基于胸片图像的身份识别研究
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摘要
X线胸片是临床上使用最多的医学成像诊断方法之一,放射科每天产生大量胸片图像,一旦发生归档错误,将产生严重的后果。本文研究了基于DICOM胸片图像的患者身份识别技术,它能够有效地辅助减少这类误归档错误。研究内容主要包括胸片图像的预处理、分割和身份识别。
在胸片的预处理和分割方面,首先,本文通过回顾和研究了以往的经典算法,综合了投影法、聚类法、阈值分割、梯度法、模板匹配、边缘追踪等多种方法实现了胸片ROI的提取、肺野的分割、肋骨锁骨边缘的提取等,取得了良好的效果。其次,本文对图像分割的新方法,如像素点分类法、活动模型法,进行了研究,并标定了12幅胸片的肺野使用AAM模型对另外10幅图像进行分割实验,其中9幅取得了很好的分割效果,只有一幅标定点略有偏移。
胸片图像的身份识别技术研究是个新颖的课题,没有过多的经验可以参考。本文参考医生临床上实际辨别方法并借鉴指纹识别和人脸识别的方法,对胸片识别进行了尝试性研究。文章从两个角度展开研究:第一种方法,我们通过胸片图像分割获得了肺野、肋骨和锁骨的信息,从这些信息中提取面积、边缘形态等特征,构建数据库,再从中选择部分重要特征进行识别;第二种方法模仿医生肉眼分辨胸片身份的方法,对比两幅图像中的胸骨框架外形、心脏形状、动脉形状、锁骨和肋骨的弯曲程度长度等等,对两幅图像上的对应部位计算相关系数,并以此为特征,根据一定的规则判别图像是否来自同一个患者,实现了两幅图像之间的身份识别。
Chest radiography is one of the most popular medical imaging diagnosis methods in clinical practice. Large amount of chest radiographs are produced in X-ray department every day. Mistakes in archiving these images will lead to serious consequence. In this paper identity recognition based on DICOM chest radiography is researched, which is helpful to reduce mis-archiving. For this purpose, preprocess, segmentation and identification of chest radiographies is discussed.
Preprocess and segmentation are discussed in this paper. Firstly, many classical algorithms are reviewed. Combining With these algorithms, such as profiling, clustering, threshold segmentation, gradient edge detection, model matching, edge tracing, A new method is put forward to extract ROI of anatomical structure, segmentation of lung field, edges of ribs and clavicle. This integrated method produces wonderful results. Furthermore, new methods, such as pixel classification and active models, are adopted in this paper. Lung fields in 12 chest CR images are marked to train an AAM model and other 10 images are inputted for testing. We get a good segmentation result that only one mismatchs is identified.
Identity recognition of chest radiography is a very new topic and little research experience can be found. In this paper, it is attempted to study patients’identity recognition through imitating how doctors do in clinic and how researchers do in fingerprint recognition and face recognition area. This problem
在胸片的预处理和分割方面,首先,本文通过回顾和研究了以往的经典算法,综合了投影法、聚类法、阈值分割、梯度法、模板匹配、边缘追踪等多种方法实现了胸片ROI的提取、肺野的分割、肋骨锁骨边缘的提取等,取得了良好的效果。其次,本文对图像分割的新方法,如像素点分类法、活动模型法,进行了研究,并标定了12幅胸片的肺野使用AAM模型对另外10幅图像进行分割实验,其中9幅取得了很好的分割效果,只有一幅标定点略有偏移。
胸片图像的身份识别技术研究是个新颖的课题,没有过多的经验可以参考。本文参考医生临床上实际辨别方法并借鉴指纹识别和人脸识别的方法,对胸片识别进行了尝试性研究。文章从两个角度展开研究:第一种方法,我们通过胸片图像分割获得了肺野、肋骨和锁骨的信息,从这些信息中提取面积、边缘形态等特征,构建数据库,再从中选择部分重要特征进行识别;第二种方法模仿医生肉眼分辨胸片身份的方法,对比两幅图像中的胸骨框架外形、心脏形状、动脉形状、锁骨和肋骨的弯曲程度长度等等,对两幅图像上的对应部位计算相关系数,并以此为特征,根据一定的规则判别图像是否来自同一个患者,实现了两幅图像之间的身份识别。
Chest radiography is one of the most popular medical imaging diagnosis methods in clinical practice. Large amount of chest radiographs are produced in X-ray department every day. Mistakes in archiving these images will lead to serious consequence. In this paper identity recognition based on DICOM chest radiography is researched, which is helpful to reduce mis-archiving. For this purpose, preprocess, segmentation and identification of chest radiographies is discussed.
