Collaborative Priors with SVD for Denoising of cDNA Microarray Images

Using fuzzy logic and particle swarm optimization to design a decision-based filter for cDNA microarray image restoration

cDNA microarray technology enables whole genome study of gene expressions by measuring the differential expression of genes in microarray images. An important first step in analyzing microarray image is the accurate delineation of the cDNA spots in the image. We report here a fully automated spot segmentation algorithm for cDNA microarray images. The algorithm makes use of morphological operations, adaptive multi-level thresholding, and statistical intensity modeling to perform automatic grid addressing and spot segmentation. Our algorithm is robust for even poor quality cDNA microarray images.

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.

Irrespective of certain major advancement in filtering process in medical images, the denoising operation in microarray images are still considered to be unsolved and offers a large scope of research. Existing denoising principles are less investigated on such complex and massive dimensional microarray image that leads to the development of the proposed system. We present a method of performing simple denoising operation considering the presence of Gaussian noise in microarray image. From the target image denoising method, an improved version of patch-based denoising approach has been developed considering various forms of distance-based matching methods. The study outcome of the proposed system has been found to offer better peak signal-to-noise ratio and structural similarity index in contrast to existing filtering techniques.

Secure transmission of images have become an important in case of its application and research. This study proposes a discrete wavelet-based resolution synthesis using interpolation in discrete wavelet transform (DWT) with a novel encryption method. Highly resoluted image has its importance in various applications like radar, electron microscopy, digital X-ray mammography, optical coherence tomography, astronomy, etc. High-resolution image is recovered from a low-resolution image where the image is transformed using Cohen-Daubechies-Feauveau 9/7 wavelet to obtain significant effectiveness in image resolution. By using the DWT, low-quality image is divided into various subbands of different frequency levels. These low-resoluted images are then interpolated using Cohen-Daubechies wavelet 9/7 coefficients and this interpolation provides nonlinearity over the image in order to preserve the visual smoothness edges. Preserved subband images are projected using fanbeam projection. These preserved various level images are used for reconstruction. High-resoluted image is then obtained using inverse discrete wavelet transform. These images are encrypted using the Pascal series. A significant improvement is obtained over other 2-D wavelet decomposition techniques such as bilinear, bicubic, nearest neighbor, and box wavelets. This study also implements this technique with the Digital Imaging and Communications in Medicine (DICOM) images for the betterment of performance metrics like peak signal-to-noise ratio (PSNR) and structural similarity index (SSIM). These results are produced using MATLAB simulation software.

Complementary DNA microarray technology is a powerful tool in many areas. Usually a two channel microarray red-green (RG) image is obtained. Due to the nature of cDNA microarray technology, a number of impairments affect the cDNA microarray image before the analysis such as identification of differentially expressed genes. Microarray image processing plays a crucial role in the extraction and quantitative analysis of the relative abundance of the DNA product. In this paper, a method combined K-means clustering algorithm and mathematical morphology is presented. Mathematical morphology is a useful tool for extracting image components. K-means clustering algorithm has a good performance in the segmentation of microarray image processing. The result of the experiment shows that the method presented in this paper is accurate, automatic and robust.

A wavelet-based Markov random field segmentation model in segmenting microarray experiments

The accuracy of gene expression measurements generated using cDNA microarrays is dependent on the quality of the image generated following hybridization of fluorescently labelled cDNA. It is not known how this image is influenced by sample preparation factors which such as RNA quality, cDNA synthesis and labelling efficiency. In this study we used a simple metric based on the ratio of the total feature (F) and background (B) fluorescence, which correlates with the visual assessment of 60 microarray images, to determine the influence of sample preparation on image quality. Results indicate that RNA purity (A260/A280) and integrity (18S:28S ratio) do not strongly influence microarray image quality. cDNA having an nucleotide to dye ratio greater than 100 produced poor microarray images, however, cDNA labelled more efficiently was not a guarantee of a better image. The data also indicate that the array image quality is not improved by loading more cDNA into the hybridization mixture however poor image quality did result from a disproportionate amounts of Cy5 and Cy3 labelled cDNA. This study provides insight into the source of variation in microarray image analysis introduced during sample preparation and will assist in the standardisation of cDNA glass slide microarray protocols.

