重症监护中X-射线胸部影像的计算机辅助诊断：自动检测导管支持器件定位
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摘要
便携式X射线影像在重症监护中大量地被使用于重病病人管理之中,用来标示需要立即改变的,意义重大和不可预见的状况。高效的病人管理的关键之一就是要有能力检测到插入病人体内的导管的正确位置,这些导管包括气管导管、进食管、鼻饲管以及其它导管。正确的导管定位能有助于确保为重症监护病人在治疗期输送或排出液体和气体。不正确的导管定位能导致病人不舒服,影响治疗效果,甚至危及病人生命。然而,由于病人本身、设备设置以及X射线曝光等等因素,导致便携式X射线AP位胸部影像的图像质量很差,临床医生很难从上面正确的判断导管以及其端点位置。因而有必要检测和标识导管的位置以协助临床医生。本论文的目的就是提出一种计算机辅助的方法去检测导管,识别导管类型。使用本方法能让临床医生更简便更准确地检测导管及其端点,从而改善在重症监护中对病人的管理。
Portable X-ray radiographs are heavily used in ICU for indicating significant or unexpected conditions requiring immediate changes in patient management. One concern for effective patient management relates to the ability to detect the proper positioning of tubes that have been inserted into the patient. These include, for example, endo-tracheal tubes (ET), feeding tubes (FT), nasogastric tubes (NT) and other tubes. Proper tube positioning can help to insure delivery or disposal of liquids and air/gases to and from the patient during a treatment procedure. Improper tube positioning can cause patient discomfort, render a treatment ineffective, or can even be life-threatening. However, because the poor image quality in portable AP x-ray images due to the variability in patient’s, apparatus positioning, and x-ray exposure, it is often hard for clinicians to visually detect the position of tube tips. Thus there is a need for detecting and identifying tube position and type to assist clinicians. The purpose of this paper is to present a computer-aided method for automated detection of tubes and identification of tube types. Use of this method may allow clinicians to detect the tube tips more easily and accurately, thus improving the quality of patient management in ICU.
Portable X-ray radiographs are heavily used in ICU for indicating significant or unexpected conditions requiring immediate changes in patient management. One concern for effective patient management relates to the ability to detect the proper positioning of tubes that have been inserted into the patient. These include, for example, endo-tracheal tubes (ET), feeding tubes (FT), nasogastric tubes (NT) and other tubes. Proper tube positioning can help to insure delivery or disposal of liquids and air/gases to and from the patient during a treatment procedure. Improper tube positioning can cause patient discomfort, render a treatment ineffective, or can even be life-threatening. However, because the poor image quality in portable AP x-ray images due to the variability in patient’s, apparatus positioning, and x-ray exposure, it is often hard for clinicians to visually detect the position of tube tips. Thus there is a need for detecting and identifying tube position and type to assist clinicians. The purpose of this paper is to present a computer-aided method for automated detection of tubes and identification of tube types. Use of this method may allow clinicians to detect the tube tips more easily and accurately, thus improving the quality of patient management in ICU.
引文
[1] CR 在急诊床边摄影中的应用,肖玉辉,陈晓武,许孟军,曾江镇,周水添,中华现代影像学杂志.
[2] 计算机辅助诊断在医学影像学中的应用 姜兴岳(综述),耿道颖(审校) 中华现代影像学杂志 2005 年第 2 卷第 9 期
[3] Brem RF,Baum J,Lechner M,et al.Improvement in sensitivity of screening mammography with computer-aided detection: a multiinstitutional trial.Am J Roentgenol,2003,181:687-693
[4] Bram VG,Bart MH.Computer-aided diagnosis in chest radiography: a survey.IEEE Transaction on Medical Imaging,2001,20:1228-1241.
[5] Doi K,MacMahon H,Katsuragawa S, et al.Computer-aided diagnosis in radiology: potential and pitfalls.European Journal of Radiology,1999,31:97-109.
[6] Quckel L,Kessels A,Goci R,et al.Miss rate of lung cancer on the radiography in clinical practice.Chest,1999,115:720-724.
[7] Geoffrey D , Rubin,John K , at al.Pulmonary nodules on multi-detector row CT scans:performance comparison of radiologists and computer-aided detection.Radiology,2005,234:274-283.