Preprocess and segmentation are discussed in this paper. Firstly, many classical algorithms are reviewed. Combining With these algorithms, such as profiling, clustering, threshold segmentation, gradient edge detection, model matching, edge tracing, A new method is put forward to extract ROI of anatomical structure, segmentation of lung field, edges of ribs and clavicle. This integrated method produces wonderful results. Furthermore, new methods, such as pixel classification and active models, are adopted in this paper. Lung fields in 12 chest CR images are marked to train an AAM model and other 10 images are inputted for testing. We get a good segmentation result that only one mismatchs is identified.
Identity recognition of chest radiography is a very new topic and little research experience can be found. In this paper, it is attempted to study patients’identity recognition through imitating how doctors do in clinic and how researchers do in fingerprint recognition and face recognition area. This problem
引文
[1] Abe H., MacMahon H., Engelmann R., etc. Computer-aided diagnosis in chest radiography: results of large-scale observer tests at the 1996-2001 RSNA scientific assemblies[J]. Radiographics, 2003 ,23 (1) :255-265
[2] Zhanjun Yue, Ardeshir Goshtasby, Laurens V. Ackermen. Automatic Detection of Rib Borders in Chest Radiography[J]. IEEE Trans .Medical Imaging, 1995 , 14(3) :525-536.
[3] Armato S. G. III , Giger M. L., MacMahon H., Automated lung segmentation in digitized posteroanterior chest radiographs [J ] . Academic Radiology. 1998. 5 (4):245-255.
[4] McNitt-Gray M. F., Huang H. K., Sayre J. W. Feature selection in the pattern classification problem of digital chest radiograph segmentation[J]. IEEE Trans. Medical Imaging, 1995 ,14(3) :537 - 547.
[5] Samuel G. Armato III, William F. Sensakovic. Automated Lung Segmentation for Thoracic CT: Impact on Computer-Aided Diagnosis[J]. Academic Radiology. 2004. 11(9):1011–1021.
[6] Shiying Hu, Eric A. Hoffman, and Joseph M. Reinhardt*. Automatic Lung Segmentation for Accurate Quantitation of Volumetric X-Ray CT Images[J]. IEEE TRANSACTIONS ON MEDICAL IMAGING. 2001. 20(6):490-498.
[7] T. Okumura, T. Miwa, Jun-ichi Kako. Automatic Detecion of Lung Cancers in Chest CT Images by Variable N-Quoit Filter[J]. Pattern Recognition, 1998. Proceedings[C]. Fourteenth International Conference on, 1998, 2:1671-1673.
[8] Lee Y., Hara T., Fujita H., et al . Automated detection of pulmonary nodules in helical CT images based on an improved template – matching technique[J]. IEEE Transations on medical imaging . 2001. 20 (7) : 595- 604.
[9] 康维,王广志,丁辉. 乳腺 X 线成像的计算机辅助诊断技术研究进展[J]. 北京生物医学工程. 2006. 25(2):213-216.
[10] 田捷,杨鑫. 生物特征识别技术理论与应用[M]. 电子工业出版社. 2005.
[11] 荣独山, X 线诊断学 第一部 胸部[M], 上海科学技术出版社,1993
[12] 庄天戈,计算机在生物医学中的应用(第二版)[M],科学出版社,2000.
[13] 章毓晋,图像工程(中册)图像分析(第 2 版)[M],清华大学出版社,2005
[14] X. W. Xu, K. Doi. Image feature analysis for computer-aided diagnosis: accurate determination of ribcage boundary in chest radiographs[J]. Medical Physics. 1995 22(5):617-626.
[15] Li L., Zheng Y., Kallergi M., Clark R.A. Improved method for automatic identification of lung regions on chest radiographs[J]. Academic Radiology. 2001.8(7):629-638.
[16] Ramachandran J., Pattichis M., Soliz P., Wilson M. A hierarchical segmentation model for the lung and the inter-costal parenchymal regions of chest radiographs[J]. Midwest Symposium on Circuits and Systems. 2002. 1:I439-I442.
[17] Van Ginneken B., Katsuragawa S., Ter Haar Romeny B.M., Doi K., Viergever M.A. Automatic detection of abnormalities in chest radiographs using local texture analysis[J]. IEEE Transactions on Medical Imaging. 2002 . 21(2):139-149.
