医学电子内窥镜图像处理技术研究
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摘要
医学电子内窥镜是传统内窥镜与计算机、微电子等技术的不断发展和融合的产物，是当前应用非常广泛的医疗仪器。论文首先分析了医学电子内窥镜图像成像特点，在此基础上提出了医学电子内窥镜图像处理系统基本方案。并进一步研究了适合医学电子内窥镜的图像降噪模型和内窥镜图像去高光模型。最后搭建了部分医学电子内窥镜系统软件平台。
论文的主要研究工作和贡献如下：
1、在深入分析和研究医学内窥镜图像特点的基础上，总结了医学电子内窥镜图像处理和分析的基本技术，并给出一般处理框架。
2、提出了“区域自适应”的概念，并以“二进小波”为工具描述并证明了“区域自适应”在医学内窥镜图像去噪中应用的可行性和有效性，建立局部自适应二进小波降噪模型，简称ADWD(Adaptive Dyadic Wavelet Denoising)。实验结果及分析表明该方法对Gaussian噪声和Pepper噪声均有较高的信噪比，且对图像的细节有较好的保持能力。
3、在分析传统图像去高光算法缺点的前提下，提出了基于侧抑制模型的去除单张内窥镜图像高光算法，简称LIHR(Lateral Inhibition Highlight Removal)。与一般方法比较，该算法可以自动检测出图像中的高光区域，恢复出的图像比较清晰饱满，光滑连通性好，基本没有纹理变化现象。实验结果表明，该算法能够有效的去除图像中的高光区域，适用于实时内窥镜图像处理。
4、用Delphi搭建了部分医学电子内窥镜系统软件平台。主要包括病例管理模块和图像处理模块两大部分。
With the medical electronic endoscope having been made full use of in the clinic operation, more and more attention is being paid to the medical endoscope image processing technology. In allusion to the characteristic of medical endoscope image, we survey the recent techniques of endoscope image processing. The merits and drawbacks of typical methods were discussed too. Finally, some promising research directions and hotspots are suggested.
For wavelet transform, wavelet coefficients domain processing and threshold estimation are important problems. Based on these ideas, a probability model based on the adaptive dyadic wavelet denoising (ADWD) is proposed. Using the connection of image information, noise information, and wavelet coefficients,. ADWD identifies noise pelses by a local adaptive model and avoids the difficulty of directly ensuring noise threshold. Experimental results and analysis are given to demonstrate the validity of the proposed model for Gaussian noise and Pepper noise, and the ability of keeping images details.
Specular detection and removal are always hot problems in endoscope image processing. Advanced results have a great impact on endoscope operation. In this paper, a specular detection and removal algorithm is proposed. First, it established the lateral inhibition model and picked up the variance information in the image, and then reversely used the lateral inhibition model in order to detect specular. Second, the algorithm accounted the range of specular and removed specular. Different from traditional ways, the algorithm has the ability to retain the detail of image. Experimental results show that this method can remove specular effectively and adapt to the timely endoscope image processing.
Finally, a software model for medical electronic endoscope is written, which includes image process software and file management software.
论文的主要研究工作和贡献如下：
1、在深入分析和研究医学内窥镜图像特点的基础上，总结了医学电子内窥镜图像处理和分析的基本技术，并给出一般处理框架。
2、提出了“区域自适应”的概念，并以“二进小波”为工具描述并证明了“区域自适应”在医学内窥镜图像去噪中应用的可行性和有效性，建立局部自适应二进小波降噪模型，简称ADWD(Adaptive Dyadic Wavelet Denoising)。实验结果及分析表明该方法对Gaussian噪声和Pepper噪声均有较高的信噪比，且对图像的细节有较好的保持能力。
3、在分析传统图像去高光算法缺点的前提下，提出了基于侧抑制模型的去除单张内窥镜图像高光算法，简称LIHR(Lateral Inhibition Highlight Removal)。与一般方法比较，该算法可以自动检测出图像中的高光区域，恢复出的图像比较清晰饱满，光滑连通性好，基本没有纹理变化现象。实验结果表明，该算法能够有效的去除图像中的高光区域，适用于实时内窥镜图像处理。
4、用Delphi搭建了部分医学电子内窥镜系统软件平台。主要包括病例管理模块和图像处理模块两大部分。
With the medical electronic endoscope having been made full use of in the clinic operation, more and more attention is being paid to the medical endoscope image processing technology. In allusion to the characteristic of medical endoscope image, we survey the recent techniques of endoscope image processing. The merits and drawbacks of typical methods were discussed too. Finally, some promising research directions and hotspots are suggested.
