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RGB color space fundamentals. (a) Additive color mixing. (b) RG-based color mixing with zero blue components.
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This paper introduces an automated image processing procedure capable of processing complementary deoxyribonucleic acid (cDNA) microarray images. Microarray data is contaminated by noise and suffers from broken edges and visual artifacts. Without the utilization of a filter, subsequent tasks such as spot identification and gene expression determina...
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... point, Fig. 3 depicts the magnitude and the orientation of a conventional RGB vector, with a nonzero B component, in a three-dimensional vector space. It should be mentioned that for microarray images the vectorial inputs are uniquely defined by their R and G components (yellow is ob- tained using the linear combination of R and B components; see Fig. 4) and, thus, the geometrical interpretation reduces to the 2-D case shown previously in Fig. 2. ...
Citations
... The performance of the MINOR method can be also assessed when filtering real noise contamination in cDNA image which determines gene expression levels 102 . As can be observed in Fig. 15, impulsive noise is removed, edges are well restored and the visible color artifacts are eliminated. ...
In this paper, a novel approach to the mixed Gaussian and impulsive noise reduction in color images is proposed. The described denoising framework is based on the Non-Local Means (NLM) technique, which proved to efficiently suppress only the Gaussian noise. To circumvent the incapacity of the NLM filter to cope with impulsive distortions, a robust similarity measure between image patches, which is insensitive to the impact of impulsive corruption, was elaborated. To increase the effectiveness of the proposed approach, the blockwise NLM implementation was applied. However, instead of generating a stack of output images that are finally averaged, an aggregation strategy combining all weights assigned to pixels from the processing block was developed and proved to be more efficient. Based on the results of comparisons with the existing denoising schemes, it can be concluded that the novel filter yields satisfactory results when suppressing high-intensity mixed noise in color images. Using the proposed filter the image edges are well preserved and the details are retained while impulsive noise is efficiently removed. Additionally, the computational burden is not significantly increased, compared with the classic NLM, which makes the proposed modification applicative for practical image denoising tasks.
... 7a) and 8a) shows that the proposed Robust Mean-Shift offers very satisfying image enhancement results when only impulsive noise is present, what makes this algorithm very versatile. The effectiveness of the proposed technique is also confirmed in Fig. 11, which presents the enhancement results of real noisy images depicting 2 works of art and also a cDNA microarray 116 . As can be observed the noise of unknown characteristic is well suppressed and edges are sharpened. ...
... Filtering results of the proposed ROMS using real noisy images. From top to bottom: cropped and zoomed part of the painting "The Milkmaid" by Johannes Vermeer, a miniature from the Balthasar Behem Codex and a cDNA microarray 116 . The images were processed using the parameters: α = 3 , σ = 30 and r = 1. ...
Noise reduction is one of the most important topics of digital image processing and despite the fact that it has been studied for a long time it remains the subject of active research. In the following work, we present an extension of the Mean Shift technique, which is efficiently reducing the Gaussian noise, so that it is able to cope with the impulsive disturbances. Furthermore, the elaborated technique can be applied to enhance the images corrupted by a mixture of strong Gaussian and impulsive noise, severely decreasing the quality of color digital images. By means of our approach, which is based on a novel similarity measure between a pixel and a patch located in the center of the processing block, even heavily disturbed images can be effectively restored, which enables the success of further stages of the image processing pipeline. We evaluate the efficiency of the proposed method using a publicly available database of test color images and compare the restored images applying a set of standard quality metrics with the results delivered by state-of-the-art denoising methods. Additionally, we compare our method with the Medoid and Quick Shift techniques, accelerating the original Mean Shift algorithm, in terms of objective quality criteria and computational complexity. The results of the performed experiments indicate that the proposed technique is superior to the widely used denoising techniques and can be used as a robust extension of the Mean Shift procedure. In the paper, a particular emphasis is placed on the ability of the presented algorithm to preserve and enhance image edges. The performed experiments evaluated with the use of the Pratt’s index, quantitatively confirm the superiority of the proposed design over the Mean Shift and standard denoising methods. The preservation of edges and even their sharpening is a very important feature of our algorithm whereas the final goal is segmentation, playing a crucial role in various computer vision tasks. The proposed algorithm is intended for the mixed noise reduction in color images, but it can be also applied in multispectral imaging and clustering of multidimensional data. To enable the comparison of our method with the standard denoising techniques and to help applying it in other image processing fields, we made its code freely available.