[18] Campadelli P., Casiraghi E. Lung field segmentation in digital Postero-Anterior chest radiographs[J]. Lecture Notes in Computer Science. 2005. 3687(PART II):736-745.
[19] Schilham A.M., van Ginneken B., Loog M. A computer-aided diagnosis system for detection of lung nodules in chest radiographs with an evaluation on a public database[J]. Medical Image Analysis. 2006 . 10(2):247-258.
[20] Xin-Wei Xu, Kunio Doi.Image feature analysis for computer-aided diagnosis: Detection of right and left hemidiaphragm edges and delineation of lung field in chest radiographs[J]. Medical Physics. 1996. 23(9):1613-1624.
[21] S.G. Armato,M.L. Giger, H. MacMahon. Computerized delineation and analysis of costophrenic angles in digital chest radiographs[J]. Academic Radiology. 1998. 5:329-335.
[22] Brown M.S., Wilson L.S., Doust B.D., Gill R.W., Sun C. Knowledge-based method for segmentation and analysis of lung boundaries in chest X-ray images. Computerized Medical Imaging and Graphics[J]. 1998. 22(6):463-477
[23] S.G. Armato, M.L. Giger, K. Ashizawa, H. MacMahon. Automated lung segmentation in digital lateral chest radiographs[J], Medical Physics. 1998,24(8):1507-1520.
[24] F.M. Carrascal, J.M. Carreira, M. Souto, P.G. Tahoces, L. Gomez, J. J. Vidal. Automatic calculation of total lung capacity from automatically traced lung boundaries in postern-anterior and lateral digital chest radiographs[J]. Medical Physics. 1998.25(7):1118-1131.
[25] Bram van Ginneken, Mikkel B. Stegmann, and Marco Loog. Segmentation of anatomical structures in chestradiographs using supervised methods: acomparative study on a public database[J]. Medical Image Analysis. 2006. 10: 19–40.
[26] 王鑫,庄天戈.基于解剖结构知识的 X 光胸片中肺部肋骨边缘检测[J].航天医学与医学工程,2005,18(6):456-460.
[27] Tsujii, Osamu; Freedman, Matthew T.; Mun, Seong K. Automated segmentation of anatomic regions in chest radiographs using an adaptive-sized hybrid neural network[J]. Med. phys. 1998, 25(6): 998-1007
[28] Canny J. A computational approach to edge detection[J]. IEEE-PAMI,1986,8(6):679-698
[29] 王鑫,X-线胸片图像中肺部区域肋骨提取的研究[D],上海交通大学硕士学位论文,2005.
[30] Ana Maria Mendon?a, Jorge Alves Silva, Aurélio Campilho. Automatic Delimitation of Lung Fields on Chest Radiographs[J]. IEEE International Symposium on Biomedical Imaging, 2004:1287-1290.
[31] B. van Ginneken. Computer-Aided Diagnosis in Chest Radiography, PhD thesis[D]. Utrecht University, The Netherlands, 2001.
[32] Hideyuki. SAKAIDA, Akira. OOSAWA, Kazuo. SHIMUR. A. Rib shape recognition in lung x-ray images for intelligent assistance[J]. Medical Imaging 2006: PACS and Imaging Informatics, Proc. of SPIE. 2006,6145(61451H):1-11.
[33] 秦翊麟. 图像分割及其在虚拟人肌肉识别与胸片特征辨识中的应用研究[D]. 上海交通大学硕士论文. 2006.
[34] N.F. Vittitoe, R. Vargas-Voracek, C.E. Floyd Jr., Identification of lung regions in chest radiographs using Markov Random Field modeling[J], Medical Physics, 1998. 25(6): 976-985.
[35] B. van Ginneken and B. M. ter Haar Romeny. Automatic segmentation of lung fields in chest radiographs[J]. Medical Physics. , 2000. 27(10):2445–2455.
[36] 戴昌达, 姜小光, 唐伶俐. 遥感图像应用处理与分析[M]. 清华大学出版社.2004.
[37] T.F. Cootes and C.J.Taylor. Statistical Models of Appearance for Computer Vision [R].University of Manchester,2004
[38] Mikkel B. Stegmann. Generative Interpretation of Medical Images, Ph.D. Thesis[D].Technical University of Denmark, Denmark.2004
[39] M. B. Stegmann, B. K. Ersb?ll, and R. Larsen. FAME – a flexible appearance modelling environment[J]. IEEE Transactions on Medical Imaging, 22(10):1319–1331, 2003.