[8] Nakamura K,Yoshida H,Engelmann R,et al.Computerized analysis of the likelihood of malignancy in solitary pulmonary nodules with use of artificial neural networks.Radiology,2000,214:823-830.
[9] Ashizawa K,MacMahon H,Ishida T,et al.Effect of an artificial neural network on radiologists performance in the differential diagnosis of interstitial lung disease using chest radiographs.AJR,1999,172:1311-1316.
[10] Monnier-Cholley L,MacMahon H,Katsuragawa S,et al.Computer-aided diagnosis for detection of interstitial pacities on chest radiographs. AJR,1998,171:1651-1657.
[11] Karssemeijier N,Otten JDM,Verbeek MD,et al.Computer-aided detection versus independent double reading of masses on mammograms.Radiology,2003,227:192-200.
[12] Jiang Y,Nishikawa RM,Schmidt RA,et al.Potential of Computer-aided diagnosis to reduce variability in radiologists interpretations of mammograms depicting microcacifications.Radiology,2001,220:787-794.
[13] Hadjiiki L,Chan HP,Sahiner B,et al.Improvement in radiologists′ characterization of malignant and benign breast masses on serial mammograms with computer-aided diagnosis:an ROC study.Radiology,2004,233: 255-265.
[14] Karel Zuiderveld, "Contrast Limited Adaptive Histogram Equalization", Graphics Gems IV, p. 474-485, code: p. 479-484
[15] Yee J,Akerkar GA,Hung RK,et al.Colorectal neoplasia: performance characteristics of CT colonography for detection in 300 patients.Radiology,2001,219: 685-692.
[16] Spinzi G,Belloni G,Martegani A,et al.Computed tomographic colonography and conventional colonoscopy for colon diseases: a prospective,blinded study.Am J Gastroenterol,2001,96:394-400.
[17] Chen EL,Chung CL,Chen CL,et al.An automatic diagnostic system for CT liver image classification.IEEE Tran Biomed Eng,1998,45: 783-794.
[18] Hong JS,Kaneko T,Sekiguchi R,et al.Automatic liver tumor detection from CT.IEICE Trans Inf & Syst,2001,84: 741-748.
[19] Siromoney A,Rughurum L,Siromoney A,et al.Inductive logic programming for knowledge discovery from MRI data.IEEE Ingineering in Medicine and Biology,2000,10: 72-77.
[20] 机器视觉 贾云得 科学出版社
[21] Varsha Kamat and Subramaniam Ganesan "A Robust Hough Transform Technique for Description of Multiple Line Segments in an Image", IEEE
[22] Canny, John, "A Computational Approach to Edge Detection," IEEE Transactions on Pattern Analysis and Machine Intelligence,Vol. PAMI-8, No. 6, 1986, pp. 679-698.
[23] Lim, Jae S., Two-Dimensional Signal and Image Processing, Englewood Cliffs, NJ, Prentice Hall, 1990, pp. 478-488.
[24] Parker, James R., Algorithms for Image Processing and Computer Vision, New York, John Wiley & Sons, Inc., 1997, pp. 23-29.
[25] D.Demigny, L.Kessal, and J.Pons, Fast Recursive Implementation of the Gaussian Filter, Montpellier, France, Dec. 2001, 339-346
[26] D.Demigny and M.Karabernou, An Effective Resolution Definition or How to Choose an Edge Detectior, It's Scale Parameter and the Threshold, Proc. IEEE. Int's Conf. Image Processing, Vol. 1, Lausanne, Sept. 1996, 829-832Kenneth R.Clastleman. Digital image processing, Prentice-Hall International, Inc.2001.
[27] 孙兆林, MATLAB 6.x 图像处理,北京清华大学出版社,2002
[28] R.J.Wall, A.Klinger, and K.R.Castleman, Analysis of Image Histograms, Proc. Second Int. Gong.on.Pattern Recogintion, Copenhagen,1974
[29] E. H. Hall, Almost uniform distribution for computer image enhancement. IEEE Trans. Comput. 23(2), 1974, 207-208.
[30] Stephen M.Pizer. E.Philip Amburn, John D.Austin, Robert Cromartie. Adaptive histogram Equalization and its variations. Computer Vision, Graphics, and Image Processing (CVGIP), 1987,39:355-368
[31] 韩晓微. 彩色图像处理关键技术研究. 东北大学博士论文,2005 年 1 月.
