Anscombe Transformation Research Articles

3D visualization in turbid water using optimal photon counting and a GAT-based peplography method

Peplography is a three-dimensional (3D) approach for the visualization of targets in environments characterized by heavy scattering. It detects ballistic photons and subsequently generates a 3D image using integral imaging, where the expected number of ballistic photons is set arbitrarily. However, it affects the robustness of the method and accuracy in the analysis of results. Therefore, we propose a method to determine the optimal number of ballistic photons based on the maximum entropy of the photon counting image. Because accuracy in visualizing 3D targets may be compromised due to extraneous photons and fluctuations in photon intensity, we also address this challenge by integrating the generalized Anscombe transformation (GAT) with the conventional peplography technique. The noise caused by an image sensor during image acquisition can be modeled as a Poisson–Gaussian noise, and the photon counting process can be modeled as a Poisson process. Addressing such noise indirectly involves applying the GAT to the reconstructed image to stabilize its variance, denoising the stabilized data with a Gaussian denoising algorithm (i.e., non-local means filtering), and subsequently applying an exact unbiased inverse GAT to the denoised data. Experimental validation is conducted through experiments, with comparisons to conventional peplography, wavelet peplography, and a few standard dehazing and machine learning methods. Various image quality metrics such as correlation, structural similarity, the peak signal-to-noise ratio, and the natural image quality evaluator are used to demonstrate the superiority of the proposed method over conventional ones.

Neutron image denoising method based on adaptive new wavelet threshold function

Neutron radiography is an important nondestructive testing technique in the industrial field. Due to the influence of various factors in the neutron imaging process, the image quality will be reduced. Aiming at the Gaussian-Poisson mixed noise in neutron images, an adaptive new wavelet threshold function denoising method is proposed to reduce the influence of noise on neutron images. The basic idea is to transform the Gaussian-Poisson mixed noise into a noise that approximately follows the Gaussian distribution by generalized Anscombe transformation, and then use Particle Swarm Optimization (PSO) algorithm and wavelet threshold function denoising method to remove the mixed noise in the image. The new wavelet threshold function overcomes the problems of discontinuity in the traditional hard threshold function and fixed deviation in the wavelet coefficient of the traditional soft threshold function, and has the adjustment factor, which can combine the advantages of the traditional soft and hard threshold functions. PSO algorithm is used to find the optimal adjustment factor for image denoising. In addition, the new wavelet threshold function is continuous and smooth at the threshold, avoiding excessive strangulation of wavelet coefficients. By analyzing the fixed threshold defect, a threshold selection formula that decreases with the number of decomposition layers is proposed. The results of Matlab software experiments show that the new method can significantly improve the Peak Signal-to-Noise Ratio (PSNR) and reduce the Mean Square Error (MSE) of noisy images compared with the other four methods in removing Gaussian-Poisson mixed noise. Therefore, the new method can retain more image details and effectively improve the quality of neutron images.

CSSQ: a ChIP-seq signal quantifier pipeline.

Chromatin immunoprecipitation followed by sequencing (ChIP-seq) has revolutionized the studies of epigenomes and the massive increase in ChIP-seq datasets calls for robust and user-friendly computational tools for quantitative ChIP-seq. Quantitative ChIP-seq comparisons have been challenging due to noisiness and variations inherent to ChIP-seq and epigenomes. By employing innovative statistical approaches specially catered to ChIP-seq data distribution and sophisticated simulations along with extensive benchmarking studies, we developed and validated CSSQ as a nimble statistical analysis pipeline capable of differential binding analysis across ChIP-seq datasets with high confidence and sensitivity and low false discovery rate with any defined regions. CSSQ models ChIP-seq data as a finite mixture of Gaussians faithfully that reflects ChIP-seq data distribution. By a combination of Anscombe transformation, k-means clustering, estimated maximum normalization, CSSQ minimizes noise and bias from experimental variations. Further, CSSQ utilizes a non-parametric approach and incorporates comparisons under the null hypothesis by unaudited column permutation to perform robust statistical tests to account for fewer replicates of ChIP-seq datasets. In sum, we present CSSQ as a powerful statistical computational pipeline tailored for ChIP-seq data quantitation and a timely addition to the tool kits of differential binding analysis to decipher epigenomes.

