On denoising and compression of DNA microarray images

Adjeroh, Donald A.; Zhang, Yong; Rahul, Parthe,

doi:10.1016/j.patcog.2006.02.019

Cited by 22 publications

(18 citation statements)

References 36 publications

(90 reference statements)

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“…The average results presented take into account the different sizes of the images, i.e., they correspond to the total number of bits divided by the total number of image pixels. Table 5 confirms the performance of the image-dependent method relatively to two recent specialized methods for compressing microarray images: MicroZip (Lonardi and Luo, 2004) and Zhang's method (Adjeroh et al, 2006;Zhang et al, 2005). As can be observed, the imagedependent multi-bitplane finite-context method provides compression gains of 9.1% relatively to MicroZip and 6.2% in relation to Zhang's method, on a set of test images that has been used by all these methods.…”

Section: Image-dependent Finite-context Modelssupporting

confidence: 59%

“…Then, for entropy coding, each stream is divided into two 8 bit sub-streams and arithmetic encoded, with the option of being previously processed by a Burrows-Wheeler transform. The method proposed by Adjeroh et al (2006); Zhang et al (2005) is based on PPAM (Prediction by Partial Approximate Matching). PPAM is an image compression algorithm which extends the PPM text compression algorithm, considering the special characteristics of natural images (Zhang et al, 2005).…”

Section: Wwwintechopencommentioning

confidence: 99%

“…This information is then used for evaluating the expression level of individual genes (Hegde et al, 2000;Moore, 2001). The common approach for microarray compression has been based on image analysis for spot finding (griding followed by segmentation) with the aim of separating the microarray image data into different streams based on pixel similarities (Adjeroh et al, 2006;Faramarzpour and Shirani, 2004;Faramarzpour et al, 2003;Hua et al, 2003;Jörnsten et al, 2003;2002a;Lonardi and Luo, 2004;Zhang et al, 2005). Once separated, the streams are compressed together with the segmentation information.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Compression of Microarray Images

Neves¹,

Pinho²

2010

Signal Processing

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Section: Image-dependent Finite-context Modelssupporting

confidence: 59%

Section: Wwwintechopencommentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Compression of Microarray Images

Neves¹,

Pinho²

2010

Signal Processing

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“…It follows that the incorrect demarcation of the circular dot features will propagate throughout the whole microarray data processing and will add to the other sources of variability of microarray data: biological variability, technical variability and labeling (Zakharkin et al, 2005). Several methods have been proposed to de-noise data (Adjeroh et al, 2006). These include adaptative split and merge algorithm (Barra, 2006), polynomial-hyperbolic spot shape model in combination with the Box-Cox transformation (Ekstrøm et al, 2004), spectral embedding (Higgs et al, 2006), noise-resistant algorithms (Novikov and Barillot, 2006), background extraction (O'Neill and Magoulas, 2003), just to name few recent contributions.…”

Section: Detection Of Microarray Featuresmentioning

confidence: 99%

‘Extremotaxis’: Computing with a bacterial-inspired algorithm

2008

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We present a general-purpose optimization algorithm inspired by "run-and-tumble", the biased random walk chemotactic swimming strategy used by the bacterium Escherichia coli to locate regions of high nutrient concentration The method uses particles (corresponding to bacteria) that swim through the variable space (corresponding to the attractant concentration profile). By constantly performing temporal comparisons, the particles drift towards the minimum or maximum of the function of interest. We illustrate the use of our method with four examples. We also present a discrete version of the algorithm. The new algorithm is expected to be useful in combinatorial optimization problems involving many variables, where the functional landscape is apparently stochastic and has local minima, but preserves some derivative structure at intermediate scales.

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“…Wang et al [27] proposed enhancing the microarray image using the stationary wavelet transform (SWT) [28]. Another approach based on a variant of the translation-invariant wavelet transform was proposed by Adjeroh [29].…”

Section: Introductionmentioning

confidence: 99%

Microarray image enhancement by denoising using decimated and undecimated multiwavelet transforms

Zifan¹,

Moradi

Gharibzadeh

2009

SIViP

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In this paper, we present a new approach to deal with the noise inherent in the microarray image processing procedure. We use the denoising capabilities of decimated and undecimated multiwavelet transforms, DMWT and UMWT respectively, for the removal of noise from microarray data. Multiwavelet transforms, with appropriate initialization, provide sparser representation of signals than wavelet transforms so that their difference from noise can be clearly identified. Also, the redundancy of the UMWT transform is particularly useful in image denoising in order to capture the salient features such as noise or transients. We compare this method with the discrete and stationary wavelet transforms, denoted by DWT and SWT, respectively, and the Wiener filter for denoising microarray images. Results show enhanced image quality using the proposed approach, especially in the undecimated case in which the results are comparable and often outperform that of the stationary wavelet transform. Both multiwavelet transforms outperform the DWT and the Wiener filter.

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On denoising and compression of DNA microarray images

Cited by 22 publications

References 36 publications

Compression of Microarray Images

Compression of Microarray Images

‘Extremotaxis’: Computing with a bacterial-inspired algorithm

Microarray image enhancement by denoising using decimated and undecimated multiwavelet transforms

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