A Masi-Entropy Image Thresholding Based on Long-Range Correlation.

Many methods have been experimented to study decoherence in quantum dot (QD). Tsallis, Shannon and Gaussian entropy have been used to study decoherence separately; in this paper, we compared the results of the Gaussian, Shannon, and Tsallis entropies in 0-D nanosystem. The linear combination operator and the unitary transformation was used to derive the magnetopolaron spectrum that strongly interacts with the LO phonons in the presence of an electric field in the pseudoharmonic and delta quantum dot. Numerical results revealed for the quantum pseudo dot that: (i) the amplitude of Gauss entropy is greater than the amplitude of Tsallis entropy which in turn is greater than the amplitude of Shannon entropy. The Tsallis entropy is not more significant in nanosystem compared to Shannon and Gauss entropies, (ii) with an increase of the zero point, the dominance of the Gauss entropy on the Shannon entropy was observed on one hand and the dominance of the Shannon entropy on the Tsallis entropy on the other hand; this suggested that in nanosystem, Gauss entropy is more suitable in the evaluation of the average of information in the system, for the delta quantum dot it was observed that (iii) when the Gauss entropy is considered, a lot of information about the system is missed. The collapse revival phenomenon in Shannon entropy was observed in RbCl and GaAs delta quantum dot with the enhancement of delta parameter; with an increase in this parameter, the system in the case of CsI evolved coherently; with Shannon and Tsallis entropies, information in the system is faster and coherently exchanged; (iv) the Shannon entropy is more significant because its amplitude outweighs the others when the delta dimension length enhances. The Tsallis entropy involves as wave bundle; which oscillate periodically with an increase of the oscillation period when delta dimension length is improved.

The Fisher information (FI) measure is an important concept in statistical estimation theory and information theory. However, it has received relatively little consideration in image processing. In this paper, a novel algorithm is developed based on the nonparametric FI measure. The proposed method determines the optimal threshold based on the FI measure by maximizing the measure of the separability of the resultant classes over all of the gray levels. This method is compared with several classic thresholding methods on a variety of images, including some nondestructive testing (NDT) images and text document images. The experimental results show the effectiveness of the new method.

Tsallis entropy and the long-range correlation in image thresholding

Image analysis usually refers to processing of images with the goal of finding objects presented in the image. Image segmentation is one of the most critical tasks in automatic image analysis. The nonextensive entropy, also known as Tsallis entropy, is a recent development in statistical mechanics and has been considered as a useful measure in describing termostatistical properties of physical systems. In this new formalism a real quantity q was introduced as parameter for physical systems that presents long range interactions, long time memories and fractal-type structures. In image processing, one of the most efficient techniques for image segmentation is entropy-based thresholding. This approach uses the Shannon entropy from the information theory considering the gray level image histogram as a probability distribution. In this work, it was applied the Tsallis entropy as a generalized entropy formalism for information theory. For the first time it was proposed an image thresholding method using a nonextensive relative entropy.

Steam generator tubes and Obscured pipe lines like sewers, water mains have to be checked for their current condition. Cracks and defects are a strong indicator for the condition of a pipe. Electromagnetic nondestructive tests are important and widely used within the field of nondestructive evaluation (NDE). Magnetic Flux Leakage (MFL) has grown into a crucial method for inspection of pipelines and tubing in order to prevent long-term failures. Digital image processing techniques open the opportunity to accelerate the image analysis process, which may ease the operator from a lot of tedious task. An affordable way to detect those cracks is to take images of the pipeline and use image processing techniques to detect defects in these images. The Magnetic flux leakage images obtained from simulation software COMSOL multiphysics 4.3a are used for this work. Automatic segmentation is an important technique in the image processing. The basic idea of segmentation is to automatically select on gray-level values for separating object from the background. In this work, median filter is used to pre process the raw NDT image and three segmentation techniques are performed to segment the defect from the defective steam generator tube images. The performance evaluation of three segmentation algorithms namely region growing, minimum error thresholding and Morphological segmentation method for Non-Destructive testing (NDT) are performed and compared. Region growing technique is performed well for almost all MFL images.