Objective To study the differential expression of recurrence-associated and imatinib resistance-associated gene between recurrent and unrecurrent gastrointestinal stromal tumors (GIST) and explore the molecular mechanism of drug resistance and recurrence of malignant GIST by cDNA microarray analysis.Methods A total of 5 pairs of recurrent and unrecurrent fresh GIST tissues were subjected to cDNA microarray with 41000 genes.Array results were validated by reverse transcription-polymerase chain reaction (RT-PCR).Results There were 1088 differential expression genes between recurrent and unrecurrent GIST tissues,including 592 genes up-regulated and 496 down-regulated.Most of the overexpressed genes were related to signaling pathway,oncogenesis,drug metabolism,cell adhesion,and the down-regulated genes were related to gap junction,tumor suppression,complement imumune defense.GIST related genes (c-kit,CD34) had no significant difference between recurrent and unrecurrent GIST tissues.RT-PCR confirmed the array results.Conclusion Gene expression profiling by cDNA microarray analysis provides not only molecular understanding of recurrence mechanism and imatinib resistance mechanism of GIST,but may also be helpful in discovering new therapeutic targets for recurrent and imatinib resistant GIST. Key words: Gastrointestinal stromal tumor; cDNA microarray; Recurrence; Imatinib

Due to light scattering by air particles, images taken under bad lighting conditions (such as haze, fog, mist, or smog) have a reduced level of visibility. One blurry image can be made visible again using single image dehazing techniques. Due to the ill-posedness of the Single image dehazing problem, it is difficult to solve. Even though, the Dark channel prior (DCP) has been the most prevalent method for image dehazing algorithms, but it suffers from huge computational time and picture quality. The accurate transmission map estimation is one of the best preferred ways to achieve the least computational time and high picture quality in DCP. Hence, this work is focused on constructing transmission map of a hazy image based on feed forward artificial neural network. The recommended method uses a feed forward ANN to conduct the transmission map straight from the minimum channel and a normalizing procedure to increase the re-covered image information. With a training set of eighty images, the network is trained by means of mean square error (MSE). The proposed method utilizing peak signal to noise ratio (PSNR) and structural similarity (SSIM) index measures are used to assess the restoration quality. The investigational conclusions have demonstrated that, the suggested method outperforms the dehazing of an input image without degrading visible quality (PSNR = 69.16, SSIM index = 0.8913). In addition, the proposed method is suited for real-time applications given the average computing time it achieves is 1.03 s.

Security and authentication of the people information is effectively provided through various watermarking algorithms. Most of the conventional watermarking algorithms have their own drawbacks in maintaining the quality and security of the image against various attacks. In order to overcome the above stated issues, a watermarking technique based on optimization is required for preserving image quality. In this paper, singular value decomposition (SVD) based digital image watermarking (DIW) scheme is proposed in hybrid discrete cosine transform (DCT) and discrete wavelet transform (DWT) using optimization algorithms like particle swarm optimization (PSO) and Jaya algorithm. The watermark image (WI) is embedded into low frequency DWT sub-band of DCT of cover image. PSO and Jaya algorithms are applied to improve the security threats and quality by assessing the fitness function. The work involves in utilizing the feature points in embedding of watermarks and analyzing the impact of various types of attacks with respect to image quality. The performance parameters like peak signal to noise ratio (PSNR), Structural Similarity Index (SSIM) and correlation coefficient of fitness function given by PSO and Jaya algorithm.

Watermarking is crucial for protecting medical images, guaranteeing data integrity and authenticity in telemedicine, especially for rural healthcare facilities with limited computational power. This paper introduces a lightweight watermarking scheme that performs a comparative study of Lifting Wavelet Transform (LWT) versus Discrete Wavelet Transform (DWT), both combined with Discrete Cosine Transform (DCT), to achieve optimal imperceptibility, robustness, and computational efficiency. The comparison of LWT and DWT is essential because LWT has lower computational complexity and memory usage, hence more appropriate for resource-limited environments than DWT, and without the use of Singular Value Decomposition (SVD) to further reduce processing requirements and improve compatibility with low-power devices. The suggested approach adopts adaptive embedding against texture complexity, secure synchronization patterns with m-sequences and Barker codes, and geometric correction utilizing ORB for guaranteed watermark extraction under attacks from JPEG compression, Gaussian noise attack, rotation attack, median filter attack, and Gaussian blur. A new error correction code interleaved with majority voting is employed to increase the watermark’s strength. Tested at various embedding strengths, the scheme attains Peak Signal to Noise Ratio (PSNR) values over 40 dB and Structural Similarity Index (SSIM) values above 0.95, signifying extremely little visual degradation. Low Bit Error Rates (BER) and high Normalized Correlation (NC) in various attack scenarios validate its success. This computational-friendly solution is designed to arm remote healthcare organizations with limited computer ability to reliably authenticate and attest to medical images, meeting severe telemedicine demands.

