骨龄测定系统
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摘要
微机骨龄测定系统是华西附属第二医院提出的应用课题，它在文艺，体育部门选材，儿童保健咨询及生长预测，小儿身高异常及内分泌疾病的诊断，治疗监护和疗效观察以及法医学鉴定等方面有重要意义。
该系统主要由图像处理和骨龄计算两部分组成，本课题主要对图象处理部分展开研究，取得了一定的成果，推进了骨龄测定系统微机化的研究。本文深入分析了目前存在的各种图像分割方法及其特点，针对医学图像及所研究对象的要求和特征，以及目前骨龄测定系统存在的优缺点，拟定了微机骨龄测定系统图像分割部分的总体方案，具有一定的创新性。
随后，笔者重点研究了小波技术在微机骨龄诊断系统中的应用，初步解决了目前骨龄系统中存在的局部分割问题，抛弃传统的链长观念，提出了链的百分比概念，国内外文献未见有相关报道；针对分割结果的不封闭性，采用边缘点生长的方法，有效地解决了区域的封闭性。最后通过寻找大区域解决了高细节区的分割问题。
最后，笔者用Matlab语言在计算机上完成了本方案的仿真，包括图像的预处理、分割和后继处理。方案通过计算机实现，与理论分析设计具有比较好的一致性，取得了较满意的结果。
The microcomputer-based bone-age recognition system is an application project by the second hospital pertaining to huaxi medical university. It has significance in the areas of selecting talents of art and sports, the healthcare consultation and growth forecast of children, the abnormity of children's stature and the diagnosis of incretion illness, the monitoring and observation of the treatment effect and the identification in medical jurisprudence.
The system is composed of two parts: the image segmentation and bone-age computation. This project mainly carried out exploration in image segmentation and some good results are derived which advances the research of the micro-computer based bone-age recognition system. In this paper the in-depth analysis is made for the extant image segmentation approaches and their characteristics. Aiming at the demand , analyzing the characteristic of the medical image and its investigation subject as well as the advantages and drawbacks of the extant bone-age recognition system, the overall scheme is designed for the image-segmentation for the micro-computer based bone-age recognition system ,which is a creative work.
In this paper emphasis is put on the application of the wavelet technique into the system and the local segmentation problem preliminarily solved existing in the extant bone-age recognition system..the traditional chain-length concept is dixcarded and the percentage concept of the chain is proposed. No related reports can be found in the literature. The chain is proposed. No related reports can be found in the literature. The edge-growth method is adopted to get the good closed image. Lastly the image segmentation is implemented by finding large regions.
Finally the simulation is implemented with matlat including the preprocessing ,the segmentation and the after-effect processing. The scheme is realized with computer and a fairly good consistency is
obtained with the theory analysis and design. The result is satisfactory according to the clinical demand.
该系统主要由图像处理和骨龄计算两部分组成，本课题主要对图象处理部分展开研究，取得了一定的成果，推进了骨龄测定系统微机化的研究。本文深入分析了目前存在的各种图像分割方法及其特点，针对医学图像及所研究对象的要求和特征，以及目前骨龄测定系统存在的优缺点，拟定了微机骨龄测定系统图像分割部分的总体方案，具有一定的创新性。
随后，笔者重点研究了小波技术在微机骨龄诊断系统中的应用，初步解决了目前骨龄系统中存在的局部分割问题，抛弃传统的链长观念，提出了链的百分比概念，国内外文献未见有相关报道；针对分割结果的不封闭性，采用边缘点生长的方法，有效地解决了区域的封闭性。最后通过寻找大区域解决了高细节区的分割问题。
最后，笔者用Matlab语言在计算机上完成了本方案的仿真，包括图像的预处理、分割和后继处理。方案通过计算机实现，与理论分析设计具有比较好的一致性，取得了较满意的结果。
The microcomputer-based bone-age recognition system is an application project by the second hospital pertaining to huaxi medical university. It has significance in the areas of selecting talents of art and sports, the healthcare consultation and growth forecast of children, the abnormity of children's stature and the diagnosis of incretion illness, the monitoring and observation of the treatment effect and the identification in medical jurisprudence.
The system is composed of two parts: the image segmentation and bone-age computation. This project mainly carried out exploration in image segmentation and some good results are derived which advances the research of the micro-computer based bone-age recognition system. In this paper the in-depth analysis is made for the extant image segmentation approaches and their characteristics. Aiming at the demand , analyzing the characteristic of the medical image and its investigation subject as well as the advantages and drawbacks of the extant bone-age recognition system, the overall scheme is designed for the image-segmentation for the micro-computer based bone-age recognition system ,which is a creative work.
