基因芯片扫描仪软件设计
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摘要
基因芯片图像的采集和芯片分析是芯片技术的关键环节，芯片图像获取的准确性和分析的可靠性直接影响芯片的应用。本文首先完成了图像采集系统的自动控制，并且针对采集过程的特点准确的恢复出扫描芯片的图像。然后从图像分析和数据分析两方面研究了影响芯片分析的因素，实现了对杂交荧光图像的信号提取和基本的生物信息分析。
在图像采集系统中，首先完成了扫描仪工作的自动控制，扫描范围可选。为了解决图像扫描过程中出现的非线性畸变，首先运用仿射变换校正图像象素位置发生的非线性移位，然后采用线性插值算法进行图像恢复。
针对杂交图像中污点、噪声形状、灰度变化比较大的特点，本文引入模糊形态学的方法成功抑制了污点、噪声等杂散信号的干扰。同时该方法对于荧光猝灭所造成的信号点中空现象具有较好的弥补作用。
在网格定位中，本文针对信号点不规则的芯片图像提出了基于图像分割的自动定位方法——阈值分割，该方法简单快速，但对于光密度分布不均匀的信号点，可能会去掉部分微弱信号，降低了计算的准确性。对于信号点规则的芯片图像，本文采用人机交互的半自动定位方法，该方法运算速度快，并且效果比较理想。
在信号强度计算中，本文研究了背景、计算方法、归一化三个问题，以及背景滤除、归一化的解决方案，并针对不同的定位方法使用不同的计算方法以确保信号点荧光强度提取的准确性。
本文针对表达型芯片进行了数据分析，研究了表达型芯片数据结果的可视化分析，即通过阈值图、柱状图、散列图直观显示表达结果。
最后本文实现了基因芯片共焦扫描仪控制系统ArrayScan 1.0和基因芯片分析系统AnaArray 1.0。
Gnenchip image acquisition and chip analysis are the key parts of techniques for genechip and play two important roles in the application of genechip. In this paper, first we accomplish how to control the image acquiring system, and recovery the chip image accurately, aiming for the feature of acquiring course. Then we study the methods of image analysis and data analysis in order to extract the hybridizing intensity and retrieve the biological information.
In image acquiring system, we can control the scanner automatically, scanning range can be specified by user. In order to resolve the nonlinear distortion occurring in the course of image acquiring, first we apply affme transformation to adjust image, then recovery image by means of linear interleration algorithms.
Some factors such as noise from irregular spots, dust on the slide, and nonspecific hybridization make it difficult to extract data accurately. We use fuzzy mathematical morphology to remove noise and dust which can lower noise successfully and also make up a loss of intensity caused by fluorescence blench.
To acquire the target region, we purpose automatic gridding method based on image segmentation for image with irregular spots, it's segmentation through threshold. This method is presented simply and fast. However it's not suitable for spots with asymmetrical intensity. As for images with regular spots , a semi-automatic gridding method is used to improve processing speed, and it can achieve good effect.
In the extraction of fluorescent intensity, we discuss background removing, the methods of computing and normalization. According to different gridding methods, diverse computing algorithms are adopted to ensure the accuracy of image analysis.
We study the data analysis of expression genechip, put emphasis on the visualization of result.
Finally we develop two software, one is ArrayScan 1.0, the other is AnaArray 1.0.
在图像采集系统中，首先完成了扫描仪工作的自动控制，扫描范围可选。为了解决图像扫描过程中出现的非线性畸变，首先运用仿射变换校正图像象素位置发生的非线性移位，然后采用线性插值算法进行图像恢复。
针对杂交图像中污点、噪声形状、灰度变化比较大的特点，本文引入模糊形态学的方法成功抑制了污点、噪声等杂散信号的干扰。同时该方法对于荧光猝灭所造成的信号点中空现象具有较好的弥补作用。
在网格定位中，本文针对信号点不规则的芯片图像提出了基于图像分割的自动定位方法——阈值分割，该方法简单快速，但对于光密度分布不均匀的信号点，可能会去掉部分微弱信号，降低了计算的准确性。对于信号点规则的芯片图像，本文采用人机交互的半自动定位方法，该方法运算速度快，并且效果比较理想。
在信号强度计算中，本文研究了背景、计算方法、归一化三个问题，以及背景滤除、归一化的解决方案，并针对不同的定位方法使用不同的计算方法以确保信号点荧光强度提取的准确性。
本文针对表达型芯片进行了数据分析，研究了表达型芯片数据结果的可视化分析，即通过阈值图、柱状图、散列图直观显示表达结果。
最后本文实现了基因芯片共焦扫描仪控制系统ArrayScan 1.0和基因芯片分析系统AnaArray 1.0。
Gnenchip image acquisition and chip analysis are the key parts of techniques for genechip and play two important roles in the application of genechip. In this paper, first we accomplish how to control the image acquiring system, and recovery the chip image accurately, aiming for the feature of acquiring course. Then we study the methods of image analysis and data analysis in order to extract the hybridizing intensity and retrieve the biological information.