We use images in several important areas such as military, health, security, and science. Images can be distorted during the capturing, recording, processing, and storing. Image quality metrics are the techniques to measure the quality and quality accuracy level of the images and videos. Most of the quality measurement algorithms does not affect by small distortions in the image. Magnetic Resonance Imaging (MRI), Computed Tomography (CT), and Ultrasonic Imaging (UI) are widely used in the health sector. Because of several reasons it might be artifacts in the medical images. Doctor decisions might be affected by these image artifacts. Image quality measurement is an important and challenging area to work on. There are several metrics that have been done in the literature such as mean square error, peak signal-noise ratio, gradient similarity measure, structural similarity index, and universal image quality. Patient information can be an embedded corner of the medical image as a watermark. Watermark can be considered one of the image distortions types. The most common objective evaluation algorithms are simple pixel based which are very unreliable, resulting in poor correlation with the human visual system. In this work, we proposed a new image quality metric which is a Measure of Singular Value Decomposition (M-SVD). Experimental results show that novel M-SVD algorithm gives very promising results against Peak Signal to Noise Ratio (PSNR), the Mean Square Error (MSE), Structural Similarity Index Measures (SSIM), and 3.4. Universal Image Quality (UIQ) assessments in watermarked and distorted images such as histogram equalization, JPEG compression, Gamma Correction, Gaussian Noise, Image Denoising, and Contrast Change.

Image enhancement is an important step in image processing to improve contrast and information quality. Intelligent enhancement algorithms are gaining popularity due to the limitations of traditional methods. This paper utilizes a transformation function to enhance the global and local information of grayscale images, but the parameters of this function can produce significant changes in the processed images. To address this, the whale optimization algorithm (WOA) is employed for parameter optimization. New equations are incorporated into WOA to improve its global optimization capability, and exemplars and advanced spiral updates improve the convergence of the algorithm. Its performance is validated on four different types of images. The algorithm not only outperforms comparison algorithms in the objective function but also excels in other image enhancement metrics, including peak signal-to-noise ratio (PSNR), feature similarity index (FSIM), structural similarity index (SSIM), and patch-based contrast quality index (PCQI). It is superior to the comparison algorithms in 11, 6, 11, 13, and 7 images in these metrics, respectively. The results demonstrate that the algorithm is suitable for image enhancement both subjectively and statistically.

Watermarking has become a crucial technique for protecting copyright and verifying the authenticity of images due to the rapid distribution of digital images and the ease and reliability of image content modification. In this research, we introduce a hybrid digital picture watermarking method that is both secure and resilient. This would use the discrete wavelet transform (DWT), the discrete cosine transforms (DCT), singular value decomposition (SVD), and cryptographic systems such as RSA and chaotic maps. Hence, this method tries to balance the tradeoff of robustness, security, and imperceptibility. We first encrypt the watermark using RSA, and then a chaotic logistic map (CLM) scrambles the already designed encrypted watermark to further enhance its robustness. By putting the watermark in the host image's separately calculated DWT-DCT coefficients, the proposed method works well even when compression, noise addition filtering, and geometric attacks are used. The test results demonstrate that the suggested method does better than other methods in Structural Similarity Index (SSIM), Normalized Correlation (NC), and Peak Signal-to-Noise Ratio (PSNR). Regardless of the nature of the attack, our proposed method consistently generates PSNR values above 55 dB and NC values above to 0.90, indicating correct watermark extraction and excellent image perception. We use cryptographic methods to secure the watermark from retrieval and modification by unauthenticated users. In addition, our approach is superior to the current watermarking methods in robustness and unobtrusiveness. In further works, we plan to improve the robustness of the approach against more complex geometric attacks and apply it for watermarking video and 3D content.