... Mixed Noise: In many real-life applications, images are corrupted by more than one noise type. The mixture of Gaussian and impulse noise is found in computed tomography (CT) images and cDNA microarray imaging [10], [11]. The mixed noise in cDNA microarray imaging occurs due to photon and electronic noise interaction, dust particles on surface of glass slides, and laser reflection. ...
Images are susceptible to various kinds of noises, which corrupt the pictorial information stored in the images. Image de-noising has become an integral part of the image processing workflow. It is used to attenuate the noises and accentuate the specific image information stored within. Machine learning is an important tool in the image-de-noising workflow in terms of its robustness, accuracy, and time requirement. This paper explores the numerous state-of-the-art machine-learning-based image de-noisers like dictionary learning models, convolutional neural networks and generative adversarial networks for a range of noises like Gaussian, Impulse, Poisson, Mixed and Real-World noises. The motivation, algorithm and framework of different machine learning de-noisers are analyzed. These de-noisers are compared using PSNR as quality assessment metric on some benchmark datasets. The best de-noising results for different noise type is discussed along with future prospects. Among various Gaussian noise de-noisers, GCBD, BRDNet and PReLU network prove to be promising. CNN+LSTM, and MC2RNet are most suitable CNN-based Poisson de-noisers. For impulse noise removal, Blind CNN, and CNN+PSO perform well. For mixed noise removal, WDL, EM-CNN, CNN, SDL, and Mixed CNN are prominent. De-noisers like GRDN and DDFN show accurate results in the domain of real-world de-noising.
... Markov random field (MRF) and active-contour-based methods are presented by the authors in [5]. The technique based on the order-statistics was presented in [6]. In [7] correlation-statistics-based approach is presented. ...
Gene expression in large scale analysis is performed by microarray imaging and its accuracy is based on the experiments performed and processing the image further. It is known well that the noise produced during the gene expression analysis will affect the accuracy significantly. The quality of microarray image is affected by several errors particularly noise. Various noise types are present in an image that generates different influence on image processing as well as it is not essential to eliminate every noise, this noise elimination of noise effects establishes difficult issue in the analysis of microarray images. Conventionally several mathematical approaches are utilized for the noise estimation when processing the microarray images. The restoration model was developed in this paper. Noise image is provided as an input and the noise type is estimated by the probability density function (PDF) utilizing appropriate filter for image denoising and restored microarray images are produced. Image sharpening is performed by Blind deconvolution and the image with noise mixture are restored by bilateral filter. Therefore, good restored images are produced from the simulation results with increased Peak Signal to Noise Ratio (PSNR) values and decreased Mean Squared Error (MSE) values.
... Random-valued impulse noise takes random value in the range [0, 255]. Gaussian noise and impulse noise often occur simultaneously as a result of sensor temperature and faulty sensor triggering [4]- [6]. However, the removal of mixed Gaussian and impulse noise is regarded as an ill-posed inverse problem because the distributions of the two types of noise differ significantly. ...
Although image denoising as a basic task of image restoration has been widely studied in the past decades, there are not many studies on mixed noise denoising. In this paper, we propose two structured dictionary learning models to recover images corrupted by mixed Gaussian and impulse noise. These two models can be merged as l
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-norm regularization. The fidelity term is used to fit image patches and the regularization term is employed for sparse coding. Particularly, we utilize proximal (and proximal linearized) alternating minimization methods as the main solvers to deal with these two models. We remove the Gaussian noise under the assumption that the uncorrupted image can be approximated with a linear representation under an appropriate orthogonal basis. We use different ways to remove impulse noise for these two models. The experimental results are reported to compare the existing methods and demonstrate the performance of the proposed denoising model is better than the other existing methods in terms of some quality assessment metrics.
... These filters involve reduced ordering 9,10 of a set of input vectors within a window to compute the output vector. Recent applications of these include enhancement of cDNA microarray images, 11,12 virtual restoration of artwork, 13,14 and video filtering. [15][16][17][18] The motivation of this study is twofold. ...
A comprehensive survey of 48 filters for impulsive noise removal from color images is presented. The filters are formulated using a uniform notation and categorized into 8 families. The performance of these filters is compared on a large set of images that cover a variety of domains using three effectiveness and one efficiency criteria. In order to ensure a fair efficiency comparison, a fast and accurate approximation for the inverse cosine function is introduced. In addition, commonly used distance measures (Minkowski, angular, and directional-distance) are analyzed and evaluated. Finally , suggestions are provided on how to choose a filter given certain requirements. © 2007 SPIE and IS&T.
