A high dynamic range vector quantisation for compressing 2DGE images of different exposures

Abstract

Currently, retrieving information from photographed two-dimensional gel electrophoresis (2DGE) images are sometimes insufficient for continuing pathogenic investigation. In this paper, we proposed a scheme to store multi-exposure copies of 2DGE images in a high dynamic range vector quantisation (HDRVQ) compressed file. 2DGE images can be retrieved for different exposures or a new exposure not of the original image. The high dynamic range concept was used to compost a detailed 2DGE image from neighbouring images. Results showed that 2DGE images can be retrieved from the HDRVQ compressed file for any exposure between the starting and ending exposures. Furthermore, the retrieved image of a new exposure can also display protein details that were not of the original images. The results of this research may be used by biologist to decide whether a new batch of gel culture was needed for further pathogenic investigation. A new batch is not only expensive but also very time consuming.

Keywords:

• two-dimensional gel electrophoresis image
• vector quantisation
• Linde–Buzo–Gray algorithm
• high dynamic range
• high dynamic range vector quantisation

This work was supported by the National Science Council of Taiwan under the contracts NSC94–2213E025–001, NSC93–2213E025002 and NSC92–2213E025003.