For wavelet transform, wavelet coefficients domain processing and threshold estimation are important problems. Based on these ideas, a probability model based on the adaptive dyadic wavelet denoising (ADWD) is proposed. Using the connection of image information, noise information, and wavelet coefficients,. ADWD identifies noise pelses by a local adaptive model and avoids the difficulty of directly ensuring noise threshold. Experimental results and analysis are given to demonstrate the validity of the proposed model for Gaussian noise and Pepper noise, and the ability of keeping images details.
Specular detection and removal are always hot problems in endoscope image processing. Advanced results have a great impact on endoscope operation. In this paper, a specular detection and removal algorithm is proposed. First, it established the lateral inhibition model and picked up the variance information in the image, and then reversely used the lateral inhibition model in order to detect specular. Second, the algorithm accounted the range of specular and removed specular. Different from traditional ways, the algorithm has the ability to retain the detail of image. Experimental results show that this method can remove specular effectively and adapt to the timely endoscope image processing.
Finally, a software model for medical electronic endoscope is written, which includes image process software and file management software.
引文
[1] Hideaki Haneishi,Yutaka Yagihashi ,Yoichi Miyake. A new method for distortion correction of electronic endoscope images[J]. IEEE Trans on Medical Imaging, 1995,14(3):548～555.
[2] K Vijayan Asari. A new approach for nonlinear distortion correction in endoscopic images based on least squares estimation [J]. IEEE Trans on Medical Imaging, 1999,18 (4): 345～354.
[3] James P Helferty, Chap Zhang, Geoffrey McLennan. Videoendoscopic distortion correction and its application to virtual guidance of endoscopy[J]. IEEE Trans on Medical Imaging, 2001, 20(7):605～617.
[4] S.J.Sangwine and R.E.N. Home. Eds.The Color Image Processing Handbook. London, U.K: Chapman& Hall, 1998.
[5] Z.M.Wojcik. Rough sets for intelligent image filtering [A]. In:RSKD-93 Rough Sets and Knowledge Discovery[C]. 1993.339～410.
[6] 杨延竹．基于模糊神经网络的图像滤波方法[J]．控制与决策，2004，19(1)：69～72．
[7] Angulo. J. Morphological coding of color images by vector connected filters[J]. Signal Processing and Its Applications, 2003,1(1):69～72.
[8] Wolff LB. Using polarization to separate reflection components. In: Proc. of the IEEE Computer Vision and Pattern Recognition. 1989. 363～369.
[9] Sato Y, Ikeuchi K. Temporal-Color space analysis of reflection. Journal of the Optical Society of America, 1994,11(11).
[10] Nayar SK, Fang X, Boult TE. Removal of specularities using color and polarization. In: Proc. of the IEEE Computer Vision and Pattern Recognition. 1993. 583～590.
[11] Klinker GJ, Shafer SA, Kanade T. The measurement of highlights in color images [J]. International Journal of Computer Vision, 1990,2(1):7～32.
[12] 谭平，杨杰．用于去除单张图像高光的光照约束[J]．软件学报，2004，15(1)：33～40．
[13] Lim H, Tan K C, Tan B T G. Edge Errors in Inverse and Wiener Filter Restorations of Motion-blurred Images and R Their Windowing Treatment. CVGIP, 1991, 53: 186～195.
[14] 王晓红，陈秀万．一种有效的运动模糊图像恢复算法[J]．计算机工程，2003，29(17)：13～14．
[15] Seungjoon Yang. Contrast enhancement using histogram equalization with bin underflow and bin overflow [J]. Image Processing, 2003,1(1):I-881～4.
[16] 张长江，付梦印．基于退火算法的红外图像自适应对比度增强[J]．中国图像图形学报，2004，9(4)：391～396．
[17] Brown T J. An adaptive strategy for wavelet based image enhancement [A]. Proceedings if Irish Machine Vision and Image Processing Conference[C], Belfast, Northern Ireland, 2000:67～81.