By replacing linear averaging in Shannon entropy with Kolmogorov-Nagumo average (KN-average) or quasilinear mean and further imposing the additivity constraint, Rényi proposed the first formal generalization of Shannon entropy. Using this recipe of Rényi, one can prepare only two information measures: Shannon and Rényi entropy. Indeed, using this formalism Rényi characterized these additive entropies in terms of axioms of quasilinear mean. As additivity is a characteristic property of Shannon entropy, pseudo-additivity of the form x ⊕ <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">q</sub> y = x + y + (1 - q)xy is a characteristic property of nonextensive (or Tsallis) entropy. One can apply Rényi's recipe in the nonextensive case by replacing the linear averaging in Tsallis entropy with KN-average and thereby imposing the constraint of pseudo-additivity. In this paper we show that nonextensive entropy is unique under the Rényi's recipe, and there by give a characterization.

Sclera is the white outer layer of the eye surrounding the cornea. Nowadays, the applications of image processing techniques on sclera are widely used in various fields, such as pattern recognition technique, biometrics, machine learning, image analysis and a range of medical applications, beneficial to humanity. Segmentation is one of the various technique in image processing. In this paper, we propose a simple technique by using thresholding method to segment the sclera images. The segmentation algorithm was implemented on RGB images. The aim of this paper is to segment the sclera by using thresholding method. Global thresholding and color thresholding were introduced in this system for segmenting sclera image. The result showed that the proposed segmentation technique has successfully segmented the sclera images by removing the nonessential parts and retaining the particular region of interest. The proposed method showed that the segmentation algorithm was able to segment the sclera images with a promising percentage of similarity indexes.

Tsallis entropy has been widely used in image thresholding because of its non-extensive properties. The non-extensive parameter q contained in this entropy plays an important role in various adaptive algorithms and has been successfully applied in bi-level image thresholding. In this paper, the relationships between parameter q and pixels' long-range correlations have been further studied within multi-threshold image segmentation. It is found that the pixels' correlations are remarkable and stable for images generated by a known physical principle, such as infrared images, medical CT images, and color satellite remote sensing images. The corresponding non-extensive parameter q can be evaluated by using the self-adaptive Tsallis entropy algorithm. The results of this algorithm are compared with those of the Shannon entropy algorithm and the original Tsallis entropy algorithm in terms of quantitative image quality evaluation metrics PSNR (Peak Signal-to-Noise Ratio) and SSIM (Structural Similarity). Furthermore, we observed that for image series with the same background, the q values determined by the adaptive algorithm are consistently kept in a narrow range. Therefore, similar or identical scenes during imaging would produce similar strength of long-range correlations, which provides potential applications for unsupervised image processing.