[40] 赵春江,施文康,邓勇 新的梯度边缘检测方法[J] 光电工程 ,2005,32(4).
[41] 罗述谦,吕维雪.医学图像配准技术[J].海外医学生物医学工程分册,1999,22(1):1-8.
[42] 飞思科技产品研发中心.神经网络理论与 MATLAB 7 实现[M].电子工业出版社,2005
[43] 闻新等. MATLAB 神经网络应用设计[M]. 科学出版社 .
[44] 陈旭,弥漫性肺疾病高分辨率 CT 影像的定量分析[D],上海交通大学博士学位论文,2002.
[2] Zhanjun Yue, Ardeshir Goshtasby, Laurens V. Ackermen. Automatic Detection of Rib Borders in Chest Radiography[J]. IEEE Trans .Medical Imaging, 1995 , 14(3) :525-536.
[3] Armato S. G. III , Giger M. L., MacMahon H., Automated lung segmentation in digitized posteroanterior chest radiographs [J ] . Academic Radiology. 1998. 5 (4):245-255.
[4] McNitt-Gray M. F., Huang H. K., Sayre J. W. Feature selection in the pattern classification problem of digital chest radiograph segmentation[J]. IEEE Trans. Medical Imaging, 1995 ,14(3) :537 - 547.
[5] Samuel G. Armato III, William F. Sensakovic. Automated Lung Segmentation for Thoracic CT: Impact on Computer-Aided Diagnosis[J]. Academic Radiology. 2004. 11(9):1011–1021.
[6] Shiying Hu, Eric A. Hoffman, and Joseph M. Reinhardt*. Automatic Lung Segmentation for Accurate Quantitation of Volumetric X-Ray CT Images[J]. IEEE TRANSACTIONS ON MEDICAL IMAGING. 2001. 20(6):490-498.
[7] T. Okumura, T. Miwa, Jun-ichi Kako. Automatic Detecion of Lung Cancers in Chest CT Images by Variable N-Quoit Filter[J]. Pattern Recognition, 1998. Proceedings[C]. Fourteenth International Conference on, 1998, 2:1671-1673.
[8] Lee Y., Hara T., Fujita H., et al . Automated detection of pulmonary nodules in helical CT images based on an improved template – matching technique[J]. IEEE Transations on medical imaging . 2001. 20 (7) : 595- 604.
[9] 康维,王广志,丁辉. 乳腺 X 线成像的计算机辅助诊断技术研究进展[J]. 北京生物医学工程. 2006. 25(2):213-216.
[10] 田捷,杨鑫. 生物特征识别技术理论与应用[M]. 电子工业出版社. 2005.
[11] 荣独山, X 线诊断学 第一部 胸部[M], 上海科学技术出版社,1993
[12] 庄天戈,计算机在生物医学中的应用(第二版)[M],科学出版社,2000.
[13] 章毓晋,图像工程(中册)图像分析(第 2 版)[M],清华大学出版社,2005
[14] X. W. Xu, K. Doi. Image feature analysis for computer-aided diagnosis: accurate determination of ribcage boundary in chest radiographs[J]. Medical Physics. 1995 22(5):617-626.
[15] Li L., Zheng Y., Kallergi M., Clark R.A. Improved method for automatic identification of lung regions on chest radiographs[J]. Academic Radiology. 2001.8(7):629-638.
[16] Ramachandran J., Pattichis M., Soliz P., Wilson M. A hierarchical segmentation model for the lung and the inter-costal parenchymal regions of chest radiographs[J]. Midwest Symposium on Circuits and Systems. 2002. 1:I439-I442.
[17] Van Ginneken B., Katsuragawa S., Ter Haar Romeny B.M., Doi K., Viergever M.A. Automatic detection of abnormalities in chest radiographs using local texture analysis[J]. IEEE Transactions on Medical Imaging. 2002 . 21(2):139-149.
[18] Campadelli P., Casiraghi E. Lung field segmentation in digital Postero-Anterior chest radiographs[J]. Lecture Notes in Computer Science. 2005. 3687(PART II):736-745.
[19] Schilham A.M., van Ginneken B., Loog M. A computer-aided diagnosis system for detection of lung nodules in chest radiographs with an evaluation on a public database[J]. Medical Image Analysis. 2006 . 10(2):247-258.
[20] Xin-Wei Xu, Kunio Doi.Image feature analysis for computer-aided diagnosis: Detection of right and left hemidiaphragm edges and delineation of lung field in chest radiographs[J]. Medical Physics. 1996. 23(9):1613-1624.