[18] R. N. Strickland, C. S. Kim, and W. f. McDonnel. Digital color image enhancement based on the saturation compoment[J], 1987,26(7):609～616.
[19] Kaiqi Huang. Color image enhancement and evaluation algorithm based on human visual system. Acoustics, Speech, and Signal Processing, Proceedings [J]. IEEE International Conference on, 2004,3(17): Ⅲ-721～4 vol.3.
[20] Ohlander r, Price K, Reddy D R. Picture segmentation using a recursive region splitting method[J]. Computer Graphics and Image Processing,1978,8(3):313～333.
[21] Guo G D,Yu S,Ma S D. Unsupervised segmentation of color images[A]. Proceeding of 1998 IEEE International Conference on Image Processing[C]. Chicago, IL, USA, 1998:199～302.
[22] Pal NR, Pal SK. A review on image segmentation technique[J].Pattem Recognition, 1993，26(29):1277～1294.
[23] Cheng H D, Li J. Fuzzy homogeneity and scale approach to color image segmentation[J]. Pattem recognition,2003,36(7):1545～1562.
[24] Saha P K, Udupa J K, Odhner D. Scale-based fuzzy connected image segmentation Theory, algorithms, and validation [J]. Computer Vision and Image Understanding, 2000,77(3):145～174.
[25] 田捷，韩博闻．模糊C-均值聚类法在医学图像分析中的应用[J]．软件学报，2001，12(11)：1623～1629．
[26] Lim Y W, Lee S U. On the color image segmentation algorithm based on the thresholding and the fuzzy c-means techniques [J]. Patten Recognition, 990, 23(9): 935～952.
[27] Ozdemir D, Akarun L. Fuzzy algorithms for combined quantization and dithering[J]. IEEE Trans. on Image Processing, 2001,10(6): 923～931.
[28] Daubechies Ⅰ. The Wavelet Transform, Tim-frequency Localization and Signal Analysis. IEEE Trans. on IT, 1990,36(5):961～1005.
[29] Averbuch A., Lazar D. and Israeli M. Image Compressing Using Wavelet Transform and Multiresolution Decomposition. IEEE Trans. on Image Processing, 1996, 5(1): 4-15.
[30] Hartung F. and Kutter M. Multimedia: Watermarking Techniques. Proceedings of the IEEE, 1999,87(7): 1079-1107.
[31] Goswami J.C., Chan A.K. and Chui C.K. on Solving First-kind Integral Equations Using Wavelets on A Bounded Interval. IEEE Trans, on AP, 1995,43(6)617-622.
[32] Mallet S. and Zhong S. Charqacterization of Signals from Multiscale edges. IEEE Trans, on PAMI, 1992,14(7):710-732.
[33] Mallet S. and Hwang W.L. Singularity Detection and Processing with Wavelets. IEEE Trans, on IT, 1992,38(2):617-643.
[34] Downie T.R. and Silverman B.W. The discrete Multiple Wavelet Transform and Thresholding Methods. IEEE Trans, on Signal Processing 1998,46(9):2558-2565.
[35] Daubechies I., Grossman A. and Meyer Y. Painless Nonorthogonal Expansions. Journal of Math. Phys., 1986,27:1271-1283.
[36] Unser M., Therenaz P. and Aldroubi A. Shift-orthogonal Wavelet bases. IEEE Trans. on SP, 1998,46(7): 1827-1836.
[37] Aldroubi A. Abry P. and Unser M. Construction of Biorthogonal Wavelets Starting from Any Two Given Multiresolution Analyzes. IEEE Trans, on SP., 1998, 46(3): 1130-1133.
[38] Donoho D L, Johnstone I M, Ideal spatial adaptation via wavelet shrinkage[J]. Biometrika, 1994,81:425-455
[39] Krim H, Pesquet J C.On the statistics of best bases criteria [A]. Antoniadis A, Oppendheim G edis. Wavelets in statistics of Lecture Notes in statistics[C].New York: Spring-Verlag, 1995:193 -207
[40] Xu Yansun, Weaver J B,Healy M Jetal. Wavelet transform domain filters: A spatially selective noise filtration technique[J].IEEE Trans. Image Processing, 1994, 3(6):743-758
[41] Chang S G, Yu Bin, Vetterli M. Adaptive wavelet thresholding for image denoising and compression[J].IEEE Trans.Image Processing, 2000,9(9): 1532-1546
[42] Moulin P,Liu Juan. Analysis of multiresolution image denoising schemes using generalized-Gaussian and complexity priors[J]. IEEE Trans. Information Theory, 1999,45:909-919
[43] Han K J,Tewfik A H.Hybrid wavelet transform filter for image recovery[A]. Proceedings of the International Conference on Image Processing[C].Chicago USA,1998:540-544.
