Biologic chip image wavelet de-noising method based on Bayesian estimation

Abstract

A biologic chip image has a larger noise signal, which is caused by factors, such as manufacture, hybridization and cleaning of a biological chip, pollution of dust in a testing process, interference of a testing sample and an instrument noise, hybridization non-specific reaction and the like. The invention provides a biologic chip image wavelet de-noising method based on the Bayesian estimation, which comprises the following steps of: firstly expanding the biologic chip image containing the noise into a wavelet coefficient through wavelet conversion; determining a Bayesian shrinkage threshold on the basis of estimating signal variance and noise variance; extracting an important wavelet coefficient by using the Bayesian shrinkage threshold to complete threshold processing and de-noising processing of the image; and finally enabling the de-noised wavelet coefficient to subject the wavelet reverse conversion to reconstruct the image and outputting the de-noised image. The invention has the advantages that the method has favorable effect of de-noising the biologic chip image, thereby the background noise is smoothened, and edge details of sample points are reserved so as to lay the foundation on further analyzing the chip data and ensuring the correctness of a detection result.

Description

technical field [0001] The invention belongs to the field of biological signal image processing, relates to a biological chip image denoising method, in particular to a biological chip image wavelet denoising method based on Bayesian estimation. Background technique [0002] Biochip refers to a microarray array composed of many nucleic acid molecules fixed on a solid phase support in a small area according to predetermined positions. Because the biochip integrates thousands of sample points on a tiny substrate, and each sample point expresses certain biological information, use a chip scanner to scan and collect the hybridized image of the chip, and analyze the image of the biochip, The intensity or ratio of each target area in the array is extracted, combined with the chip description in the database (the sequence of each probe and the position of the probe on the chip) to determine the detection result of the biochip. [0003] Biochip technology is not limited to the prep...

AI Technical Summary

Problems solved by technology

Although mean filtering has the advantages of simplicity and intuition, due to the uneven contamination caused by the biochip during the experiment, the traditional mean filtering uses the same filtering amplitude for the entire image, that is to say, each pixel value is its own. The average of the sum of each pixel value in the neighborhood makes the edge of the image sample point blurred, and the degree of blurring is proportional to the size of the template

Mean filtering is achieved at the cost of sacrificing important grayscale information, and it is difficult to guarantee the accuracy of subsequent analysis

Median filtering is a nonlinear signal processing method that does not consider the statistical characteristics of pixels, resulting in the loss of useful details of the chip image

At the same time, using the median filter multiple times, although the impulse noise can basically be completely eliminated, it will cause blurring and roughening of the image edge

In addition, if the spatial density of impulse noise in biochip images is large, the effect of median filtering will be greatly reduced

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