臺灣博碩士論文加值系統

X 光片的骨齡評估 (bone age assessment, BAA) 是小兒科醫師用來判讀兒童生長發育的常規檢查。目前最常使用的判讀準則有兩種：G&P 和 TW2 方法。以 G&P 和 TW2 方法做的骨齡比對方法，不僅耗時，且會根據個人主觀意識或人員訓練方法不同而有不同的比對結果。為了排除上述人為因素，使影像判讀有更客觀的結果，我們提出以電腦視覺的技術從 X 光片中擷取腕骨輪廓。
我們的方法包含影像前處理及腕骨分割兩部份。在影像前處理部份，我們利用二值化 (bi-level thresholding) 手部影像的前臂資訊校正腕部方向，並由尺、橈骨交點和掌骨資訊幫助訂定腕骨分析範圍 (CROI)，藉由最小最大濾波器 (min/max filter) 評估 CROI 的影像背景，將原影像減去背景以提昇對比。在腕骨分割部份，我們利用 Osma-Ruiz等人的分水嶺方法、梯度運算、及距離轉換方法做重疊腕骨的分割；其中的分割重點在於腕骨重疊時各腕骨的判定，引進腕骨骨化次序的特性，依序對鉤狀骨、頭狀骨、三角骨、月狀骨、大多角骨、小多角骨、及舟狀骨進行輪廓擷取。此技術不僅適用 7 歲以下未重疊的腕骨，也有助於 7-14 歲重疊腕骨的分割。

Bone age assessment (BAA) using hand radiographs is an important technique of pediatric radiology for doctors assessing the growing situation of children. Two most commonly used methods are the Greulich-Pyle (G&P) method and the Tanner-Whitehouse (TW2) method. However these methods have the disadvantage of time consuming and the results of BAA highly depending upon the assessor expertise and experience. In order to obtain more objective results, an automatic carpal bone feature extraction method is pursued and proposed. The proposed system consists of two main stages: image preprocessing and carpal-bone segmentation. In image preprocessing, we starts with acquiring wrist direction and extracting carpal bone region of interest (CROI) in the binary hand images. Then, the CROI contrast is enhanced by using a min/max filter. In carpal-bone segmentation, the techniques of watershed method proposed by Osma-Ruiz et al., gradient technique, distance transformation, and anatomy knowledge were used to assist the extraction of carpal bone contours. The carpal bones include: trapezium, trapezoid, capitate, hamate, scaphoid, lunate, triquetrum, and pisform. Our system can extract carpal bone contours effectively in 1-14 year old children.

摘要 ii
Abstract iii
誌謝 iv
目錄 v
圖目錄 vii
表目錄 x
第一章 緒論 1
1.1 研究動機 1
1.2 系統架構 3
1.3 論文架構 5
第二章 相關研究 6
2.1 影像前處理 6
2.2 指骨特徵自動擷取 8
2.3 腕骨特徵自動擷取 9
第三章 腕骨分割原理 12
3.1 分水嶺演算法 12
3.1.1 分水嶺演算法名詞定義 14
3.1.2 演算法表示 17
3.2 距離轉換 23
3.3 型態學運算 25
3.3.1 侵蝕 26
3.3.2 膨脹 26
3.3.3 細線化 27
第四章 方法及步驟 28
4.1 影像前處理 28
4.1.1 中值濾波 29
4.1.2 腕部方向正規化 29
4.1.3 CROI擷取 33
4.1.4 強化CROI資訊 36
4.2 分水嶺分割法 37
4.2.1 梯度強度影像 38
4.2.2 距離轉換 39
4.2.3 以分水嶺方法產生粗分割影像 39
4.3 腕骨分割 41
4.3.1 腕骨骨科解剖特性 41
4.3.2 腕骨分割流程 44
4.3.3 腕骨分割方法 46
第五章 實驗與討論 60
5.1 實驗 60
5.1.1 不同差分強度方法比較 60
5.1.2 不同閥值比較 62
5.1.3 不同距離轉換函式比較 63
5.1.4 各年齡及性別兒童腕骨分割結果 64
5.2 討論 68
第六章 結論與未來展望 71
6.1 結論 71
6.2 未來展望 71
參考文獻 73
附錄 76