[44] Shark L K, Yu C.Denoising by optimal fuzzy thresholding in wavelet domain[J]. Electronics Letters,2000,36(6):581～582
[45] Malfait M, Roose D. Wavelet-based image denoising using a Markov random field a priori model[J].IEEE Trans. Image Processing, 1997,6(4):549～565
[46] Liu Juan, Moulin P. Image denoising based on scale-space mixture modeling of wavelet coefficients[A]. Proceedings of IEEE International Conference on Image Processing[C]. Kobe Japan, 1999:386～390
[47] Badulescu P, Zaciu R. Removal of mixed-noise using order statistic filter and wavelet domain Wiener filter[A].Proceedings of the International Semiconductor Conference[C].Sinaia Romania,1999:301～304
[48] Krim H, Schick I C.Minimax description length for signal denoising and optimized representation[J]. IEEE Trans. Information Theory, 1999,45(3):898～908
[49] S.Saitoh. Theory of Reproducing Kernels and Its Applications[M]. Longman Scientific&Technical Press, 1988,1～15.
[50] 费佩燕，郭宝龙等．基于二进小波变换的图像去噪技术研究[J]．西安电子科技大学，2003，30(4)：492～496．
[51] S Chang, B Yu, Vetterli M. Adaptive Wavelet Thresholding for Image Denoising and Compression[J]. IEEE Transactions on Image Processing, 2000,9(9): 1532～1546.
[52] Donoho D L. Johnstone I M. Ideal Spatial Adaptation by Wavelet Shrinkage[J]. Biometrika, 1994,81(2):425～455
[53] Donoho D L. De-noising Via Soft-thresholding[J].IEEE Trans on Info Theory, 1992,41(3): 613～627.
[54] Donoho D L, Johnstone I M. Adapting to Unknown Smoothness Via Wavelet Shrinkage[J]. J.ASA, 1995,90:1200～1223.
[55] Mallat.S and Hwang Wen Liang. Singularity Detection and Processing with Wavelets[J].IEEE Trans on Information Theory, 1992,38(2):617～643.
[56] 顾凡及，江云九．侧抑制网络中的信息处理[M]：科学出版社，1983．
[57] 郭宝龙，郭雷．扩散和集中神经网络区分图形与背景[J]．1994，39(10)：1805～1084．
[58] 陈卉，欧阳楷．用于图像增强的侧抑制网络模型的仿真比较[J]．2003，5(1)：100～103．
[2] K Vijayan Asari. A new approach for nonlinear distortion correction in endoscopic images based on least squares estimation [J]. IEEE Trans on Medical Imaging, 1999,18 (4): 345～354.
[3] James P Helferty, Chap Zhang, Geoffrey McLennan. Videoendoscopic distortion correction and its application to virtual guidance of endoscopy[J]. IEEE Trans on Medical Imaging, 2001, 20(7):605～617.
[4] S.J.Sangwine and R.E.N. Home. Eds.The Color Image Processing Handbook. London, U.K: Chapman& Hall, 1998.
[5] Z.M.Wojcik. Rough sets for intelligent image filtering [A]. In:RSKD-93 Rough Sets and Knowledge Discovery[C]. 1993.339～410.
[6] 杨延竹．基于模糊神经网络的图像滤波方法[J]．控制与决策，2004，19(1)：69～72．
[7] Angulo. J. Morphological coding of color images by vector connected filters[J]. Signal Processing and Its Applications, 2003,1(1):69～72.
[8] Wolff LB. Using polarization to separate reflection components. In: Proc. of the IEEE Computer Vision and Pattern Recognition. 1989. 363～369.
[9] Sato Y, Ikeuchi K. Temporal-Color space analysis of reflection. Journal of the Optical Society of America, 1994,11(11).