In image acquiring system, we can control the scanner automatically, scanning range can be specified by user. In order to resolve the nonlinear distortion occurring in the course of image acquiring, first we apply affme transformation to adjust image, then recovery image by means of linear interleration algorithms.
Some factors such as noise from irregular spots, dust on the slide, and nonspecific hybridization make it difficult to extract data accurately. We use fuzzy mathematical morphology to remove noise and dust which can lower noise successfully and also make up a loss of intensity caused by fluorescence blench.
To acquire the target region, we purpose automatic gridding method based on image segmentation for image with irregular spots, it's segmentation through threshold. This method is presented simply and fast. However it's not suitable for spots with asymmetrical intensity. As for images with regular spots , a semi-automatic gridding method is used to improve processing speed, and it can achieve good effect.
In the extraction of fluorescent intensity, we discuss background removing, the methods of computing and normalization. According to different gridding methods, diverse computing algorithms are adopted to ensure the accuracy of image analysis.
We study the data analysis of expression genechip, put emphasis on the visualization of result.
Finally we develop two software, one is ArrayScan 1.0, the other is AnaArray 1.0.
引文
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6. Xiaoli zhang, Jianming Xie, Xiao Sun, Hong Wang, Zuhong Lu, Data management and analysis for gene chip, IEEE-EMBS Asia-Pacific Conference on Biomedical Engineering, 2000.9
7. Xiaoli zhang, Zuhong lu, Image processing of gene chip using mathematical morphological operations based on fuzzy set, IEEE-EMBS Asia-Pacific Conference on Biomedical Engineering, 2000.9
8. Mendelsohn M, Mayall B, Prewitt J, Digital transformation and Computer Analysis of Microscope Images, Advances in Optical and Electron Microscopy, 1968
9. Lee J S, Digital image smoothing and the sigma filter, CGIP, Vol.24, 1984: 255-269
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11. Nevatia R Locating object boundaries in textured environments, IEEE Trans, Comp,C-25, 1976: 1170-1180
12. M. Laster J, Williams H A, Two Graph searching techniques for boundary finding in white blood cell image, Computer in Biology and Medicine, 8, 1978: 293-308
13. Zucker S, Region growing: childhood and adolescence, Computer Graphics and Image Processing, 5, 1976:382-399
14. Marr D, Hildreth E, Theory of the edge detection, Proc, R, Soc, London, Ser, B, 207, 1980: 187-217
15. Rosenfield A, Connectivity in digital picture, Jouranl of the ACM, 17, 1979: 146-160
16. Nielson G M, Scattered data modeling, IEEE Computer Graphics & Application, 1993, 1:60-70
17. Porter T, Compositing digital images, Computer Graphics, 18(3)1984: 253-259
18. J.Koplowitz, S Plante, Coner detection for chain coded curves, Pattern recognition, Vol. 28, No. 6, 1995: 843-852
19. P.F. Zhu, P.M chirlian, On Critical Point Detection of Digital Shapes, IEEE Trans, Pattern Analysis and Machine Intelligence, Vol. 17, No.8, 1995: 737-748
20. J.M Hu, H Yan, Polygonal Approximation of Digital Curves Based on The Principles of Perceptual Organization, Pattern Recognition, Vol.30, No.5, 1997:845-851
21. Kitchen L, A Rosenfield, Gray level Comer detection, Pattern Recognition, Lett, 1, 1982: 95-102
22. Heikki kalbiainen, Petri Hirvonen, An Extension to the Randomized Hough Transform Exploitiong Connectivity, Pattern Recognition Letters, Vol. 19, No. 1, 1997: 77-85
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24. Ohya J, Shio A, Akamatsu S, Recognition characters in scene images, IEEE Trans-PAMI, Vol. 16, No.2, 1994:214-220
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27. Fodor S, Maddively parallel genomics, Science, 1997, V277: 393-394
