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研究生:施宏潔
研究生(外文):Hung-Chieh Shy
論文名稱:應用於骨骼肌超音波影像之紋理特徵分析
論文名稱(外文):Texture Feature Analysis for Ultrasound Image of Skeleton Muscle
指導教授:王榮華
指導教授(外文):Jung-Hua Wang
學位類別:碩士
校院名稱:國立臺灣海洋大學
系所名稱:電機工程學系
學門:工程學門
學類:電資工程學類
論文種類:學術論文
論文出版年:2009
畢業學年度:97
語文別:中文
論文頁數:68
中文關鍵詞:超音波影像骨骼肌肉系統紋理肌肉纖維化運行長度矩陣空間灰階相依矩陣
外文關鍵詞:UltrasonographyMusculoskeletal SystemMuscle FibrosisTextureRun-length MatrixSpatial Gray Level Dependence Matrix
相關次數:
  • 被引用被引用:3
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  • 收藏至我的研究室書目清單書目收藏:1
超音波影像於骨骼肌肉系統之臨床診斷,對於經驗豐富的醫師而言有很好的準確度,但對初學者而言,較易忽略細微的變化,而影響病情的判斷,導致治療效果不佳。
本論文嘗試以紋理特徵參數擷取的方式,提出2個灰階運行長度矩陣之紋理參數ImpSRHGE與ImpLRHGE,並與空間灰階相依矩陣之紋理參數contrast,利用正常肌肉與不正常肌肉間的參數差異,分析肌肉纖維化的程度,預期可輔佐並同時減少不同醫師之間的診斷差異,提高診斷準確度。
In clinical, although experienced physicians may make accurate diagnosis when using ultrasonography of musculoskeletal system, for beginners it is rather difficult to recognize subtle change of gray level in the involved muscle.
This thesis attempts to provide an objective tool which can help improve diagnosis accuracy by extracting texture features present in a pair of ultrasonic images. Two texture features ImpSRHGE and ImpLRHGE are derived from gray level run-length matrices to characterize the B-mode muscle ultrasonography. Then, the aforementioned texture features and the contrast value derived from a spatial gray level dependence matrix are used together to evaluate the degree of muscle fibrosis through distinguishing texture feature variation between the image pair (i.e., normal and abnormal muscles). Our empirical results on ten patients have indicated that subjective diagnosis variation among different physicians may potentially be alleviated.
第一章 緒論 1
1-1 背景介紹 1
1-2 研究動機與目的 2
第二章 超音波影像分析 4
2-1 先天性肌肉斜頸 4
2-2 骨骼肌於超音波影像上的特性 8
2-2-1 骨骼肌的組成結構 8
2-2-2 非等向性 11
2-3 紋理特徵擷取 12
2-3-1 空間灰階相依矩陣 12
2-3-2 灰階運行長度矩陣 16
第三章 肌肉超音波影像之紋理特徵分析 22
3-1 來源影像 23
3-2 圈選肌肉區域 24
3-3 量化以減少灰階個數 25
3-4 分割區塊 27
3-5 擷取紋理特徵參數 31
3-5-1 改進紋理特徵參數 41
3-6求得代表各ROI之相對量值 44
第四章 實驗結果 45
4-1 正常與病變之SCM肌肉比較 45
4-2 HIGH GRAY LEVEL的影響 53
第五章 結論與未來展望 65
參考文獻 67
[1] T.C. Hsu, C.L. Wang, M.K. Wong, K.H. Hsu, F.T. Tang, and H.T. Chen, “Correlation of clinical and ultrasonographic features in congenital muscular torticollis,” Archives of physical medicine and rehabilitation, vol. 80, 1999, pp. 637-641.
[2] J. Lascaratos and A. Damanakis, “Ocular torticollis: a new explanation for the abnormal head-posture of Alexander the Great,” The Lancet, vol. 347, 1996, pp. 521-523.
[3] J.K. Bredenkamp, L.A. Hoover, G.S. Berke, and A. Shaw, “Congenital Muscular Torticollis: A Spectrum of Disease,” Arch Otolaryngol Head Neck Surg., vol. 116, 1990.
[4] J.R. Davids, D.R. Wenger, and S.J. Mubarak, “Congenital muscular torticollis: sequela of intrauterine or perinatal compartment syndrome,” Journal of pediatric orthopedics, vol. 13, 1993.
[5] S.B. Porter and B.W. Blount, “Pseudotumor of infancy and congenital muscular torticollis,” American family physician, vol. 52, 1995, pp. 1731-6.
[6] S.W. Chou and M.K. Wong, “Outcome of Torticollis Children,” Journal of Rehabilitation Medicine Association, Dec. 1991, pp. 71-77.
[7] 王崇禮, 謝豐舟, 蕭自佑, 邵耀華, 王亭貴, 及 謝正宜等, 骨骼肌肉超音波, 台北: 力大圖書, 2003.
[8] A. Mir, M. Hanmandlu, and S. Tandon, “Texture analysis of CT images,” Engineering in Medicine and Biology Magazine, IEEE, vol. 14, 1995, pp. 781-786.
[9] R.M. Haralick, K. Shanmugam, and I. Dinstein, “Textural Features for Image Classification,” Systems, Man and Cybernetics, IEEE Transactions on, vol. 3, 1973, pp. 610-621.
[10] M.M. Galloway, “Texture Analysis using Gray Level Run Lengths,” Computer Graphics and Image Processing, vol. 4, 1975, pp. 172-179.
[11] B.V. Dasarathy and E.B. Holder, “Image characterizations based on joint gray level-run length distributions,” Pattern Recogn. Lett., vol. 12, 1991, pp. 497-502.
[12] R.C. Gonzalez and R.E. Woods, Digital Image Processing, Addition Wesley, 1992.
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