[10] Nayar SK, Fang X, Boult TE. Removal of specularities using color and polarization. In: Proc. of the IEEE Computer Vision and Pattern Recognition. 1993. 583～590.
[11] Klinker GJ, Shafer SA, Kanade T. The measurement of highlights in color images [J]. International Journal of Computer Vision, 1990,2(1):7～32.
[12] 谭平，杨杰．用于去除单张图像高光的光照约束[J]．软件学报，2004，15(1)：33～40．
[13] Lim H, Tan K C, Tan B T G. Edge Errors in Inverse and Wiener Filter Restorations of Motion-blurred Images and R Their Windowing Treatment. CVGIP, 1991, 53: 186～195.
[14] 王晓红，陈秀万．一种有效的运动模糊图像恢复算法[J]．计算机工程，2003，29(17)：13～14．
[15] Seungjoon Yang. Contrast enhancement using histogram equalization with bin underflow and bin overflow [J]. Image Processing, 2003,1(1):I-881～4.
[16] 张长江，付梦印．基于退火算法的红外图像自适应对比度增强[J]．中国图像图形学报，2004，9(4)：391～396．
[17] Brown T J. An adaptive strategy for wavelet based image enhancement [A]. Proceedings if Irish Machine Vision and Image Processing Conference[C], Belfast, Northern Ireland, 2000:67～81.
[18] R. N. Strickland, C. S. Kim, and W. f. McDonnel. Digital color image enhancement based on the saturation compoment[J], 1987,26(7):609～616.
[19] Kaiqi Huang. Color image enhancement and evaluation algorithm based on human visual system. Acoustics, Speech, and Signal Processing, Proceedings [J]. IEEE International Conference on, 2004,3(17): Ⅲ-721～4 vol.3.
[20] Ohlander r, Price K, Reddy D R. Picture segmentation using a recursive region splitting method[J]. Computer Graphics and Image Processing,1978,8(3):313～333.
[21] Guo G D,Yu S,Ma S D. Unsupervised segmentation of color images[A]. Proceeding of 1998 IEEE International Conference on Image Processing[C]. Chicago, IL, USA, 1998:199～302.
[22] Pal NR, Pal SK. A review on image segmentation technique[J].Pattem Recognition, 1993，26(29):1277～1294.
[23] Cheng H D, Li J. Fuzzy homogeneity and scale approach to color image segmentation[J]. Pattem recognition,2003,36(7):1545～1562.
[24] Saha P K, Udupa J K, Odhner D. Scale-based fuzzy connected image segmentation Theory, algorithms, and validation [J]. Computer Vision and Image Understanding, 2000,77(3):145～174.
[25] 田捷，韩博闻．模糊C-均值聚类法在医学图像分析中的应用[J]．软件学报，2001，12(11)：1623～1629．
[26] Lim Y W, Lee S U. On the color image segmentation algorithm based on the thresholding and the fuzzy c-means techniques [J]. Patten Recognition, 990, 23(9): 935～952.
[27] Ozdemir D, Akarun L. Fuzzy algorithms for combined quantization and dithering[J]. IEEE Trans. on Image Processing, 2001,10(6): 923～931.
[28] Daubechies Ⅰ. The Wavelet Transform, Tim-frequency Localization and Signal Analysis. IEEE Trans. on IT, 1990,36(5):961～1005.
[29] Averbuch A., Lazar D. and Israeli M. Image Compressing Using Wavelet Transform and Multiresolution Decomposition. IEEE Trans. on Image Processing, 1996, 5(1): 4-15.
[30] Hartung F. and Kutter M. Multimedia: Watermarking Techniques. Proceedings of the IEEE, 1999,87(7): 1079-1107.
[31] Goswami J.C., Chan A.K. and Chui C.K. on Solving First-kind Integral Equations Using Wavelets on A Bounded Interval. IEEE Trans, on AP, 1995,43(6)617-622.
[32] Mallet S. and Zhong S. Charqacterization of Signals from Multiscale edges. IEEE Trans, on PAMI, 1992,14(7):710-732.
[33] Mallet S. and Hwang W.L. Singularity Detection and Processing with Wavelets. IEEE Trans, on IT, 1992,38(2):617-643.
