應用於生物晶片影像上之減少錯誤的｜國立中央大學博碩士論文系統

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研究生：	楊千柏 Chien-Po Yang
論文名稱：	應用於生物晶片影像上之減少錯誤的 Design Technique for Error Reduction On Automatic Segmentation In Microarray Image
指導教授：	蔡宗漢 Tsung-Han Tsai
口試委員:
學位類別：	碩士 Master
系所名稱：	資訊電機學院 - 電機工程學系 Department of Electrical Engineering
畢業學年度：	93
語文別：	英文
論文頁數：	79
中文關鍵詞：	生物晶片影像處理、生物晶片、生物資訊
外文關鍵詞：	bio-information, microarray, microarray image processing
相關次數：	點閱：6 下載：0
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學術和工業上在功能性基因組的研究中，微陣列基因雜交是一種受歡迎且高輸出的一個技術。這個microarray影像對於大範圍的基因序列和分析基因表現是考慮過的一個重要的工具和非常有用的技術。
有非常多的方法去分析microarray影像經由自動化切割斑點的邊緣或影像格子化，這些方法總是有相同的問題出自雜訊和歪斜的斑點矩陣，在強大的雜訊影響上是非常困難的自動化處理影像。在這篇論文中，我們減少在雜訊和歪斜的斑點矩陣的影響上找出邊緣中所發生的錯誤，我們對microarray影像應用自動的邊緣切割和格子化的處理，我們不但可以減少在自動的斑點切割發生的錯誤而且得到更精準的切割結果。我們只需使用低複雜度的方法而能得到更少的錯誤。只是運用Microarray上的一些特性，這些特性，如兩張影像的來源都是從同一個Microarray上掃瞄出來的影像和這些斑點都是像陣列一樣排列。
最後，我們跟ScanAlyze的軟體工具處理的結果做一個比較，因為ScanAlyze工具是運用人工的方式找出斑點的位置與大小而萃取斑出點上的資訊，我們得到與ScanAlyze工具分析的結果比較，平均差異約為1.43%，在我們提出的方法中，我們可以自動化分析Microarray影像且更精確的找出斑點的邊緣，而且更低的錯誤發生。

DNA Microarray hybridization is a popular high throughput technique in academic as well as in industrial genomics research. The Microarray image is considered as an important tool and powerful technology for large-scale gene sequence and gene expression analysis. There are many methods to analyze the Microarray image by automatic segmentation or gridding spot. These methods always have the same problem of noise and tilt in spot array. It is difficult to process strong noise image in automation. In this paper, we can reduce the error of the edge detection which is influenced by noise and tilt spot array. We apply an automatic segmentation usually application in video segmentation to process the Microarray image. We reduce the automatic spot segmentation errors and get more exact spot position. We only use low complexity methods and some simple concept by Microarray property. Using this property as two image scan in the same Microarray and spots are like array. Eventually, we compare the result with ScanAlyze tool because ScanAlyze tool extract spot position and edge by artificial interface. We obtain the 1.43% average differential value of spots analysis ratio in result with ScanAlyze. By the proposed method, we can get more accurate spot edge segmentation and lowest error in automatic analysis Microarray image.

