1 A JPEG-LS Based Lossless/Lossy Compression Method for Two-Dimensional Electrophoresis Images Source: 2003 International Conference on Informatics, Cybernetics, and Systems Authors: Kevin I-J Ho, Tung-Shou Chen, Hui-Fang Tsai, Mingli Hsieh, and Chia-Chun Wu Speaker: Chia-Chun Wu ( 吳佳駿 ) Date: 2004/12/09

NCHU 2 Outline  Introduction Introduction  Schema Schema  Compression Method Compression Method  Decompression Method Decompression Method  Results Results  Conclusion Conclusion

NCHU 3  We use Lossless and Near-Lossless compress the important areas and unimportant areas in Two-Dimensional Electrophoresis (2D-Gel) images.  Our system improves traditional JPEG-LS to enhancing the compressed image quality. Introduction

NCHU 4 Schema (1/2)  Compression flow chart 3 Original 2D-Gel Image Detect Protein ’ s Areas 1 Record Boolean Value of Important Areas 2 JPEG-LS Near-Lossless Compress ５ 4 Write Difference Value of Original Image’s Important Areas Difference value Record File Near-Lossless Compressed File

NCHU 5  Decompression flow chart Add Difference to Image’s Pixel Value Add Difference to Image’s Pixel Value Keep Important Information of 2D-Gel Image JPEG-LS Near-Lossless Decompress Near-Lossless Compressed File Difference value Record File Near-Lossless Decompressed 2D-Gel Image Schema (2/2)

NCHU 6 Compression Method (1/5)  Original 2D-Gel image － This is an original 2D-Gel image. X-axis represented the PH value of protein and Y-axis represented the amount molecular weight. Fig. 1 Original 2D-Gel image X-axis Y-axis

NCHU 7 Compression Method (2/5)  Fetching protein’s area. － To collect the important protein ‘s areas of 2D-Gel image. The colourful areas will treat as important areas, and white areas will treat as unimportant areas. Fig. 2 The important part of 2D-Gel image

NCHU 8 Compression Method (3/5)  Transform the important parts to Boolean value － Boolean value True(1) represents important areas, whereas False (0) represents unimportant areas. Fig. 3 Boolean value record file of important part

NCHU 9 Compression Method (4/5)  Image after JPEG-LS Near-Lossless compression － This is an 2D-Gel image after traditional JPEG-LS Near-Lossless compression. Fig. 4 Image after JPEG-LS Near-Lossless compression

NCHU 10 Compression Method (5/5)  Difference value records important part － The difference value of 2D- Gel image via the original image and lossless compression will store in a record file. Fig.5 Difference value record file

NCHU 11 Compression Example Original 2D-Gel Image Image after JPEG-LS Near-Lossless compression Difference value record file - =

NCHU 12 Decompression Method (1/3) Fig 6.Image after JPEG-LS Near-Lossless compression  The image of Near-Lossless decompression － This is a decompressed image after traditional JPEG-LS Near- Lossless compression

NCHU 13 Decompression Method (2/3)  Modify the protein’s area of important part － Next, we base on the difference value of pixels for modifying the protein’s area of important parts. Fig. 7 Difference value record file

NCHU 14 Decompression Method (3/3) Fig. 8 Our system’s lossless compression of important part.  The lossless image of our system’s important areas － This complete 2D-Gel image is though traditional JPEG-LS Near-Lossless compression technique

NCHU 15 Decompression Example Image after JPEG-LS Near-Lossless compression Difference value record file Our system’s lossless compression of important part + =

NCHU 16 Results (1/4) Fig. 9 Partial magnify image of original 2D-Gel image  Fig. 9 is the result of the amplification of dotted frame in Fig. 1.

NCHU 17 Fig. 10 Partial magnify image of traditional JPEG-LS. Results (2/4)  Fig.10 is the result of the amplification of dotted frame in Fig. 4.

NCHU 18 Fig. 11 Partial magnify image of our system. Results (3/4)  Fig. 11 is the result of the amplification of dotted frame in Fig. 8.

NCHU 19 Result (4/4) Table 1. The Comparison of image quality in traditional JPEG-LS Near-Lossless with our system (PSNR value). Unit:dB Results (4/4)

NCHU 20 Conclusion  We store the unimportant areas by Near-Lossless method. But we store important areas by Lossless method. It is very important to medical images.  Under different lossless level, we can find out our system has better image quality than traditional JPEG-LS.  Therefore, how to compress the size of record file and detect the protein’s location more correctly becoming an important topic in the future.