In this paper emphasis is put on the application of the wavelet technique into the system and the local segmentation problem preliminarily solved existing in the extant bone-age recognition system..the traditional chain-length concept is dixcarded and the percentage concept of the chain is proposed. No related reports can be found in the literature. The chain is proposed. No related reports can be found in the literature. The edge-growth method is adopted to get the good closed image. Lastly the image segmentation is implemented by finding large regions.
Finally the simulation is implemented with matlat including the preprocessing ,the segmentation and the after-effect processing. The scheme is realized with computer and a fairly good consistency is
obtained with the theory analysis and design. The result is satisfactory according to the clinical demand.
引文
[1] J.H.HIMES. An early hand-wrist atlas and it's implications for secular change in lone age. ANNALS OF HUMAN BIOLOGY. 1984.Vol 11.No 1
[2] Greulich.W.W. and S.I..Pyle. Radiographic Altas of Skeletal Development of Hand and Wrist(2stcd) Stanford University Press. Stanford Calif. 1959
[3] Acheson R.M. a method of assessing skeletal maturity from radiographs. J.Anat. 1954.88
[4] Tanner J.M. and R.H. Whitehouse. Standard for skeletal maturity part I .International Children's center. Paris . 1959
[5]Tanner J.M. and R.H. Whitehouse et al. Assessment of skeletal maturity and prediction of adult height(TW2 method) 2nd edition London. Academic Press. 1983
[6] 顾光宁等。中国人手与腕骨部之骨化 解剖学报 第五卷 第二期 173~183 1982
[7] 中国人手骨腕部骨龄标准的研究 中国人手腕骨骨龄标准研究课题组 1988
[8] Beunen G. and N.Cameron The reproducibility of TW2 skeletal age assessments log a self-taught assessor Annals of Human Biol. P.155-162 1980
[9]陈达明等 中日合作青少年体质研究联合调查报告 第一报 中华全国体育总会科教部.日本科学协会科学委员会 1987
[10] 曾凡辉 发育程度和发育高潮持续时间与发育类型的鉴别及对选材的
作用 优秀青少年运动员科学选材研究论文汇编 1983
[11] 张国栋 中小学生手和腕部骨骼发育变化规律的研究 学校卫生 1982年第2期
[12] 罗代升、何小海等 激光共焦显微生物医学图像的等高地图分割法 生物医学工程杂志 p500~503 2001 18卷第四期
[13] Ong SH.Jin XC,Jayasooriah, et al,Image analysis of tissue section (J).Computers in Biology and Medicine,1996 26(3) 269
[14]刘镰斧 多择图像识别技术与微机骨龄诊断系统 成都电讯工程学院研究生毕业论文 p34 1990年四月
[15] Moghaddamazadeh A , bourbakis N,A fuzzy region growing approach for segmentation of color images pattern recognition 1997,30(6) 867~881
[16] Zhu S C,Yuille A Region competition: unifying snakes region growing ,and bayes/MDL for multiband image segmentation,IEEE Trans. Pattern analysis Mach. Intell 1996,18（9）：884~900
[17] lyengar S S,Deng W. An efficient edge detection algorithm using relaxing labeling technique Pattern recognition 1995 28(4) 519~536
[18] Farag A A, Delp E J. Edge linking by sequential search Pattern recognition 1995 28(5) 611~633
[19] Tremeau A, Borel N. A region growing and merging algorithm to color segmentation Pattern Recognition 1997 30(7) 1191~1203
[20] Bimbo A D et al. Visual querying by color perceptive regions Pattern Recognition 1998 31(9) 1241~1253
[21] Shen X et al. Segmentation of 2D or 3D images through a
hierarchical clustering based on region modeling Pattern Recognition 1998 31(9) 1295~1309
[22] Urage S ,Zerubia J, Berthod M, A Markovian model for contour grouping Pattern recognition 1995 28(5) 683~693
[23] Chun D N. Yang H S. Robust images segmentation using genetic algorithm with a fuzzy measure . Pattern Recognition 1996 29(7) 1195~1211
[24] Zugaj D Lattuati V. A new approach of color images segmentation based on fusing region and edge segmentations outputs Pattern Recognition 1998 31(2) 105~113