ABSTRACT …………………………………………………………… I
LIST OF FIGURES ………………………………………………… IV
LIST OF TABLES ………………………………………………… VIII
Chapter  1  INTRODUCTION ………………………………… 1
1  Background …………………………………………………………………1
2  What’s Microarray ………………………………………………………… 2
2.1  Principle of the Microarray technique ……………………………… 2
2.2  Printing and fabrication ……………………………………………… 3
2.3  Target labeling and hybridization …………………………………… 4
2.4  Detection and image analysis ………………………………………… 6
2.5  Data analysis and management ……………………………………… 7
3  Microarray image analysis ………………………………………………… 7
3.1  Unsupervised analysis ………………………………………………… 8
3.2  Supervised analysis …………………………………………………… 8
4  Application of Microarrays ………………………………………………… 9
4.1  Cancer research ……………………………………………………… 10
4.2  Toxicology111.5Microarrays’ future ………………………………… 13
Chapter  2  PROCESSING MICROARRAY IMAGE METHODS ……………………………………………………… 14
1  Automatic process Microarray image …………………………………… 14
2  Automatic segmentation ………………………………………………… 15
2.1  Wavelet modulus maxima …………………………………………… 16
2.2  Markov random field (MRF) ……………………………………… 17
2.3  Mathematical morphology algorithm ………………………………… 19
3  Gridding image …………………………………………………………… 21
3.1  Fourier Methods and Grid Refinement ……………………………… 21
Chapter  3  REDUCTION ON AUTOMATIC SEGMENTATION ERRORS ………………………………… 24
1  Extraction of spot edge based on canny method ……………………… 24
2  The algorithm of reduction on automatic segmentation errors ……… 27
2.1  Proposed de-noise algorithm ……………………………………… 27
2.2  Insert algorithm ……………………………………………………… 29
3  Experimental result ……………………………………………………… 32
Chapter  4  DIGITAL TO ANALOG CONVERTER ……… 47
1  Modify partition current-steering DAC ………………………………… 49
2  Modified current mirror in PMOS-type ………………………………… 56
3  Segmented DAC Operation Principles …………………………………… 58
4  DAC simulation result …………………………………………………… 59
Chapter  5  CONCLUSION …………………………………… 61
REFERENCE ………………………………………………………… 62

                                

[1] Wang, X.H., Istepanian, R.S.H., Yong Hua Song, “Application of wavelet modulus maxima in Microarray spots recognition.” IEEE Transactions on NanoBioscience, Volume: 2, Issue: 4, Dec. 2003, pp.190 – 192.
[2] Demirkaya, O., Asyali, M.H., Shoukri, M.M., Abu-Khaba, K.S.;Engineering, ”Segmentation of Microarray cDNA spots using MRF-based method.” Proceedings of the 25th Annual International Conference of the IEEE, Volume: 1, Sept. 2003, pp. 674 – 677.
[3] Chiao-Ling Shih, Hung-Wen Chiu, “Automatic spot detection of cDNA Microarray images using mathematical morphology methods.” Conference on IEEE EMBS Asian-Pacific, Oct. 2003, pp. 70 – 71.
[4] Siddiqui, K.I., Hero, A.O., Siddiqui, M.M., ”Mathematical morphology applied to spot segmentation and quantification of gene Microarray images.” Conference on Conference Record of the Thirty-Sixth Asilomar, Volume: 1, Nov. 2002, pp. 926 – 930.
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[6] Hirata, Jr., R., Barrera, J., Hashimoto, R.F., Dantas, D.O., ”Microarray gridding by mathematical morphology.” Symposium on Computer Graphics and Image Processing, 2001 Proceedings of XIV Brazilian, Oct. 2001, pp. 112 – 119.
[7] M. Katzer, F. Kummert, and G. Sagerer, “A markov random field model of Microarray gridding.” In Proc. 18th ACM Symposium on Applied Computing, 2003.
[8] Canny, John., "A Computational Approach to Edge Detection," IEEE Transactions on Pattern Analysis and Machine Intelligence, Volume : 8, 1986, pp. 679 – 698.
[9] Pierre Soille, Morphological Image Analysis: Principles and Applications, Springer-Verlag, 2003, pp. 173-174.
[10] Van den Boomgaard, and Van Balen, "Image Transforms Using Bitmapped Binary Images," Computer Vision, Graphics, and Image Processing: Graphical Models and Image Processing, volume : 54, no. 3, May, 1992, pp. 254 – 258.
[11] Eisen, M. “ScanAlyze User Manual” (http://rana.lbl.gov/EisenSoftware.htm)
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[13] M. B. Eisen and P. O. Brown, “DNA arrays for analysis of gene expression,” Methods in Enzymology, vol. 303, 1999, pp. 179–205.
[14] P. Arena, M. Bucolo, L. Fortuna, and L. Occhipinty, “Celular neural networks for real-time DNA Microarray analysis,” IEEE Eng. Med. Biol. Mag., vol. 21, Mar./Apr. 2002, pp. 17–25.
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