[32] 任明武. 数字图像处理.第 1 版. 南京:南京理工大学出版社,2003
[33] 吴立德计算机视觉 上海:复口人学出版利 1993.
[34] Henschke CI, Pasternack GS, Schroeder S, et al. Bedside chest radiography: diagnostic efficacy. Radiology. 1983;149:23-26.
[35] Bekemeyer WB, Crapo RD, Calhoon S, et al. Efficacy of chest radiography in a respiratory intensive care unit. Chest. 1985;88:691-696.
[36] Strain DS, Kinasewitz GT, Vereen LE, et al. Value of routine daily chest x-rays in themedical intensive care unit. Crit Care Med. 1985;13:534-536.
[37] Hall JB, White SR, Karrison T. Efficacy of daily routine chest radiographs in intubated, mechanically ventilated patients. Crit Care Med. 1991;19(5):689-693.
[38] Silverstein DS, Livingston DH, Elcavage J, et al. The utility of routine daily chest radiography in the surgical intensive care unit. J Trauma. 1993;35(4):643-646.
[39] Wandtke J. Bedside chest radiography. Radiology. 1994;190:1-10.
[40] Steckel R. The current applications of PACS to radiology practice. Radiology. 1994;190:50A-54A.
[41] Kundel HL, Gefter W, Aronchick J, et al. Accuracy of bedside chest hard-copy screen-film versus hard- and softcopy computed radiographs in a medical intensive care unit: receiver operating
[2] 计算机辅助诊断在医学影像学中的应用 姜兴岳(综述),耿道颖(审校) 中华现代影像学杂志 2005 年第 2 卷第 9 期
[3] Brem RF,Baum J,Lechner M,et al.Improvement in sensitivity of screening mammography with computer-aided detection: a multiinstitutional trial.Am J Roentgenol,2003,181:687-693
[4] Bram VG,Bart MH.Computer-aided diagnosis in chest radiography: a survey.IEEE Transaction on Medical Imaging,2001,20:1228-1241.
[5] Doi K,MacMahon H,Katsuragawa S, et al.Computer-aided diagnosis in radiology: potential and pitfalls.European Journal of Radiology,1999,31:97-109.
[6] Quckel L,Kessels A,Goci R,et al.Miss rate of lung cancer on the radiography in clinical practice.Chest,1999,115:720-724.
[7] Geoffrey D , Rubin,John K , at al.Pulmonary nodules on multi-detector row CT scans:performance comparison of radiologists and computer-aided detection.Radiology,2005,234:274-283.
[8] Nakamura K,Yoshida H,Engelmann R,et al.Computerized analysis of the likelihood of malignancy in solitary pulmonary nodules with use of artificial neural networks.Radiology,2000,214:823-830.
[9] Ashizawa K,MacMahon H,Ishida T,et al.Effect of an artificial neural network on radiologists performance in the differential diagnosis of interstitial lung disease using chest radiographs.AJR,1999,172:1311-1316.
[10] Monnier-Cholley L,MacMahon H,Katsuragawa S,et al.Computer-aided diagnosis for detection of interstitial pacities on chest radiographs. AJR,1998,171:1651-1657.
[11] Karssemeijier N,Otten JDM,Verbeek MD,et al.Computer-aided detection versus independent double reading of masses on mammograms.Radiology,2003,227:192-200.
[12] Jiang Y,Nishikawa RM,Schmidt RA,et al.Potential of Computer-aided diagnosis to reduce variability in radiologists interpretations of mammograms depicting microcacifications.Radiology,2001,220:787-794.
[13] Hadjiiki L,Chan HP,Sahiner B,et al.Improvement in radiologists′ characterization of malignant and benign breast masses on serial mammograms with computer-aided diagnosis:an ROC study.Radiology,2004,233: 255-265.
[14] Karel Zuiderveld, "Contrast Limited Adaptive Histogram Equalization", Graphics Gems IV, p. 474-485, code: p. 479-484
[15] Yee J,Akerkar GA,Hung RK,et al.Colorectal neoplasia: performance characteristics of CT colonography for detection in 300 patients.Radiology,2001,219: 685-692.
