Sharpness evaluation of microscopic detection image for micro parts

According to the characteristics of micro parts microscopic detection image, including the image texture is similar, the edge information is too little and the gray distribution Range is limited, based on the basic principles of algorithm, analyzes the traditional sharpness evaluation function. Aiming at the defect that the traditional sharpness evaluation function cannot have both high sensitivity and noise immunity, an algorithm based on local variance information entropy is proposed. The method uses the local variance to weight the self-information of each gray level, on the one hand, it makes up for the lack of spatial information of information entropy and avoids misjudgement of sharpness; on the other hand, it can increase the weights of clear region pixels when they participate in the calculation of information, while reducing the weights of background and noise region pixels, thereby improve the function sensitivity. The experimental results show that compared with the traditional sharpness evaluation function, the local variance information entropy function not only has high sensitivity, but also has better noise immunity and is suitable for actual auto-focusing systems.

Quantitative evaluation of hepatic steatosis using novel ultrasound technology normalized local variance (NLV) and its standard deviation with different ROIs in patients with metabolic-associated fatty liver disease: a pilot study

Purpose The purpose of this study was to evaluate the diagnostic performance of novel ultrasound technology normalized local variance (NLV) and the standard deviation of NLV (NLV-SD) using different ROIs for hepatic steatosis in patients with metabolic-associated fatty liver disease (MAFLD) and to identify the factors that influence the NLV value and NLV-SD value, using pathology results as the gold standard. Methods We prospectively enrolled 34 consecutive patients with suspected MAFLD who underwent percutaneous liver biopsy for evaluation of hepatic steatosis from June 2020 to December 2020. All patients underwent ultrasound and NLV examinations. NLV values and NLV-SD values were measured using different ROIs just before the liver biopsy procedure. Results The distribution of hepatic steatosis grade on histopathology was 4/19/6/5 for none (< 5%)/ mild (5–33%)/ moderate (> 33–66%)/ and severe steatosis (> 66%), respectively. The NLV value with 50-mm-diameter ROI and NLV-SD value with 50-mm-diameter ROI showed a significant negative correlation with hepatic steatosis (spearman correlation coefficient: − 0.449, p = 0.008; − 0.471, p = 0.005). The AUROC of NLV (50 mm) for the detection of mild, moderate, and severe hepatic steatosis was 0.875, 0.735, and 0.583, respectively. The AUROC of NLV-SD (50 mm) for the detection of mild, moderate, and severe hepatic steatosis was 0.900, 0.745, and 0.603, respectively. NLV (50 mm) values and NLV-SD (50 mm) values between two readers showed excellent repeatability and the intraclass correlation coefficient (ICC) was 0.930 (p < 0.001) and 0.899 (p < 0.001). Hepatic steatosis was the only determinant factor for NLV value and NLV-SD value (p = 0.012, p = 0.038). Conclusion The NLV (50 mm) and NLV-SD (50 mm) provided good diagnostic performance in detecting the varying degrees of hepatic steatosis with great reproducibility. This study showed that the degree of steatosis was the only significant factor affecting the NLV value and NLV-SD value.

Image Scale-Space Filtering Using Directional Local Variance Controlled Anisotropic Diffusion

The purpose of this paper is to develop an effective edge indicator and propose an image scale-space filter based on anisotropic diffusion equation for image denoising. We first develop an effective edge indicator named directional local variance (DLV) for detecting image features, which is anisotropic and robust and able to indicate the orientations of image features. We then combine two edge indicators (i.e., DLV and local spatial gradient) to formulate the desired image scale-space filter and incorporate the modulus of noise magnitude into the filter to trigger time-varying selective filtering. Moreover, we theoretically show that the proposed filter is robust to the outliers inherently. A series of experiments are conducted to demonstrate that the DLV metric is effective for detecting image features and the proposed filter yields promising results with higher quantitative indexes and better visual performance, which surpass those of some benchmark models.

Skin Lesion Extraction Using Multiscale Morphological Local Variance Reconstruction Based Watershed Transform and Fast Fuzzy C-Means Clustering

Early identification of melanocytic skin lesions increases the survival rate for skin cancer patients. Automated melanocytic skin lesion extraction from dermoscopic images using the computer vision approach is a challenging task as the lesions present in the image can be of different colors, there may be a variation of contrast near the lesion boundaries, lesions may have different sizes and shapes, etc. Therefore, lesion extraction from dermoscopic images is a fundamental step for automated melanoma identification. In this article, a watershed transform based on the fast fuzzy c-means (FCM) clustering algorithm is proposed for the extraction of melanocytic skin lesion from dermoscopic images. Initially, the proposed method removes the artifacts from the dermoscopic images and enhances the texture regions. Further, it is filtered using a Gaussian filter and a local variance filter to enhance the lesion boundary regions. Later, the watershed transform based on MMLVR (multiscale morphological local variance reconstruction) is introduced to acquire the superpixels of the image with accurate boundary regions. Finally, the fast FCM clustering technique is implemented in the superpixels of the image to attain the final lesion extraction result. The proposed method is tested in the three publicly available skin lesion image datasets, i.e., ISIC 2016, ISIC 2017 and ISIC 2018. Experimental evaluation shows that the proposed method achieves a good result.