Materials property mapping from atomic scale imaging via machine learning based sub-pixel processing

Direct visualization of the atomic structure in scanning transmission electron microscopy has led to a comprehensive understanding of the structure-property relationship. However, a reliable characterization of the structural transition on a picometric scale is still challenging because of the limited spatial resolution and noise. Here, we demonstrate that the primary segmentation of atomic signals from background, succeeded by a denoising process, enables structural analysis in a sub-pixel accuracy. Poisson noise is eliminated using the block matching and three-dimensional filtering with Anscombe transformation, and remnant noise is removed via morphological filtering, which results in an increase of peak signal-to-noise ratio from 7 to 11 dB. Extracting the centroids of atomic columns segmented via K-means clustering, an unsupervised method for robust thresholding, achieves an average error of less than 0.7 pixel, which corresponds to 4.6 pm. This study will contribute to a profound understanding of the local structural dynamics in crystal structures.

Deep Residual Learning with Anscombe Transformation for Low-Dose Digital Tomosynthesis

Deep Residual Learning with Anscombe Transformation for Low-Dose Digital Tomosynthesis

Estimation of genetic parameters for superovulatory response traits in Japanese Black cows.

The aim of this study was to estimate genetic parameters for superovulatory response traits in order to explore the possibility of genetic improvement in Japanese Black cows. We analyzed 19 155 records of the total number of embryos and oocytes (TNE) and the number of good embryos (NGE) collected from 1532 donor cows between 2008 and 2018. A two-trait repeatability animal model analysis was performed for both. Because records of TNE and NGE did not follow a normal distribution, the records were analyzed following no, logarithmic, or Anscombe transformation. Without transformation, the heritability estimates were 0.26 for TNE and 0.17 for NGE. With logarithmic transformation, they were 0.22 for TNE and 0.18 for NGE. With Anscombe transformation, they were 0.26 for TNE and 0.18 for NGE. All analyses gave similar genetic correlations between TNE and NGE, ranging from 0.60 to 0.71. Spearman’s rank correlation coefficient between breeding values of cows with more than 10 records was ≥0.95 with both transformations. Thus, the genetic improvement of TNE and NGE of donor cows could be possible in Japanese Black cattle.

A Multiprocessing Scheme for PET Image Pre-Screening, Noise Reduction, Segmentation and Lesion Partitioning.

Accurate segmentation and partitioning of lesions in PET images provide computer-aided procedures and doctors with parameters for tumour diagnosis, staging and prognosis. Currently, PET segmentation and lesion partitioning are manually measured by radiologists, which is time consuming and laborious, and tedious manual procedures might lead to inaccurate measurement results. Therefore, we designed a new automatic multiprocessing scheme for PET image pre-screening, noise reduction, segmentation and lesion partitioning in this study. PET image pre-screening can reduce the time cost of noise reduction, segmentation and lesion partitioning methods, and denoising can enhance both quantitative metrics and visual quality for better segmentation accuracy. For pre-screening, we propose a new differential activation filter (DAF) to screen the lesion images from whole-body scanning. For noise reduction, neural network inverse (NN inverse) as the inverse transformation of generalized Anscombe transformation (GAT), which does not depend on the distribution of residual noise, was presented to improve the SNR of images. For segmentation and lesion partitioning, definition density peak clustering (DDPC) was proposed to realize instance segmentation of lesion and normal tissue with unsupervised images, which helped reduce the cost of density calculation and completely deleted the cluster halo. The experimental results of clinical data demonstrate that our proposed methods have good results and better performance in noise reduction, segmentation and lesion partitioning compared with state-of-the-art methods.

Denoising Model of Terahertz Imaging of Honeycomb Material Based on Geometric Texture and Anscombe Transformation

Denoising Model of Terahertz Imaging of Honeycomb Material Based on Geometric Texture and Anscombe Transformation

A Parametric Bootstrap for the Mean Measure of Divergence.

For more than 50 years the Mean Measure of Divergence (MMD) has been one of the most prominent tools used in anthropology for the study of non-metric traits. However, one of the problems, in anthropology including palaeoanthropology (more often there), is the lack of big enough samples or the existence of samples without sufficiently measured traits. Since 1969, with the advent of bootstrapping techniques, this issue has been tackled successfully in many different ways. Here, we present a parametric bootstrap technique based on the fact that the transformed θ, obtained from the Anscombe transformation to stabilize the variance, nearly follows a normal distribution with standard deviation $\sigma = 1 / \sqrt{N + 1/2}$σ=1/N+1/2, where N is the size of the measured trait. When the probabilistic distribution is known, parametric procedures offer more powerful results than non-parametric ones. We profit from knowing the probabilistic distribution of θ to develop a parametric bootstrapping method. We explain it carefully with mathematical support. We give examples, both with artificial data and with real ones. Our results show that this parametric bootstrap procedure is a powerful tool to study samples with scarcity of data.