[34] Downie T.R. and Silverman B.W. The discrete Multiple Wavelet Transform and Thresholding Methods. IEEE Trans, on Signal Processing 1998,46(9):2558-2565.
[35] Daubechies I., Grossman A. and Meyer Y. Painless Nonorthogonal Expansions. Journal of Math. Phys., 1986,27:1271-1283.
[36] Unser M., Therenaz P. and Aldroubi A. Shift-orthogonal Wavelet bases. IEEE Trans. on SP, 1998,46(7): 1827-1836.
[37] Aldroubi A. Abry P. and Unser M. Construction of Biorthogonal Wavelets Starting from Any Two Given Multiresolution Analyzes. IEEE Trans, on SP., 1998, 46(3): 1130-1133.
[38] Donoho D L, Johnstone I M, Ideal spatial adaptation via wavelet shrinkage[J]. Biometrika, 1994,81:425-455
[39] Krim H, Pesquet J C.On the statistics of best bases criteria [A]. Antoniadis A, Oppendheim G edis. Wavelets in statistics of Lecture Notes in statistics[C].New York: Spring-Verlag, 1995:193 -207
[40] Xu Yansun, Weaver J B,Healy M Jetal. Wavelet transform domain filters: A spatially selective noise filtration technique[J].IEEE Trans. Image Processing, 1994, 3(6):743-758
[41] Chang S G, Yu Bin, Vetterli M. Adaptive wavelet thresholding for image denoising and compression[J].IEEE Trans.Image Processing, 2000,9(9): 1532-1546
[42] Moulin P,Liu Juan. Analysis of multiresolution image denoising schemes using generalized-Gaussian and complexity priors[J]. IEEE Trans. Information Theory, 1999,45:909-919
[43] Han K J,Tewfik A H.Hybrid wavelet transform filter for image recovery[A]. Proceedings of the International Conference on Image Processing[C].Chicago USA,1998:540-544.
[44] Shark L K, Yu C.Denoising by optimal fuzzy thresholding in wavelet domain[J]. Electronics Letters,2000,36(6):581～582
[45] Malfait M, Roose D. Wavelet-based image denoising using a Markov random field a priori model[J].IEEE Trans. Image Processing, 1997,6(4):549～565
[46] Liu Juan, Moulin P. Image denoising based on scale-space mixture modeling of wavelet coefficients[A]. Proceedings of IEEE International Conference on Image Processing[C]. Kobe Japan, 1999:386～390
[47] Badulescu P, Zaciu R. Removal of mixed-noise using order statistic filter and wavelet domain Wiener filter[A].Proceedings of the International Semiconductor Conference[C].Sinaia Romania,1999:301～304
[48] Krim H, Schick I C.Minimax description length for signal denoising and optimized representation[J]. IEEE Trans. Information Theory, 1999,45(3):898～908
[49] S.Saitoh. Theory of Reproducing Kernels and Its Applications[M]. Longman Scientific&Technical Press, 1988,1～15.
[50] 费佩燕，郭宝龙等．基于二进小波变换的图像去噪技术研究[J]．西安电子科技大学，2003，30(4)：492～496．
[51] S Chang, B Yu, Vetterli M. Adaptive Wavelet Thresholding for Image Denoising and Compression[J]. IEEE Transactions on Image Processing, 2000,9(9): 1532～1546.
[52] Donoho D L. Johnstone I M. Ideal Spatial Adaptation by Wavelet Shrinkage[J]. Biometrika, 1994,81(2):425～455
[53] Donoho D L. De-noising Via Soft-thresholding[J].IEEE Trans on Info Theory, 1992,41(3): 613～627.
[54] Donoho D L, Johnstone I M. Adapting to Unknown Smoothness Via Wavelet Shrinkage[J]. J.ASA, 1995,90:1200～1223.
[55] Mallat.S and Hwang Wen Liang. Singularity Detection and Processing with Wavelets[J].IEEE Trans on Information Theory, 1992,38(2):617～643.
[56] 顾凡及，江云九．侧抑制网络中的信息处理[M]：科学出版社，1983．
[57] 郭宝龙，郭雷．扩散和集中神经网络区分图形与背景[J]．1994，39(10)：1805～1084．
[58] 陈卉，欧阳楷．用于图像增强的侧抑制网络模型的仿真比较[J]．2003，5(1)：100～103．
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