Image Denoising of Adaptive Fractional Operator Based on Atangana–Baleanu Derivatives

A fractional integral operator can preserve an image edge and texture details as a denoising filter. Recently, a newly defined fractional-order integral, Atangana–Baleanu derivatives (ABC), has been used successfully in image denoising. However, determining the appropriate order requires numerous experiments, and different image regions using the same order may cause too much smoothing or insufficient denoising. Thus, we propose an adaptive fractional integral operator based on the Atangana–Baleanu derivatives. Edge intensity, global entropy, local entropy, and local variance weights are used to construct an adaptive order function that can adapt to changes in different regions of an image. Then, we use the adaptive order function to improve the masks based on the Grumwald–Letnikov scheme (GL_ABC) and Toufik–Atangana scheme (TA_ABC), namely, Ada_GL_ABC and Ada_TA_ABC, respectively. Finally, multiple evaluation indicators are used to assess the proposed masks. The experimental results demonstrate that the proposed adaptive operator can better preserve texture details when denoising than other similar operators. Furthermore, the image processed by the Ada_TA_ABC operator has less noise and more detail, which means the proposed adaptive function has universality.

On Null Hypotheses and Heteroskedasticity

Bivand and Wong (2018), a recent review on spatial statistical software, noted important differences in the results of the local Moran’s Ii statistic depending on the method of inference. That review speculated the differences may be due to the presence of local spatial heterogeneity. In this paper we design an experiment to assess the impact of local heterogeneity on hypothesis testing for local statistics. In this experiment, we analyze the relationship between measures of local variance, such as the local spatial heteroskedasticity (LOSH) statistic, and components of the local Moran’s Ii statistic. We consider this experiment with controlled synthetic heteroskedastic data and with uncontrolled real world data. We show that in both situations the variance components of the local Moran’s Ii statistic demonstrate a varying correlation with alternative measures of local variance like LOSH. In addition, we resituate the available inferential methods and suggest an alternative explanation for the differences observed in Bivand and Wong 2018. Ultimately, this paper demonstrates that there are important conceptual and computational differences as to what constituents a null hypothesis in local testing frameworks. Therefore, researchers must be aware as to how their choices may shape the observed spatial patterns.

Identification of the Characteristic Scale of Fine Ground Objects: A Case Study of the Core Observation Area in the Middle Reaches of the Heihe River Basin

Capturing the spatial heterogeneity and characteristic scale is the key to determining the spatial patterns of land surfaces. In this research, the core observation area of the middle reaches of the Heihe River Basin was selected as the study area, and the scale identification of several typical objects was carried out by implementing experiments on moderate- and high-resolution remotely sensed ASTER and CASI NDVI images. The aim was to evaluate the potential of the local variance and semivariance analysis to characterize the spatial heterogeneity of objects, track their changes with scale, and obtain their scales. Our results show that natural objects have multiscale structures. For a single object with a recognizable size, the results of the two methods are relatively consistent. For continuously distributed samples of indistinctive size, the scale obtained by the local variance is smaller than that obtained by the semivariance. As the image resolution becomes coarser and the research scopes expand, the scales of objects are also increasing. This article also indicates that with a small research area of uniform objects, the local variance and semivariance are easy to facilitate researchers to quickly select the appropriate spatial resolution of remote sensing data according to the research area.

Improving sampling accuracy of stochastic gradient MCMC methods via non-uniform subsampling of gradients

<p style='text-indent:20px;'>Many Markov Chain Monte Carlo (MCMC) methods leverage gradient information of the potential function of target distribution to explore sample space efficiently. However, computing gradients can often be computationally expensive for large scale applications, such as those in contemporary machine learning. Stochastic Gradient (SG-)MCMC methods approximate gradients by stochastic ones, commonly via uniformly subsampled data points, and achieve improved computational efficiency, however at the price of introducing sampling error. We propose a non-uniform subsampling scheme to improve the sampling accuracy. The proposed exponentially weighted stochastic gradient (EWSG) is designed so that a non-uniform-SG-MCMC method mimics the statistical behavior of a batch-gradient-MCMC method, and hence the inaccuracy due to SG approximation is reduced. EWSG differs from classical variance reduction (VR) techniques as it focuses on the entire distribution instead of just the variance; nevertheless, its reduced local variance is also proved. EWSG can also be viewed as an extension of the importance sampling idea, successful for stochastic-gradient-based optimizations, to sampling tasks. In our practical implementation of EWSG, the non-uniform subsampling is performed efficiently via a Metropolis-Hastings chain on the data index, which is coupled to the MCMC algorithm. Numerical experiments are provided, not only to demonstrate EWSG's effectiveness, but also to guide hyperparameter choices, and validate our <i>non-asymptotic global error bound</i> despite of approximations in the implementation. Notably, while statistical accuracy is improved, convergence speed can be comparable to the uniform version, which renders EWSG a practical alternative to VR (but EWSG and VR can be combined too).</p>