Microarray Image Denoising Using Complex Gaussian Scale Mixtures of Complex Wavelets

Question

1. What are the contributions mentioned in the paper "Microarray image denoising using complex gaussian scale mixtures of complex wavelets by lakshmi srinivasan presented to the faculty of the graduate school of the university of texas at arlington in partial fulfillment of the requirements for the degree of master of science in electrical engineering the university of texas at arlington" ?

2. What are the different kinds of transforms available for processing microarray images?

3. What is the main reason why wavelet techniques are being used for image denoising?

4. What is the importance of denoising microarray images?

Accepted Answer

In this paper, the authors proposed an efficient noise reduction technique for DNA microarray images, by using an appropriate model for the complex wavelet coefficients, obtained by decomposition of these images using a complex transform.

Accepted Answer

Various kinds of transforms such as the discrete cosine20transform (DCT), the discrete Fourier transform (DFT), the discrete wavelet transform (DWT) and Complex Wavelet Transform (CWT) are available for processing microarray images.

Accepted Answer

Denoising of images corrupted by Gaussian noise using wavelet techniques is effective because of its ability to capture the energy of a signal in few energy transform values.

Accepted Answer

given the various sources of noise in the microarray procedure, microarray images need to be denoised to ensure reliable and accurate downstream analysis.

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Various algorithms proposed to solve this problem include fixed circle method in [5], adaptive circle method, hybrid k-means algorithm [7], Markov random field (MRF) modeling for the microarray spot segmentation [8], wavelet based MRF [9], etc.

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methods that account for the inter-channel noise and signal correlation between the red and the green channel images are more efficient in denoising the microarray images.

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The separation of gene spots in the image into distinct cells by constructing grids covering block of spots is called microarray image gridding.

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A mutation or corruption of a single nucleotide within a sequence interaction can have a significant impact on the final hybridization results.

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among the methods that are proposed for denoising the images, the wavelet based approaches have been promising due to its ability to analyze images in more than one resolution.

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The area of expertise of wavelet and curvelet transform has been studied by many and in particular its been pointed out in [34] that curvelets are computationally efficient for geometric features with line and surface singularities, while wavelets efficiently represent small features (such as eyes of Lena).

Accepted Answer

This im-50plies, joint estimation of the denoised subband coefficients is essential for effective noise reduction in these images.

Accepted Answer

These transcription values from the image analysis stage are commonly focused at finding similarities between the behavior genes under two different conditions or between known and unknown genes in the initial samples.

Accepted Answer

The authors find the best window size for denoising these microarray images using their proposed method such that, the PSNR of the output images is maximized.

Accepted Answer

Once the genes have been localized by the addressing process, the task becomes one of accurately separating the gene spots themselves from their local background area.

Microarray Image Denoising Using Complex Gaussian Scale Mixtures of Complex Wavelets

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Most frequently asked questions

2. What are the different kinds of transforms available for processing microarray images?

3. What is the main reason why wavelet techniques are being used for image denoising?

4. What is the importance of denoising microarray images?

5. What are the different algorithms proposed to solve this problem?

6. What are the advantages of denoising microarray images?

7. What is the definition of microarray image gridding?

8. What is the impact of a single nucleotide on the final hybridization results?

9. What are the advantages of the wavelet based approach for denoising the microarray?

10. What is the area of expertise of wavelet and curvelet transform?

11. What is the importance of the proposed method for effective noise reduction in microarray images?

12. What is the purpose of the transcription values from the image analysis stage?

13. How do the authors find the window size for denoising the microarray images?

14. What is the task of separating the gene spots from their local background?

Figures

Citations

Wavelet Denoising Algorithm Based on NDOA Compressed Sensing for Fluorescence Image of Microarray

Ultrasound Speckle Reduction Using Wavelet-Based Generative Adversarial Network

A New Image Denoising Method Based on Adaptive Multiscale Morphological Edge Detection

Unsupervised image segmentation for microarray spots with irregular contours and inner holes

Progressive Lossy-to-Lossless Compression of DNA Microarray Images

References

Image quality assessment: from error visibility to structural similarity

Bayesian Inference in Statistical Analysis

Bayesian Inference in Statistical Analysis

The dual-tree complex wavelet transform

Image denoising using scale mixtures of Gaussians in the wavelet domain

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