations,' Computer Vision, Graphics, and Image Processing, Vol. 39, pp. 355-368, 1987. [53] K. Zuiderveld, 'Contrast Limited Adaptive Histogram Equalization. In: P. Heckbert: Graphics Gems IV,' Academic Press, ISBN 0-12-336155-9, pp. 474-485, 1994. [54] J.S. Lim, 'Two Dimensional Signal & Image Processing,' Prentice-Hall, Englewood Cliffs, NJ, pp.536–540, 1990. [55] Soille P., 'Morphological Image Analysis: Principles and Applications,' Springer-Verlag, pp. 173-174, 1999.
摘要: Assessing bone age by viewing hand radiographs plays a critical role in clinical pediatric endocrinology. According to the past researches, to assess bone ages by phalanx features among 0 to 7 years old may get the worse bone age assessment accuracy as the smaller ages goes. The clinical researches had indicated that among the ages from newborn to about 7 years old, the maturation of carpal bones appear in a specific order and separate from each other. And the features among the period that the carpal bones begin fusing in about 8 years old has been proven effective in a research of recent years. For these reasons, to assess bone age had demonstrated to be reliable under this range of ages. However, the process of extracting the carpal region of interest (CROI) from radiographs is usually coupled with such image quality problems like low contrast, non-uniformly or over-low exposure. Hence it is quite challengeable task to separate the pixels represent the bone tissue from the soft tissue in radiographs. This dissertation proposed an image intensity normalization method which applied the transform function, z transform in Statistics. By transforming each intensity value found in an image to z value respectively, all the radiographs from heterogeneous sources can be normalized into one consistent standard. The pixels represent the carpal bones in radiographs are segmented by intensity thresholding. With respect to balanced accuracy, the chosen criteria of segmentation accuracy in this dissertation, the experiment results reveal proposed method raises the performance of control group obviously and outperforms the image intensity normalization methods including linear normalization, histogram equalization and contrast limited histogram equalization, which are often used in medical image processing. In addition, the average balance accuracy of the samples normalized by proposed method and grouped by rounded bone age all reach about 80 percent.
從手掌X光影像估測骨齡,在兒童內分泌的問題及成長失調的診斷上扮演著極重要的角色。文獻顯示在骨齡估測方法的研究上,若是依靠指骨特徵對0~7歲的幼童估測骨齡得出的效果不佳,而且年齡愈小這種現象愈明顯。而在醫學上的研究顯示,從新生兒到7歲左右的兒童,每一塊腕骨的成長是依序出現且相互分離,並且在8歲腕骨開始癒合時的特徵也在近幾年的研究中得到有效的證實。因此在這一個階段利用腕骨做幼童的骨齡估測被認為是可信賴的。但是在萃取腕骨感興趣區域(CROI)的過程中,常遭遇到X光影像低對比、照度不均或是曝光過多或不足的問題。因此,要能夠從手掌X光影像中,將顯示骨骼的像素從軟組織中分離出來,是相當具有挑戰性的工作。本論文應用統計學上的z轉換,提出一個影像亮度正規化的方法,首先將影像中的各灰階值轉換成相對應的z值,讓各種來源不同的X光影像亮度可以校正到一致的標準,之後再選取適當的門檻值來切割腕骨影像。實驗結果顯示,本方法在balanced accuracy上相較於對照組能夠明顯的提高切割正確率,與過去研究醫學影像上常用的亮度正規化方法包括線性正規化、直方圖等化、CLAHE等演算法相比,有更佳的樣本平均切割正確率,另外對於1~ 8歲骨齡樣本各自分群的平均正確率可達80%左右。
URI: http://hdl.handle.net/11455/90713
其他識別: U0005-2606201414272500
文章公開時間: 2016-12-16
Appears in Collections:資訊科學與工程學系所

文件中的檔案:

取得全文請前往華藝線上圖書館



Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.