... The model based segmentation technique suggested by Q. Li et al. [13] for segmenting microarray spots uses clustering techniques but removes small disconnected clusters based on some threshold assuming that they are artifacts. The spot segmentation using the Markov random field method (MRF) [14], [15] utilizes neighbor information, along with intensity information based on an MRF modeling of the compartment. Although this combination of intensity and spatial information results in a more accurate pixel classification process, it requires an initial classification of the pixels, which in turn affects the final results. ...
... In, [20] Markov random field (MRF) approach has been proposed. In [21] Gottardo presented a method based on the MRF, in which the intensity of the background and foreground are shown by t-distribution. Nagarajan proposed another method, which the segmentation procedure of each spot is conducted based on the correlation of statistical information of spots. ...
DNA microarray is a powerful approach to study simultaneously, the expression of 1000 of genes in a single experiment. The average value of the fluorescent intensity could be calculated in a microarray experiment. The calculated intensity values are very close in amount to the levels of expression of a particular gene. However, determining the appropriate position of every spot in microarray
images is a main challenge, which leads to the accurate classification of normal and abnormal (cancer) cells. In this paper, first a preprocessing approach is performed to eliminate the noise and artifacts available in microarray cells using the nonlinear anisotropic diffusion filtering method. Then, the coordinate center of each spot is positioned utilizing the mathematical morphology operations. Finally, the position of each spot is exactly determined through applying a novel hybrid model based on the principle component
analysis and the spatial fuzzy c‑means clustering (SFCM) algorithm. Using a Gaussian kernel in SFCM algorithm will lead to improving the quality in complementary DNA microarray segmentation. The performance of the proposed algorithm has been evaluated on the real microarray images, which is available in Stanford Microarray Databases. Results illustrate that the accuracy of microarray cells segmentation in the proposed algorithm reaches to 100% and 98% for noiseless/noisy cells, respectively
... Hybrid approaches include methods based on Markov random fields (MRF) like [21] and [22] which combine neighboring and intensity information based on an MRF modeling of the quadrilaterals of the microarray image. ...
Complementary DNA (cDNA) microarray is a well-established tool for simultaneously studying the expression level of thousands of genes. Segmentation of microarray images is one of the main stages in a microarray experiment. However, it remains an arduous and challenging task due to the poor quality of images. Images suffer from noise, artifacts, and uneven background, while spots depicted on images can be poorly contrasted and deformed. In this paper, an original approach for the segmentation of cDNA microarray images is proposed. First, a preprocessing stage is applied in order to reduce the noise levels of the microarray image. Then, the grow-cut algorithm is applied separately to each spot location, employing an automated seed selection procedure, in order to locate the pixels belonging to spots. Application on datasets containing synthetic and real microarray images shows that the proposed algorithm performs better than other previously proposed methods. Moreover, in order to exploit the independence of the segmentation task for each separate spot location, both a multithreaded CPU and a graphics processing unit (GPU) implementation were evaluated.
... 4 Reduction of noise, as in any other field, is of great importance in microarray technology. There have been studies regarding denoising microarray images, such as this work 10 , in which the authors have used nonlinear filtering based on robust order statistics to remove noise from the image. Also in this study 11 , a component based approach for reduction of noise in microarray images is proposed. ...
Microarray technology has become a power tool in the field of bioinformatics. It is used to measure gene expression levels and similar to any other image capturing processes is prone to noise. There are different kinds of noise, during preparation, hybridization and scanning in microarray images which usually are modeled by Gaussian noise. Since introduction of wavelets in 1970s, many more forms and extensions of this transform have been developed and used, such as stationary wavelet transform (SWT), complex wavelet transform (CWT), curvelet transform (CURV) and contourlet transform (CNT). By developing of more sparse transforms, it is important to have a perspective of how efficient the transforms are in different applications, such as microarray image analysis. In this paper, we compare the efficiency of common sparse transforms including ordinary discrete wavelet transform (DWT), SWT, CWT, CURV, CNT, Contourlet-SD decomposition, steerable pyramid (STP) and shearlet transform (SHR) for microarray image denoising. Therefore after converting microarray image into x-let transform, BayesShrink method, soft and hard thresholding are used to perform denoising of these images. Both local and general thresholds are calculated for each subband in order to evaluate the effect of incorporating intrascale dependency on top of sparsity property in statistical modeling of x-let's coefficients. Our simulation results show that CWT and SHR outperforms the others when using global thresholding and SWT is the preferred transform when using local thresholding. Although STP and SHR have better performance for some criteria like structural similarity (SSIM) index, but CWT is faster.