Improved Anscombe transformation and total variation for denoising of lowlight infrared images

The infrared image is corrupted by heavy Poisson noise in lowlight situation, which results in the loss of detail information. To suppress the heavy Poisson noise and keep the distinct edges of images in the lowlight situation, the denoising method based on the improved Anscombe transformation in wavelet domain is proposed. The Anscombe transformation in wavelet domain is improved to control the image distribution from Poisson into Gaussian distribution accurately; then the improved total variation regularization is applied to the wavelet-Anscombe domain for suppressing the heavy noise effectively with the optimal wavelet function. Denoising experiments on artificially degraded and practical lowlight infrared images show that the proposed denoising method can suppress the noise effectively and preserve the detail of images with heavy noise compared with several state-of-the-art methods.

Joint solution for PET image segmentation, denoising, and partial volume correction.

Segmentation, denoising, and partial volume correction (PVC) are three major processes in the quantification of uptake regions in post-reconstruction PET images. These problems are conventionally addressed by independent steps. In this study, we hypothesize that these three processes are dependent; therefore, jointly solving them can provide optimal support for quantification of the PET images. To achieve this, we utilize interactions among these processes when designing solutions for each challenge. We also demonstrate that segmentation can help in denoising and PVC by locally constraining the smoothness and correction criteria. For denoising, we adapt generalized Anscombe transformation to Gaussianize the multiplicative noise followed by a new adaptive smoothing algorithm called regional mean denoising. For PVC, we propose a volume consistency-based iterative voxel-based correction algorithm in which denoised and delineated PET images guide the correction process during each iteration precisely. For PET image segmentation, we use affinity propagation (AP)-based iterative clustering method that helps the integration of PVC and denoising algorithms into the delineation process. Qualitative and quantitative results, obtained from phantoms, clinical, and pre-clinical data, show that the proposed framework provides an improved and joint solution for segmentation, denoising, and partial volume correction.

Fuzzy genetic-based noise removal filter for digital panoramic X-ray images

This paper proposed a novel fuzzy genetic-based noise removal filter and surveyed the gain of popular filters for noise removal in the digital orthopantomography (OPG) images. The proposed filter is a non-invasive technique for attaining sub-clinical information from the areas of interest in each tooth, both jaws and maxillofacial.The proposed Poisson removal filter combines 4th-order partial differential equations (PDE), total variation (TV) and Bayes shrink threshold accompanied by fuzzy genetic algorithm (FGA) and the exact unbiased inverse of generalized Anscombe transformation (EUIGAT). Experiments were performed in order to show the effect of noise removal filters on 110 simulated, 106 phantom and 104 panoramic radiographic images for subjects (aged 30–60 years old, 50 males and 54 females). Various noises degraded filters and Canny edge detection was performed separately in three kinds of images. The program measured mean square error (MSE), peak signal to noise ratio (PSNR), image quality index (IQI), structural similarity index metric (SSIM) and figure of merit (FOM).The results verify that the proposed filter enhances physicians’ and dentists’ skill of diagnosing normal and pathological events in the teeth, jaws, temporomandibular joint (TMJ) regions and changeable anatomical panoramic landmarks related to osteoporosis progress in the mandible bone using noise removal and improving images quality. Experimental results show the superiority of this filter over other noise removal filters.

Filtrado wavelet en estudios planares óseos de Medicina Nuclear

Planar scintigraphy images present problems for the detection of small lesions, due to high noise contamination levels and low spatial resolution. In this work, an algorithm is introduced in order to reduce Poisson noise in these kind of images, by using Anscombe transformation followed by wavelet filtering. Material and methodsSimulated, noise-free lesions were inserted on bone images from real patients. Each image was contaminated with Poisson noise by using various noise intensities. Anscombe transformation was applied with the purpose of treating Poisson noise as gaussian noise. The images were then filtered into the wavelet domain using different filters. Finally, the quality of the images was assessed by using objective measurements, such as signal to noise ratio gain, normalised mean-squared error, and structural similarity index over regions of interest. ResultsIt was found that by applying Anscombe transformation before wavelet filtering, the resulting image quality was better in all cases, with a significant reduction of the noise levels (p=.015), with no noticeable deterioratio in the spatial resolution. ConclusionThe filtering process using wavelets coif3 with 5 decomposition levels, bior 3.5 with 5 levels, and db2 with 4 levels, showed the best results in our experiments. These results were better than those obtained using traditional filters.