28. Schena M, Shalon D, Davis R W, Brown P O, Quantitative monitoring of gene expression patterns with a complementary DNA microarray, Science, 1995, V270: 467-470
29. De Risi J, Penland L, Brown P O, Bittner M L, Use of a cDNA microarray to analyse gene expression patterns in human cancer, Nature Genetics, 1996, V14: 457-460
30. Wallraff J, Labadie, DNA Sequencing on a chip, Chemtech, February, 1997
31. Joseph G, Hoheisel, Oligomer-Chip Technology, 1997, Novermber, Tibtech, V15
32. http://www.affmetrix.com
33. http://www.healthtech.com
34. http://www.biodiscovery.com
35. Chee M, Yang R, Hubbel, Accessing genetic information with high-density DNA arrays, Science, 1996, 274: 610-613
36. Pennis E, Chipping away at the human genome, Science, 1996, V272:1737
37. Nuwaysir EF, Bittner M, Trent J, Microarrays and toxicology: the advent of
toxicogenomics, Mol Carcinog, 1999, V24:153-159
38. Olda Ermolaeva, Mohit Rastogi, Data managament and analysis for gene expression arrays, Nature genetics, 1998, V20: 19-23
39. David J, Michael Bittner, Yidong Chen, Expression profiling using cDNA microarrays, Nature genetics supplement, 1999, V21:10-14
40. Pease A N, Solas D, Sullivan E J, Light generated oligonucleotide arrays for rapid DNA sequence analysis, Proc Natl Acad Sci, 1994, V91:5022-5026
41. Haralick R M, Sternberg S R, Zhuang X, Image analysis using mathematical morphology, IEEE Trans on PAMI, 1987, V9(4): 532-550
42. Canny J, Finding Edges and Lines in images, MIT June 1983
43. Canny J, A Computational Approach to Edge Detection, IEEE Trans on PAMI, 1986,V8(6): 678-698
44. Reddis S S, Rudin S F, An optimal multiple threshold scheme for image segmentation, IEEE Trans SMC, 1984, V14(4):661-665
45. Data management analysis for genechip, NHGRI
46. Trevor Hastie, Uwe Scherf, Gene Shaving-a new class of clustering Method of expression analysis
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49．何斌，马天予，“Visual C++数字图像处理”，人民邮电出版社，2001
50．夏德深，傅德胜，“现代数字图像处理技术与应用”，东南大学出版社，1997
51．章敏晋，“图像处理和分析”，清华大学出版社，1999
52．吴健康，“图像处理和分析”，人民邮电出版社，1990
53．王新成，“高级图形处理技术”，中国科学技术出版社，2000
54．李楠，“激光扫描共聚焦显微术”，人民军医出版社，1997
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2. Young J Z, Roberts F, A flying-spot microscope, Nature, 1951, 167:231-232
3. Streibl N, Depth transfer by an imaging system, OPTICA ACTA, Vol.31 No.11,1984: 1223-1241
4. Stipp D, Gene Chip Breakthrough, Fortune, 1997, March 31: 44-53
5. Ramsay R, Nature Biotechnology, 1998, V1, N6: 40-44
6. Xiaoli zhang, Jianming Xie, Xiao Sun, Hong Wang, Zuhong Lu, Data management and analysis for gene chip, IEEE-EMBS Asia-Pacific Conference on Biomedical Engineering, 2000.9
7. Xiaoli zhang, Zuhong lu, Image processing of gene chip using mathematical morphological operations based on fuzzy set, IEEE-EMBS Asia-Pacific Conference on Biomedical Engineering, 2000.9
8. Mendelsohn M, Mayall B, Prewitt J, Digital transformation and Computer Analysis of Microscope Images, Advances in Optical and Electron Microscopy, 1968
9. Lee J S, Digital image smoothing and the sigma filter, CGIP, Vol.24, 1984: 255-269
10. Zhang D Z, Li C C, An image filtering algorithm based on selective averaging by hypothsis testing, Proce, 7th, IJCPR, Montreal, Canada: 159-162
11. Nevatia R Locating object boundaries in textured environments, IEEE Trans, Comp,C-25, 1976: 1170-1180
12. M. Laster J, Williams H A, Two Graph searching techniques for boundary finding in white blood cell image, Computer in Biology and Medicine, 8, 1978: 293-308
13. Zucker S, Region growing: childhood and adolescence, Computer Graphics and Image Processing, 5, 1976:382-399
14. Marr D, Hildreth E, Theory of the edge detection, Proc, R, Soc, London, Ser, B, 207, 1980: 187-217
15. Rosenfield A, Connectivity in digital picture, Jouranl of the ACM, 17, 1979: 146-160
16. Nielson G M, Scattered data modeling, IEEE Computer Graphics & Application, 1993, 1:60-70