[21] S.G. Armato,M.L. Giger, H. MacMahon. Computerized delineation and analysis of costophrenic angles in digital chest radiographs[J]. Academic Radiology. 1998. 5:329-335.
[22] Brown M.S., Wilson L.S., Doust B.D., Gill R.W., Sun C. Knowledge-based method for segmentation and analysis of lung boundaries in chest X-ray images. Computerized Medical Imaging and Graphics[J]. 1998. 22(6):463-477
[23] S.G. Armato, M.L. Giger, K. Ashizawa, H. MacMahon. Automated lung segmentation in digital lateral chest radiographs[J], Medical Physics. 1998,24(8):1507-1520.
[24] F.M. Carrascal, J.M. Carreira, M. Souto, P.G. Tahoces, L. Gomez, J. J. Vidal. Automatic calculation of total lung capacity from automatically traced lung boundaries in postern-anterior and lateral digital chest radiographs[J]. Medical Physics. 1998.25(7):1118-1131.
[25] Bram van Ginneken, Mikkel B. Stegmann, and Marco Loog. Segmentation of anatomical structures in chestradiographs using supervised methods: acomparative study on a public database[J]. Medical Image Analysis. 2006. 10: 19–40.
[26] 王鑫,庄天戈.基于解剖结构知识的 X 光胸片中肺部肋骨边缘检测[J].航天医学与医学工程,2005,18(6):456-460.
[27] Tsujii, Osamu; Freedman, Matthew T.; Mun, Seong K. Automated segmentation of anatomic regions in chest radiographs using an adaptive-sized hybrid neural network[J]. Med. phys. 1998, 25(6): 998-1007
[28] Canny J. A computational approach to edge detection[J]. IEEE-PAMI,1986,8(6):679-698
[29] 王鑫,X-线胸片图像中肺部区域肋骨提取的研究[D],上海交通大学硕士学位论文,2005.
[30] Ana Maria Mendon?a, Jorge Alves Silva, Aurélio Campilho. Automatic Delimitation of Lung Fields on Chest Radiographs[J]. IEEE International Symposium on Biomedical Imaging, 2004:1287-1290.
[31] B. van Ginneken. Computer-Aided Diagnosis in Chest Radiography, PhD thesis[D]. Utrecht University, The Netherlands, 2001.
[32] Hideyuki. SAKAIDA, Akira. OOSAWA, Kazuo. SHIMUR. A. Rib shape recognition in lung x-ray images for intelligent assistance[J]. Medical Imaging 2006: PACS and Imaging Informatics, Proc. of SPIE. 2006,6145(61451H):1-11.
[33] 秦翊麟. 图像分割及其在虚拟人肌肉识别与胸片特征辨识中的应用研究[D]. 上海交通大学硕士论文. 2006.
[34] N.F. Vittitoe, R. Vargas-Voracek, C.E. Floyd Jr., Identification of lung regions in chest radiographs using Markov Random Field modeling[J], Medical Physics, 1998. 25(6): 976-985.
[35] B. van Ginneken and B. M. ter Haar Romeny. Automatic segmentation of lung fields in chest radiographs[J]. Medical Physics. , 2000. 27(10):2445–2455.
[36] 戴昌达, 姜小光, 唐伶俐. 遥感图像应用处理与分析[M]. 清华大学出版社.2004.
[37] T.F. Cootes and C.J.Taylor. Statistical Models of Appearance for Computer Vision [R].University of Manchester,2004
[38] Mikkel B. Stegmann. Generative Interpretation of Medical Images, Ph.D. Thesis[D].Technical University of Denmark, Denmark.2004
[39] M. B. Stegmann, B. K. Ersb?ll, and R. Larsen. FAME – a flexible appearance modelling environment[J]. IEEE Transactions on Medical Imaging, 22(10):1319–1331, 2003.
[40] 赵春江,施文康,邓勇 新的梯度边缘检测方法[J] 光电工程 ,2005,32(4).
[41] 罗述谦,吕维雪.医学图像配准技术[J].海外医学生物医学工程分册,1999,22(1):1-8.
[42] 飞思科技产品研发中心.神经网络理论与 MATLAB 7 实现[M].电子工业出版社,2005
[43] 闻新等. MATLAB 神经网络应用设计[M]. 科学出版社 .
[44] 陈旭,弥漫性肺疾病高分辨率 CT 影像的定量分析[D],上海交通大学博士学位论文,2002.