[16] Spinzi G,Belloni G,Martegani A,et al.Computed tomographic colonography and conventional colonoscopy for colon diseases: a prospective,blinded study.Am J Gastroenterol,2001,96:394-400.
[17] Chen EL,Chung CL,Chen CL,et al.An automatic diagnostic system for CT liver image classification.IEEE Tran Biomed Eng,1998,45: 783-794.
[18] Hong JS,Kaneko T,Sekiguchi R,et al.Automatic liver tumor detection from CT.IEICE Trans Inf & Syst,2001,84: 741-748.
[19] Siromoney A,Rughurum L,Siromoney A,et al.Inductive logic programming for knowledge discovery from MRI data.IEEE Ingineering in Medicine and Biology,2000,10: 72-77.
[20] 机器视觉 贾云得 科学出版社
[21] Varsha Kamat and Subramaniam Ganesan "A Robust Hough Transform Technique for Description of Multiple Line Segments in an Image", IEEE
[22] Canny, John, "A Computational Approach to Edge Detection," IEEE Transactions on Pattern Analysis and Machine Intelligence,Vol. PAMI-8, No. 6, 1986, pp. 679-698.
[23] Lim, Jae S., Two-Dimensional Signal and Image Processing, Englewood Cliffs, NJ, Prentice Hall, 1990, pp. 478-488.
[24] Parker, James R., Algorithms for Image Processing and Computer Vision, New York, John Wiley & Sons, Inc., 1997, pp. 23-29.
[25] D.Demigny, L.Kessal, and J.Pons, Fast Recursive Implementation of the Gaussian Filter, Montpellier, France, Dec. 2001, 339-346
[26] D.Demigny and M.Karabernou, An Effective Resolution Definition or How to Choose an Edge Detectior, It's Scale Parameter and the Threshold, Proc. IEEE. Int's Conf. Image Processing, Vol. 1, Lausanne, Sept. 1996, 829-832Kenneth R.Clastleman. Digital image processing, Prentice-Hall International, Inc.2001.
[27] 孙兆林, MATLAB 6.x 图像处理,北京清华大学出版社,2002
[28] R.J.Wall, A.Klinger, and K.R.Castleman, Analysis of Image Histograms, Proc. Second Int. Gong.on.Pattern Recogintion, Copenhagen,1974
[29] E. H. Hall, Almost uniform distribution for computer image enhancement. IEEE Trans. Comput. 23(2), 1974, 207-208.
[30] Stephen M.Pizer. E.Philip Amburn, John D.Austin, Robert Cromartie. Adaptive histogram Equalization and its variations. Computer Vision, Graphics, and Image Processing (CVGIP), 1987,39:355-368
[31] 韩晓微. 彩色图像处理关键技术研究. 东北大学博士论文,2005 年 1 月.
[32] 任明武. 数字图像处理.第 1 版. 南京:南京理工大学出版社,2003
[33] 吴立德计算机视觉 上海:复口人学出版利 1993.
[34] Henschke CI, Pasternack GS, Schroeder S, et al. Bedside chest radiography: diagnostic efficacy. Radiology. 1983;149:23-26.
[35] Bekemeyer WB, Crapo RD, Calhoon S, et al. Efficacy of chest radiography in a respiratory intensive care unit. Chest. 1985;88:691-696.
[36] Strain DS, Kinasewitz GT, Vereen LE, et al. Value of routine daily chest x-rays in themedical intensive care unit. Crit Care Med. 1985;13:534-536.
[37] Hall JB, White SR, Karrison T. Efficacy of daily routine chest radiographs in intubated, mechanically ventilated patients. Crit Care Med. 1991;19(5):689-693.
[38] Silverstein DS, Livingston DH, Elcavage J, et al. The utility of routine daily chest radiography in the surgical intensive care unit. J Trauma. 1993;35(4):643-646.
[39] Wandtke J. Bedside chest radiography. Radiology. 1994;190:1-10.
[40] Steckel R. The current applications of PACS to radiology practice. Radiology. 1994;190:50A-54A.
[41] Kundel HL, Gefter W, Aronchick J, et al. Accuracy of bedside chest hard-copy screen-film versus hard- and softcopy computed radiographs in a medical intensive care unit: receiver operating