17. Porter T, Compositing digital images, Computer Graphics, 18(3)1984: 253-259
18. J.Koplowitz, S Plante, Coner detection for chain coded curves, Pattern recognition, Vol. 28, No. 6, 1995: 843-852
19. P.F. Zhu, P.M chirlian, On Critical Point Detection of Digital Shapes, IEEE Trans, Pattern Analysis and Machine Intelligence, Vol. 17, No.8, 1995: 737-748
20. J.M Hu, H Yan, Polygonal Approximation of Digital Curves Based on The Principles of Perceptual Organization, Pattern Recognition, Vol.30, No.5, 1997:845-851
21. Kitchen L, A Rosenfield, Gray level Comer detection, Pattern Recognition, Lett, 1, 1982: 95-102
22. Heikki kalbiainen, Petri Hirvonen, An Extension to the Randomized Hough Transform Exploitiong Connectivity, Pattern Recognition Letters, Vol. 19, No. 1, 1997: 77-85
23. W.Eric Grimson, Daniel P, Huttenlcher, On the sensiticity of hough tranform for object recognition, IEEE Trans-PAMI, Vol. 12, No.3, 1990:255-257
24. Ohya J, Shio A, Akamatsu S, Recognition characters in scene images, IEEE Trans-PAMI, Vol. 16, No.2, 1994:214-220
25. Blattner F R, Rlunkett G, Bloch C A, The complete genmone sequence of Escherichia coli K-12, Science 1997, V277: 1453
26. Fodor S, Read L, Pirrung M, Light-directed, spatially addressable parallel chemical synthesis, Science, 1991, 251: 767-773
27. Fodor S, Maddively parallel genomics, Science, 1997, V277: 393-394
28. Schena M, Shalon D, Davis R W, Brown P O, Quantitative monitoring of gene expression patterns with a complementary DNA microarray, Science, 1995, V270: 467-470
29. De Risi J, Penland L, Brown P O, Bittner M L, Use of a cDNA microarray to analyse gene expression patterns in human cancer, Nature Genetics, 1996, V14: 457-460
30. Wallraff J, Labadie, DNA Sequencing on a chip, Chemtech, February, 1997
31. Joseph G, Hoheisel, Oligomer-Chip Technology, 1997, Novermber, Tibtech, V15
32. http://www.affmetrix.com
33. http://www.healthtech.com
34. http://www.biodiscovery.com
35. Chee M, Yang R, Hubbel, Accessing genetic information with high-density DNA arrays, Science, 1996, 274: 610-613
36. Pennis E, Chipping away at the human genome, Science, 1996, V272:1737
37. Nuwaysir EF, Bittner M, Trent J, Microarrays and toxicology: the advent of
toxicogenomics, Mol Carcinog, 1999, V24:153-159
38. Olda Ermolaeva, Mohit Rastogi, Data managament and analysis for gene expression arrays, Nature genetics, 1998, V20: 19-23
39. David J, Michael Bittner, Yidong Chen, Expression profiling using cDNA microarrays, Nature genetics supplement, 1999, V21:10-14
40. Pease A N, Solas D, Sullivan E J, Light generated oligonucleotide arrays for rapid DNA sequence analysis, Proc Natl Acad Sci, 1994, V91:5022-5026
41. Haralick R M, Sternberg S R, Zhuang X, Image analysis using mathematical morphology, IEEE Trans on PAMI, 1987, V9(4): 532-550
42. Canny J, Finding Edges and Lines in images, MIT June 1983
43. Canny J, A Computational Approach to Edge Detection, IEEE Trans on PAMI, 1986,V8(6): 678-698
44. Reddis S S, Rudin S F, An optimal multiple threshold scheme for image segmentation, IEEE Trans SMC, 1984, V14(4):661-665
45. Data management analysis for genechip, NHGRI
46. Trevor Hastie, Uwe Scherf, Gene Shaving-a new class of clustering Method of expression analysis
47．陈廷标，夏良正，“数字图像处理”，人民邮电出版社，1990
48．徐建华，“图像处理与分析”，科学出版社，1992
49．何斌，马天予，“Visual C++数字图像处理”，人民邮电出版社，2001
50．夏德深，傅德胜，“现代数字图像处理技术与应用”，东南大学出版社，1997
51．章敏晋，“图像处理和分析”，清华大学出版社，1999
52．吴健康，“图像处理和分析”，人民邮电出版社，1990
53．王新成，“高级图形处理技术”，中国科学技术出版社，2000
54．李楠，“激光扫描共聚焦显微术”，人民军医出版社，1997
55．崔屹，“数字图像处理技术与应用”，电子工业出版社，1997
56．孙啸，生物信息学与基因芯片，东南大学吴健雄实验室，2000
57．马立人，蒋中华，生物芯片，化学工业出版社，2000
58．陆祖宏，高密度基因芯片技术及其发展趋势，中国医疗器械信息，2000，V6，N3：14-16
59．王磊等，基于Canny理论的边缘提取改善方法，中国图像图形学报，1996，V1(3)：190-193
60．马然，陈颖，图像处理中数学形态学方法的基本研究，上海轻工业高等专科学校学报，2000，Vol．21，No．1
61．邢汝冰等，DNA芯片技术及国内外发展概况，光机电信息，2000，Vol．17，No．8
62．王浩军等，原位分子杂交图像中银粒的分割方法研究，中国图像图形学报，1999，V4(6)：454-457
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