DICOM格式医学图像显示方法研究
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摘要
研究DICOM格式图像的非线性开窗显示方法,并同目前常用的线性开窗显示方法进行比较。本研究提出了逆线性、指数型、对数型、幂律型、S型、倒S型和基于复杂曲线的伪彩色非线性开窗显示方法,并比较了各种开窗显示方法。仿真结果表明,线性开窗显示适合于较亮背景中的低密度病变细节显示,对低密度病变增强效果较好。逆线性开窗显示刚好相反。对数型开窗显示能够将窗口中较窄范围的低密度值映射到较宽范围的输出密度值来显示,对窗口中的低密度病变增强效果好,而指数型开窗显示相反。幂律型开窗显示根据幂的取值不同分别类似于线性、指数型和对数型开窗显示。S型和倒S型开窗显示对窗口中的中等密度的病变增强效果好。基于复杂曲线的伪彩色显示方法,能够在较宽的窗口中显示较多的病变信息。非线性方法能够显示更多的图像信息,是线性方法的较好补充。
The non-linear window display methods of DICOM images were studied and compared with the commonly used linear display methods. The authors analyzed the methods for inverse linear, exponential, logarithmic, power-law, S function,inverse S function and complex curve-based pseudo color window displays, and compared these methods with linear methods of window display. The experimental results demonstrated that linear window display had good performance in displaying the details of low- density lesions against a bright background with a good lesion enhancement effect, while non- linear window display suited for displaying high- density lesions in dark background. The logarithmic window display was capable of mapping low gray-level values in a narrow range in the window of an input image to a wider range of output values, and suited particularly for enhancing low gray- level lesions; the exponential window display method better suited for enhancing high gray- level lesions. The power- law window display method, according to different values of gamma, could be similar to the linear, logarithmic or the exponential displays. The S and inverse S function displays had good enhancement effect for moderate gray-level lesions in the window. The pseudo color window display was capable of displaying more information of the lesions in a wider range of the window to assist in disease diagnosis. The non-linear methods provide more information of the input images and are thus good complimentary techniques for linear display methods.
The non-linear window display methods of DICOM images were studied and compared with the commonly used linear display methods. The authors analyzed the methods for inverse linear, exponential, logarithmic, power-law, S function,inverse S function and complex curve-based pseudo color window displays, and compared these methods with linear methods of window display. The experimental results demonstrated that linear window display had good performance in displaying the details of low- density lesions against a bright background with a good lesion enhancement effect, while non- linear window display suited for displaying high- density lesions in dark background. The logarithmic window display was capable of mapping low gray-level values in a narrow range in the window of an input image to a wider range of output values, and suited particularly for enhancing low gray- level lesions; the exponential window display method better suited for enhancing high gray- level lesions. The power- law window display method, according to different values of gamma, could be similar to the linear, logarithmic or the exponential displays. The S and inverse S function displays had good enhancement effect for moderate gray-level lesions in the window. The pseudo color window display was capable of displaying more information of the lesions in a wider range of the window to assist in disease diagnosis. The non-linear methods provide more information of the input images and are thus good complimentary techniques for linear display methods.
引文
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[15]DONG B,YANG J,HAO S,et al.Research on an improved medical image enhancement algorithm based on P-M model[J].Open Biomed Eng J,2015,9:209-213.
[16]GAN H S,SWEE T T,ABDUL KARIM A H,et al.Medical image visual appearance improvement using bihistogram Bezier curve contrast enhancement:data from the osteoarthritis initiative[J].Scientific World J,2014,2014:294104.
[17]GUO Q,SU X.The study of medical image enhancement based on curvelet[J].Technol Health Care,2015,23(Suppl 2):S319-323.
[18]QI G,FEI S,SHU-TING S.A case study on medical image enhancement based on II curvelet[J].Biomed Mater Eng,2015,26(3-4):129-138.
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[2]KALLIO-PULKKINEN S,HUUMONEN S,HAAPEA M,et al.Effect of display type,DICOM calibration and room illuminance in bitewing radiographs[J].Dentomaxillofac Radiol,2016,45(1):20150129.