Influence of Smear Layer on the Antimicrobial Activity of a Sodium Hypochlorite/Etidronic Acid Irrigating Solution in Infected Dentin

IntroductionThe aim of this study was to evaluate the influence of the smear layer on the antimicrobial activity of a 2.5% sodium hypochlorite (NaOCl)/9% etidronic acid (HEBP) irrigating solution against bacteria growing inside dentin tubules. MethodsDentin tubules were infected with Enterococcus faecalis by centrifugation. After 5 days of incubation, the smear layer had formed in half of the samples, which were then treated with 2.5% NaOCl either alone or combined with 9% HEBP for 3 minutes. The percentage of dead cells in infected dentinal tubules was measured using confocal laser scanning microscopy and the live/dead technique. The smear layer on the surface of the root canal wall was also observed by scanning electron microscopy. Results of the percentage of dead cells were compared using parametric tests after subjecting data to the normalized Anscombe transformation. The level of significance was P < .05. ResultsIn the absence of the smear layer, 2.5% NaOCl alone and combined with 9% HEBP showed high antimicrobial activity without significant differences between the 2. The smear layer reduced the antimicrobial activity of 2.5% NaOCl significantly, whereas the solution with HEBP was not affected. No dentin tubules free of the smear layer were obtained in the 2.5% NaOCl group. In the case of 2.5% NaOCl/9% HEBP, 95.40% ± 3.63% of dentin tubules were cleaned. ConclusionsThe presence of the smear layer reduced the antimicrobial activity of 2.5% NaOCl. The combination of 2.5% NaOCl/9% HEBP exerted antimicrobial activity that was not reduced by the smear layer.

Method for simulating dose reduction in digital mammography using the Anscombe transformation.

Purpose:This work proposes an accurate method for simulating dose reduction in digital mammography starting from a clinical image acquired with a standard dose.Methods:The method developed in this work consists of scaling a mammogram acquired at the standard radiation dose and adding signal-dependent noise. The algorithm accounts for specific issues relevant in digital mammography images, such as anisotropic noise, spatial variations in pixel gain, and the effect of dose reduction on the detective quantum efficiency. The scaling process takes into account the linearity of the system and the offset of the detector elements. The inserted noise is obtained by acquiring images of a flat-field phantom at the standard radiation dose and at the simulated dose. Using the Anscombe transformation, a relationship is created between the calculated noise mask and the scaled image, resulting in a clinical mammogram with the same noise and gray level characteristics as an image acquired at the lower-radiation dose.Results:The performance of the proposed algorithm was validated using real images acquired with an anthropomorphic breast phantom at four different doses, with five exposures for each dose and 256 nonoverlapping ROIs extracted from each image and with uniform images. The authors simulated lower-dose images and compared these with the real images. The authors evaluated the similarity between the normalized noise power spectrum (NNPS) and power spectrum (PS) of simulated images and real images acquired with the same dose. The maximum relative error was less than 2.5% for every ROI. The added noise was also evaluated by measuring the local variance in the real and simulated images. The relative average error for the local variance was smaller than 1%.Conclusions:A new method is proposed for simulating dose reduction in clinical mammograms. In this method, the dependency between image noise and image signal is addressed using a novel application of the Anscombe transformation. NNPS, PS, and local noise metrics confirm that this method is capable of precisely simulating various dose reductions.

Genetic analysis of superovulatory response of Holstein cows in Canada

Superovulation of dairy cattle is frequently used in Canada. The cost of this protocol is high, and so is the variability of the outcome. Knowing the superovulatory potential of a donor cow could influence the breeder’s decision to superovulate it or not. The main objective of this study was to perform a genetic analysis for superovulatory response of Holstein cows in Canada using data recorded by Holstein Canada, and to investigate if these data could be used for genetic evaluation. Data contained the total number of embryos and the number of viable embryos from every successful flushing performed across Canada. After editing, 137,446 records of superovulation performed between 1992 and 2014 were analyzed. A univariate repeatability animal model analysis was performed for both total number of embryos and number of viable embryos. Because both data and residuals did not follow a normal distribution, records were subject to either logarithmic or Anscombe transformation. Using logarithmic transformation, heritability estimates (SE) of 0.15 (0.01) and 0.14 (0.01) were found for total number of embryos and number of viable embryos, respectively. Using Anscombe transformation, heritability estimates (SE) of 0.17 (0.01) and 0.14 (0.01) were found for total number of embryos and number of viable embryos, respectively. The genetic correlation between the 2 traits was estimated at 0.97 using logarithmic transformation and 0.95 using Anscombe transformation. Breeding values were estimated for 54,463 cows, and 3,513 sires. Only estimated breeding values of sires having a reliability higher than 40% were considered for estimated breeding values correlations with other routinely evaluated traits. The results showed that selection for a higher response to superovulation would lead to a slight decrease in milk production, but an improvement for functional traits, including all reproduction traits. In all cases, the estimated correlations are either low or modest. We conclude that genetic selection for increased superovulatory response in donors is possible; daughters of sires with high estimated breeding values for superovulatory response will tend to yield more embryos, whereas the additive effect of service sire seems not to contribute to the variability of the 2 superovulation traits and was not significantly correlated with the additive effect of the donor.