[25]Maeda J et al Segmentation of natural images using anisorropic diffusion and linking of boundary edges Pattern Recognition 1998 31(12) 1993~1999
[26] G.K.Manos ,A.Y.Cairns,I.W.Rickets,D.Sinclair Segmenting radiographs of the hand and wrist Computer Methods and Programs in Biomedicine 1994 43 227~237
[27]罗炳伟 一种新的边缘检测方法 生物医学杂志
[28]李建平 小波分析与信号处理 重庆出版社 1997
[29] Kenneth.R.Castleman,朱志刚等译. 数字图像处理 电子工业出版社 1998.9
[30]杨文杰，刘浩学，马昆. 边界探测的小波变换方法 中国图像图形学报 Vol.4(a).No.1 Jan.1999
[2] Greulich.W.W. and S.I..Pyle. Radiographic Altas of Skeletal Development of Hand and Wrist(2stcd) Stanford University Press. Stanford Calif. 1959
[3] Acheson R.M. a method of assessing skeletal maturity from radiographs. J.Anat. 1954.88
[4] Tanner J.M. and R.H. Whitehouse. Standard for skeletal maturity part I .International Children's center. Paris . 1959
[5]Tanner J.M. and R.H. Whitehouse et al. Assessment of skeletal maturity and prediction of adult height(TW2 method) 2nd edition London. Academic Press. 1983
[6] 顾光宁等。中国人手与腕骨部之骨化 解剖学报 第五卷 第二期 173~183 1982
[7] 中国人手骨腕部骨龄标准的研究 中国人手腕骨骨龄标准研究课题组 1988
[8] Beunen G. and N.Cameron The reproducibility of TW2 skeletal age assessments log a self-taught assessor Annals of Human Biol. P.155-162 1980
[9]陈达明等 中日合作青少年体质研究联合调查报告 第一报 中华全国体育总会科教部.日本科学协会科学委员会 1987
[10] 曾凡辉 发育程度和发育高潮持续时间与发育类型的鉴别及对选材的
作用 优秀青少年运动员科学选材研究论文汇编 1983
[11] 张国栋 中小学生手和腕部骨骼发育变化规律的研究 学校卫生 1982年第2期
[12] 罗代升、何小海等 激光共焦显微生物医学图像的等高地图分割法 生物医学工程杂志 p500~503 2001 18卷第四期
[13] Ong SH.Jin XC,Jayasooriah, et al,Image analysis of tissue section (J).Computers in Biology and Medicine,1996 26(3) 269
[14]刘镰斧 多择图像识别技术与微机骨龄诊断系统 成都电讯工程学院研究生毕业论文 p34 1990年四月
[15] Moghaddamazadeh A , bourbakis N,A fuzzy region growing approach for segmentation of color images pattern recognition 1997,30(6) 867~881
[16] Zhu S C,Yuille A Region competition: unifying snakes region growing ,and bayes/MDL for multiband image segmentation,IEEE Trans. Pattern analysis Mach. Intell 1996,18（9）：884~900
[17] lyengar S S,Deng W. An efficient edge detection algorithm using relaxing labeling technique Pattern recognition 1995 28(4) 519~536
[18] Farag A A, Delp E J. Edge linking by sequential search Pattern recognition 1995 28(5) 611~633
[19] Tremeau A, Borel N. A region growing and merging algorithm to color segmentation Pattern Recognition 1997 30(7) 1191~1203
[20] Bimbo A D et al. Visual querying by color perceptive regions Pattern Recognition 1998 31(9) 1241~1253
[21] Shen X et al. Segmentation of 2D or 3D images through a
hierarchical clustering based on region modeling Pattern Recognition 1998 31(9) 1295~1309
[22] Urage S ,Zerubia J, Berthod M, A Markovian model for contour grouping Pattern recognition 1995 28(5) 683~693
[23] Chun D N. Yang H S. Robust images segmentation using genetic algorithm with a fuzzy measure . Pattern Recognition 1996 29(7) 1195~1211
[24] Zugaj D Lattuati V. A new approach of color images segmentation based on fusing region and edge segmentations outputs Pattern Recognition 1998 31(2) 105~113
[25]Maeda J et al Segmentation of natural images using anisorropic diffusion and linking of boundary edges Pattern Recognition 1998 31(12) 1993~1999
[26] G.K.Manos ,A.Y.Cairns,I.W.Rickets,D.Sinclair Segmenting radiographs of the hand and wrist Computer Methods and Programs in Biomedicine 1994 43 227~237
[27]罗炳伟 一种新的边缘检测方法 生物医学杂志
[28]李建平 小波分析与信号处理 重庆出版社 1997
[29] Kenneth.R.Castleman,朱志刚等译. 数字图像处理 电子工业出版社 1998.9
[30]杨文杰，刘浩学，马昆. 边界探测的小波变换方法 中国图像图形学报 Vol.4(a).No.1 Jan.1999