1. Lossy Compression of DNA Microarray Images
Source: Electrical and Computer Engineering,
Vol. 2, May 2004, pp
Authors: N. Faramarzpour, S. Shirani
and M.J. Deen
Speaker: Chia-Chun Wu (吳佳駿)
Date: 2005/04/01

2. Outline 1. Introduction 2. Proposed method 3. Experimental results
4. Conclusions
5. Comments

3. 1. Introduction Microarray images are usually massive in size.
about 30MBytes (2560 × 4096) or more
Lossless compression is the low compression rate that can be achieved.
2.2:1 in good implementations [4, 5]

4. Optimize spot coordinates and radius
2. Proposed method
Initial radius: (16+16)/2
= 16
Calculated initial spot coordinates and radius
Extract individual spots
Input image
Optimize spot coordinates and radius
16 × 16
Apply Circle to Square (C2S) transform
DCT, quantize, and encode
Last spot?
Yes No
Compressed files

5. where Im[i, j] is the image pixel value.
2.1 Spot extraction
where Im[i, j] is the image pixel value.

6. 2.1 Spot extraction spot sub-image (16 x 16)
(c) White lines show how spot sub-images are extracted.

7. 2.1 Spot extraction spot sub-image (16 x 16) msub= 16, nsub = 16 14 1517 16 18 1 22 25 24 19 12 13 2 28 35 42 47 44 39 32 3 20 21 34 43 56 60 64 57 49 31 4 59 63 65 40 5 46 61 70 62 48 6 53 68 54 7 27 52 8 9 10 51 45 58 55 11 23 50 30 26 37 41
spot sub-image
(16 x 16)
msub= 16, nsub = 16

8. 2.2 Parameter extraction The initial value for the radius us given by:
where γ has a value between 7 and 8,
Rp usually converges in 4 or 5 iterations.

9. 2.2 Parameter extraction (9, 9) CenterX = 89916/10509= 914 15 17 16 18 1
249 22 25 24 19 12 13 2
286 28 35 42 47 44 39 32 3
441 20 21 34 43 56 60 64 57 49 31 4
620 59 63 65 40 5
704 46 61 70 62 48 6
758 53 68 54 7
793 27 52 8
769 9
779 10
786 51 45 58 55 11
750 23 50
745 30 26 37 41
716
598
302
379
528
680
767
791
811
855
886
878
856
824
744
660
284
264
CenterX = 89916/10509= 9
Centery = 97214/10509= 9
Initial radius
Rp=(msub+nsub)/γ
= (16+16)/2= 16

10. 2.3 Circle to Square(C2S) transform
Geometric representation of C2S transform

11. 2.3 Circle to Square(C2S) transform
First, r and Θ are calculated for every pixel belonging to the square. Then we have:
where (Xt, Yt) is the center of the square. And then:
x is expected to have a value in the range of [0, Rp].

12. 2.3 Circle to Square(C2S) transform35 36 37 38 39 40 34 41 42 47 44 43 56 60 64 57 49 59 63 65 46 61 70 62 48 53 68 54 50 52 45 51 58 55 14 15 17 16 18 22 25 24 19 12 13 28 35 42 47 44 39 32 20 21 34 43 56 60 64 57 49 31 59 63 65 40 46 61 70 62 48 53 68 54 27 52 51 45 58 55 23 50 30 26 37 41
C2S

13. 2.3 Circle to Square(C2S) transform
(a) A microarray image before, and
(b) after applying C2S transform to each of its spots.

14. 2.4 DCT, Quantization, and Encoding
First, the image is divided into 8 × 8 blocks.
Those blocks are then DCT transformed.
After DCT is applied to each block, the transformed blocks are quantized.
Last, the arithmetic coding is used for this application.

15. 3. Experimental results
The rate distortion curve achieved by JPEG compared to lossy compression method for the same test image.

16. 4. Conclusions
This paper proposed a new algorithm for lossy compression of microarray images.
For various applications which have images with circular patterns, using a stage of C2S transform can provide improvement the overall performance of the compression.

17. 5. Comments
由於DNA Microarray Images背景顏色是不重要的，因此，可以利用本篇論文偵測Spot的方法，將偵測出來的Spot視為重要的ROI區域，以無失真的方式進行壓縮；反之，背景則視為不重要的區域，用失真的方法進行壓縮，以提高整體的壓縮率。