Efficacy of antimicrobial solutions against polymicrobial root canal biofilm.

To evaluate the antibiofilm activity of 2.5% sodium hypochlorite (NaOCl), 2% chlorhexidine (CHX), 2% alexidine (ALX) and 0.2% cetrimide (CTR) alone and in combination on mature polymicrobial root canal biofilms on human dentine using confocal laser scanning microscopy (CLSM). Twenty-eight human dentine specimens were infected for 21days with microbial samples collected from infected root canals of three volunteers. Antibiofilm activity of the irrigating solutions was evaluated after 3min of contact time under CLSM. For quantification purposes, bioimage_L software was used. The variables evaluated were the log10 of total biovolume (μm3 ) and percentage of live cells (green) population. Statistical analysis of both variables was performed using an anova test and a post hoc Duncan test to determine significant clusters amongst groups. The variable green population percentage was previously subjected to the normalized Anscombe transformation. The NaOCl group had a total biovolume and percentage of live cells significantly lower than the other groups (P˂0.001). The addition of 0.2% CTR significantly increased the antimicrobial effect of 2% CHX (P˂0.001). There were no significant differences between 0.2% CTR, 2% ALX and the combination of both (P˂0.05). Overall, 2.5% NaOCl dissolved and killed bacteria significantly more efficiently when used against polymicrobial mature biofilm on human dentine. Cetrimide improved the antimicrobial activity of chlorhexidine and alexidine.

Neutron radiographic image restoration using BM3D frames and nonlinear variance stabilization

Neutron radiography is a powerful tool for non-destructive investigations in industrial applications. However, the resulting images are degraded inevitably due to some physical limitations. In this paper, we propose a new scheme for neutron image restoration, which utilizes BM3D frames and nonlinear variance stabilization including generalized anscombe transformation and its exact unbiased inverse. Experimental results show that superior to the existing restoration methods, the proposed scheme improves the restoration quality efficiently and exhibits better visual results.

A real-time video denoising algorithm with FPGA implementation for Poisson–Gaussian noise

The denoising function in digital imaging devices must consider resource consumption and real-time capability in addition to effective noise-removal performance. One commonly used denoising method is pixel similarity weighted frame averaging (PSWFA). In this study, we improve the denoising capability of PSWFA using a pre-filter that consists of a downsampling operator and a small Gaussian filter. Moreover, given that noise in digital imaging devices is signal dependent and is typically modeled as a Poisson---Gaussian distribution, we introduce generalized Anscombe transformation to remove the signal dependency by rendering the noise variance constant. The transformed image can be considered corrupted by an approximately Gaussian noise. To embed our algorithm in hardware, we implement our algorithm on a Spartan-6 FPGA for evaluation. We also compare our algorithm with some existing denoising methods on FPGA. For further evaluation of the denoising ability, the algorithm is compared with some state-of-the-art algorithms that are not implemented on FPGA but have high performance on a personal computer. Experimental results on both simulated noise videos and actually captured low-light noise videos show the effectiveness of our algorithm, particularly in the processing of large-scale noise.

Segmentation based denoising of PET images: an iterative approach via regional means and affinity propagation.

Delineation and noise removal play a significant role in clinical quantification of PET images. Conventionally, these two tasks are considered independent, however, denoising can improve the performance of boundary delineation by enhancing SNR while preserving the structural continuity of local regions. On the other hand, we postulate that segmentation can help denoising process by constraining the smoothing criteria locally. Herein, we present a novel iterative approach for simultaneous PET image denoising and segmentation. The proposed algorithm uses generalized Anscombe transformation priori to non-local means based noise removal scheme and affinity propagation based delineation. For nonlocal means denoising, we propose a new regional means approach where we automatically and efficiently extract the appropriate subset of the image voxels by incorporating the class information from affinity propagation based segmentation. PET images after denoising are further utilized for refinement of the segmentation in an iterative manner. Qualitative and quantitative results demonstrate that the proposed framework successfully removes the noise from PET images while preserving the structures, and improves the segmentation accuracy.