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Lossless DNA Microarray Image Compression

Lossless DNA Microarray Image Compression. Source: Thirty-Seventh Asilomar Conference on Signals, Systems and Computers, Vol. 2, Nov. 2003, pp. 1501-1504 Authors: N. Faramarzpour, S. Shirani and J. Bondy Speaker: Chia-Chun Wu ( 吳佳駿 ) Date: 2005/05/13. Outline.

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Lossless DNA Microarray Image Compression

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  1. Lossless DNA Microarray Image Compression • Source: Thirty-Seventh Asilomar Conference on Signals, Systems and Computers, Vol. 2, Nov. 2003, pp. 1501-1504 • Authors: N. Faramarzpour, S. Shirani and J. Bondy • Speaker: Chia-Chun Wu (吳佳駿) • Date: 2005/05/13

  2. Outline • 1. Introduction • 2. Spiral path • 3. Proposed method • 4. Experimental results • 5. Conclusions • 6. Comments

  3. 1. Introduction • Microarray images are usually massive in size. • about 30MBytes or more • They propose the new concept of spiral path • which is an innovative tool for spatial scanning of images

  4. 2. Spiral path • The idea is to convert the 2D structure of an image into a 1D sequence • which can scan the image in a highly correlated manner while preserving its spatial continuity • It can be used for spatial scanning of any image • it is more useful for images with circular, or central behavior

  5. 2. Spiral path (a) (b)(c) Spiral path (a) spiral sequence (b) and its differential sequence (c)

  6. 2. Spiral path Table Ⅰ Matrix P for An 18 ×19 Image

  7. 3. Proposed method Calculated initial center coordinates Extract individual spots Input image Tune the spiral path 16 × 16 Divide the sequences Encode Last spot? Yes No Compressed files

  8. 3.1 Spot extraction where Im[i, j] is the image pixel value.

  9. 3.1 Spot extraction spot sub-image (16 x 16) White lines show how spot sub-images are extracted.

  10. 3.1 Spot extraction spot sub-image (16 x 16) mSub= 16, nSub = 16

  11. 3.2 Spiral path fitting where mSub and nSub are the size of extracted spot sub-image.

  12. 3.2 Spiral path fitting (9, 9) CenterX = (302×1+379×2+…+ 284×15+264×16)/ (302+379+…+ 284+264) =89916/10509= 9 Centery = 97214/10509= 9

  13. 3.2 Spiral path fitting Spiral path

  14. 3.3 Pixel prediction The linear interpolation function: and use ŷ to predict the intensity of our pixel based on r0, its distance to center. In (3) we have where yi s being their pixel values, ris being their distances from center and nNeighbor is the number of (yi, ri) pairs.

  15. 3.3 Pixel prediction Linear interpolation function for 5 neighbors used to predict intensity of the pixel with distance r0 from the center

  16. 3.4 Sequence coding First, we have a residual sequence with the length mSub×nSub-1 for a mSub×nSubspot sub-image. Spot parts and background parts of all spot sub-images of the microarray image are concatenated to form two files. Last, the adaptive Huffman coding is chosen for this application.

  17. 3.4 Sequence coding Spot parts Background parts (a) (b) Spiral path sequence (a) and prediction residual sequence (b)

  18. 3.4 Sequence coding (c) (d) Spot part (c) and background part (d)of residual sequence

  19. 4.1 Experimental results Table Ⅱ Cumulative Compressed Size of Original File (in Bytes) Header: spiral path centers, and first pixel intensity values

  20. 4.2 Experimental results Table Ⅲ Compression Ratio of Our Method Compared to Some Others

  21. 5. Conclusions • This paper proposed a lossless compression algorithm for microarray images. • Spiral path and linear neighbor prediction are some of the new concepts proposed in this work.

  22. 6. Comments • 從實驗結果可以明顯的發現，Spot區域的壓縮率相較於背景區域而言非常的低，因此可以針對Spot區域找到一個更適合的壓縮方法，以提昇整體的壓縮率。

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