[3]LOWE J,BRENNAN P,EVANOFF M,et al.Variations in performance of LCDs are still evident after DICOM gray-scale standard display calibration[J].AJR Am J Roentgenol,2010,195(1):181-187.
[4]PETERS K,RAMSBY G.Natural display mode for digital DICOMconformant diagnostic imaging[J].Acad Radiol,2002,9(9):1025-1035.
[5]SALAZAR A,AGUIRRE D,OCAMPO J,et al.DICOM gray-scale standard display function:clinical diagnostic accuracy of chest radiography in medical-grade gray-scale and consumer-grade color displays[J].AJR Am J Roentgenol,2014,202(6):1272-1280.
[6]THOMPSON S,WILLIS C,KRUGH K,et al.Implementing the DICOM grayscale standard display function for mixed hard-and soft-copy operations[J].J Digit Imaging,2002,15(Suppl 1):27-32.
[7]贾克斌.数字医学图像处理,存档及传输技术[M].北京:科学出版社,2006:20-25.JIA K B.Digital medical image processing,archiving and communication technique[M].Beijing:Science Press,2006:20-25.
[8]NERCESSIAN S,PANETTA K,AGAIAN S.Non-linear direct multi-scale image enhancement based on the luminance and contrast masking characteristics of the human visual system[J].IEEE Trans Image Process,2013,22(9):3549-3561.
[9]PANETTA K,WHARTON E,AGAIAN S.Human visual systembased image enhancement and logarithmic contrast measure[J].IEEE Trans Syst Man Cybern B Cybern,2008,38(1):174-188.
[10]WANG S,JIN K,LU H,et al.Human visual system-based fundus image quality assessment of portable fundus camera photographs[J].IEEE Trans Med Imaging,2016,35(4):1046-1055.
[11]WANG H,CUIJPERS R,LUO M,et al.Optimal illumination for local contrast enhancement based on the human visual system[J].J Biomed Opt,2015,20(1):015005.
[12]ZHANG Y,PHAM B,ECKSTEIN M.The effect of nonlinear human visual system components on performance of a channelized Hotelling observer in structured backgrounds[J].IEEE Trans Med Imaging,2006,25(10):1348-1362.
[13]冈萨雷斯,伍兹.数字图像处理[M].2版.北京:电子工业出版社,2003:75-85.GONZALEZ R C,WOODS R E.Digital image processing[M].2nd ed.Beijing:Publishing House of Electroincs Industry,2003:75-85.
[14]陈科,林江莉,李德玉,等.超声医学图像滤波和对比度增强新方法[J].生物医学工程学杂志,2007,24(2):434-438.CHEN K,LIN J L,LI D Y,et al.Filtering and contrast enhancement of medical ultrasonic image[J].Journal of Biomedical Engineering,2007,24(2):434-438.
[15]DONG B,YANG J,HAO S,et al.Research on an improved medical image enhancement algorithm based on P-M model[J].Open Biomed Eng J,2015,9:209-213.
[16]GAN H S,SWEE T T,ABDUL KARIM A H,et al.Medical image visual appearance improvement using bihistogram Bezier curve contrast enhancement:data from the osteoarthritis initiative[J].Scientific World J,2014,2014:294104.
[17]GUO Q,SU X.The study of medical image enhancement based on curvelet[J].Technol Health Care,2015,23(Suppl 2):S319-323.
[18]QI G,FEI S,SHU-TING S.A case study on medical image enhancement based on II curvelet[J].Biomed Mater Eng,2015,26(3-4):129-138.
[19]QING-HANG H,ZHEN-XI Z,ZHENG L,et al.The processing of the degraded Medical digital image's image enhancement[C].Conf Proc IEEE Eng Med Biol Soc,2005:3383-3385.
[20]WANG Q,CHEN L Y,SHEN D G.Fast histogram equalization for medical image enhancement[C]//2008 30th Annual International Conference of the IEEE Engineering in Medicine and Biology Society,Vols 1-8,2008:2217-2220.