[1]Bieniek, A. and A. Moga, "An efficient watershed algorithm based on
connected components," Pattern Recognition, vol.33, no.6, pp.907-916, 2000.
[2]Cao, F., H. K. Huang, E. Pietka, and V. Gilsanz, "Digital hand atlas and web-based bone age assessment: system design and implementation," Computerized Medical Imaging and Graphics, vol.24, pp.297-307, 2000.
[3]Fan, B. C., C. W. Hsieh, T. L. Jong, and C. M. Tiu, "Automatic bone age estimation based on carpal bone images - a preliminary report," Chinese Medical Journal, vol.64, pp.203-208, 2001.
[4]Ganch, J. M., "Image segmentation and analysis via multiscale gradient watershed hierarchies," IEEE Trans. on Image Processing, vol.8, no.1, pp.69-79, 1999.
[5]Gertych, A., A. Zhang, J. Sayre, K.-P. Sylwia, and H. K. Huang, "Bone age assessment of children using a digital hand atlas," Computerized Medical Imaging and Graphics, vol.31, pp.322-331, 2007.
[6]Grau, V., A. U. J. Mewes, M. Alcaniz, R. Kikinis, and S. K. Warfield, "Improved watershed transform for medical image segmentation using prior information," IEEE Trans. on Medical Imaging, vol.23, no.4, pp.447-458, 2004.
[7]Greulich, W. W. and S. I. Pyle, Radiographic Atlas of Skeletal Development of Hand Wrist, Stanford University Press, San Francisco, CA, 1959.
[8]Haris, K., S. N. Efstratiadis, N. Maglaveras, and A. K. Katsaggelos, "Hybrid image segmentation using watersheds and fast region merging," IEEE Trans. on Image Processing, vol.7, no.12, pp.1684-1698, 1998.

[9]Huang, P. C., The Extraction and Process of ROI of Phalangeal Bones Radiographs, Master Thesis, Institute of Computer Science and Information Engineering, National Chung-Hsing University, Jun. 2008.
[10]Osma-Ruiz, V., J. I. G. Llorente, N. S. Lechon, and P. G. Vilda, "An improvedwatershed algorithm based on efficient computation of shortest paths," Pattern Recognition, vol.40, pp. 1078-1090, 2007.
[11]Pang, Q., C. Yang, Y. Fan, and Y. Chen, "Overlapped cell image segmentation based on distance transform," in Proc. the 6th World Congress on Intelligent Control and Automation, Dalian, China, June 21 - 23, 2006, pp.9858-9861.
[12]Pietka, E., M. F. McNitt-Gray, M. L. Kuo, and H. K. Huang, "Computer assisted phalangeal analysis in skeletal age assessment," IEEE Trans. on Medical Imaging, vol.10, no.4, pp.616-620, 1991.
[13]Pietka, E., L. Kaabi, M. L. Kuo, and H. K. Huang, "Feature extraction in carpal-bone," IEEE Trans. on Medical Imaging, vol.12, no.1, pp.44-49, 1993.
[14]Pietka, E., "Computer-assisted bone age assessment based on features automatically extracted from a hand radiograph," Computerized Medical Imaging Graphics, vol.19, no.3, pp.251-259, 1995.
[15]Pietka, E., A. Gertych, S. Pospiech, F. Cao, and H. K. Huang, "Computer assisted bone age assessment: Image processing and epiphyseal/metaphyseal ROI extraction," IEEE Trans. on Medical Imaging, vol.20, no.8, pp.715-729, 2001.
[16]Pietka, E., S. Pospiech, A. Gertych, F. Cao, H. K. Huang, and V. Gilsanz, "Computer automated approach to the extraction of epiphyseal regions in hand radiographs," Journal of Digital Imaging, vol.14, no.4, pp.165-172, 2001.
[17]Pietka, E., K.-P. Sylwia, A. Gertych, and F. Cao, "Integration of computer assisted bone age assessment with clinical PACS," Computerized Medical Imaging and Graphics, vol.27, pp.217-228, 2003.
[18]Pietka, E., A. Gertych, and K. Witko, "Informatics infrastructure of CAD system," Computerized Medical Imaging and Graphics, vol.29, pp.157-169, 2005.
[19]Silva, A., S. Olabarriaga, C. Dietrich, and C. Schmitz, "On determining a signature for skeletal maturity," in Proc. 14th Brazilian Symposium on Computer Graphics and Image Processing, Florianopolis, Brazil, Oct. 15-18, 2001, pp.246-251.
[20]Sun, H., J. Yang, and M. Ren, "A fast watershed algorithm based on chain code and application in image segmentation," Pattern Recognition, vol.26, pp.1266-1274, 2005.
[21]Tanner, J. M., R. H. Whitehouse, W. A. Marshall, and M. J. R. Healy, Assessment of Skeletal Maturity and Predication of Adult Height (TW2 Method), Academic Press, New York, 1983.
[22]Vincent, L. and P. Soille, "Watersheds in digital spaces: An efficient algorithm based on immersion simulations," IEEE Trans. on Pattern Analysis and Machine Intelligence, vol.13, no.6, pp.583-597, 1991.
[23]Zhang, J. and H. K. Huang, "Automatic background recognition and removal (ABRR) of computed radiography images," IEEE Trans. on Medical Imaging, vol.16, no.6, pp.762-771, 1997.
[24]Zhang, A., A. Gertych, K.-P. Sylwia, B. J. Liu, and H. K. Huang, "Carpal bone analysis in bone age assessment," in Proc. SPIE. Medical Imaging2006: PACS and Imaging Informatics, San Diego, CA, USA, Feb. 14, 2006, pp.61450Z-1 - 61450Z-9.
[25]Zhang, A., A. Gertych, and B. J. Liu, "Automatic bone age assessment for young children from newborn to 7-year-old using carpal bones," Computerized Medical Imaging and Graphics, vol.31, pp.299-310, 2007.