致病微生物检测生物芯片图像去噪方法的研究

Method of microorganism detection microarray image denoising

致病微生物生物芯片图像去噪是国境口岸传染病疫情检测的重要步骤之一,对于芯片数据处理、信息提取和检测结果的准确性具有重要意义。本文用广义高斯分布对芯片图像子带的小波系数进行建模,在此基础上运用Bayes Shrink法对图像进行小波去噪。实验结果表明,这种方法能够在有效去除生物芯片图像噪声的同时,很好地保持图像的边缘,与其他几种去噪方法相比,不仅提高了去噪后图像的信噪比(SNR)和均方误差(MSE),而且使图像更加清晰,为芯片数据进一步分析处理和保证检测结果的准确性奠定了基础。

Microorganism detection microarray image denoising is a significant step in the process of microarray application, and is of great importance for microarray data processing and information extraction. In this paper Bayes Shrink method is used to remove noise on the basis of generalized Gaussian distribution statistical modeling for wavelet coefficients of microarray image. Experiment results show that this method can effectively suppress noise and preserve edge signal information of the microarray image compared with other conventional methods, which not only improves the SNR （signal-to-noise rate） and MSE （mean squared error） , but also makes denoised image clearer. This method lays the foundation for further microarray data analysis.