Introduction: CT scan was first invented by Hounsfield in the twentieth century in 1972. But it was soon used in engineering, agriculture, biology, physics, chemistry, etc. Recently, with advances in computed tomography at the global level, the use of different generations of X-rays on a micrometer scale to study some of the different phenomena in soil science has begun. Due to the lack of geotechnical and soil mechanics studies in many engineering projects, CT scan image processing method can be used as a suitable method for extracting soil particle size and other soil characteristics. The main purpose of this study: a) The use of CBCT-scan in soil science for the first time in Iran. B) Comparing the ability of CBCT-scan in terms of quality of results with conventional methods. C) Identify the best filter and binary method (threshold). Another goal of this research is to acquaint more researchers with the application of computed tomography (CT-scan) technology in soil science studies. Material and Methods: The sampling area for this study was located in Diwandareh-Saqez axis in Kurdistan province, where six disturbed and undisturbed soil samples were collected in a sandy area (12 samples in total). In disturbed samples, particle size distribution was measured by ASTM D421 method, and the porosity of the samples was measured directly using the fuzzy equations in soil mechanics. In a radiology laboratory, three-dimensional images of intact soil samples were taken using a Planmeca Promax 3D CBCT CT scanner. In this study, ImageJ software was used to process CBCT-scan images. With this software, the percentage of phases, number of particles and particle size can be calculated. One of the most important steps in image processing is generating binary images. A total of 17 global thresholding methods have been proposed for generating binary images in ImageJ software. In this study, 15 standard methods for generating binary images were examined and the best method was selected. The total pore volume and soil particle size distribution of each sample calculated by quantifying X-ray images were compared with the total pore volume and soil particle size distribution obtained in the soil science laboratory and performance of the CT scan method evaluated by statistical parameters including The results of the accuracy evaluation for the correlation coefficient, mean absolute value of deviations, mean square error, root mean square error, and mean absolute error percentage. Results and discussion: The most significant point in image processing is the image thresholding method. In this study, due to the nature of CBCT-scan images, global thresholding was preferred. From the results of image processing, it can be understood that the results of binary images with Otsu and Intermodes methods are in complete agreement with the laboratory sample. The average of total porosity of the processing image slides is 44.03%, which is approximately consistent with the calculated 45/6% for the laboratory sample. Also, the average of ineffective porosity of the samples is about 6.53%. Therefore, it can be said that the effective porosity of the samples is about 37.5%. The results of the accuracy evaluation for the correlation coefficient, mean absolute value of deviations, mean square error, root mean square error, and mean absolute error percentage were 0.98, 1.082, 1.229, 1.108 and 2.334 respectively, indicating that the use of CBCT-scan images and image processing technique can identify and evaluate the geometric properties of granular soils with acceptable accuracy. The advantages of the computed tomography method of the soil are: (1) Obtaining information from the three-dimensional structure of the soil with appropriate accuracy in a short time, (2) Non-destructiveness of this method, and (3) Accurate separation into soil phases in different energy radiations. Conclusion: Using the processes defined by the authors for image processing, this technique is well able to determine some engineering features such as particle size distribution, total porosity, effective porosity and ineffective porosity. Also, the best thresholding method for binary images and processing in ImageJ is the Ostu and Intermodes method. The accuracy of the device used in this research is 0.2 mm, in other words, spaces or grains smaller than this value cannot be identified; For this reason, in the present study, the term coarse-textured soils, which means gravel to coarse-grained sand, has been emphasized. The results of evaluating the statistical parameters testify to the accuracy and ability of this method.

Breast cancer (BC) is the leading cause of death among women worldwide. It affects in general women older than 40years old. Medical images analysis is one of the most promising research areas since it provides facilities for diagnosis and decision-making of several diseases such as BC. This paper conducts a Structured Literature Review (SLR) of the use of Machine Learning (ML) and Image Processing (IP) techniques to deal with BC imaging. A set of 530 papers published between 2000 and August 2019 were selected and analyzed according to ten criteria: year and publication channel, empirical type, research type, medical task, machine learning techniques, datasets used, validation methods, performance measures and image processing techniques which include image pre-processing, segmentation, feature extraction and feature selection. Results showed that diagnosis was the most used medical task and that Deep Learning techniques (DL) were largely used to perform classification. Furthermore, we found out that classification was the most ML objective investigated followed by prediction and clustering. Most of the selected studies used Mammograms as imaging modalities rather than Ultrasound or Magnetic Resonance Imaging with the use of public or private datasets with MIAS as the most frequently investigated public dataset. As for image processing techniques, the majority of the selected studies pre-process their input images by reducing the noise and normalizing the colors, and some of them use segmentation to extract the region of interest with the thresholding method. For feature extraction, we note that researchers extracted the relevant features using classical feature extraction techniques (e.g. Texture features, Shape features, etc.) or DL techniques (e. g. VGG16, VGG19, ResNet, etc.), and finally few papers used feature selection techniques in particular the filter methods.

Radiography is one of the most common and widely used non-destructive testing (NDT) technique in inspecting weld discontinuity in welded joints. Conventionally, radiography inspector is requires to do the inspection analysis manually on weld discontinuity based on visual characteristics such as location, shape, length and density. The results can be very subjective, time consuming and inconsistent. Hence, semi-automated inspection using digital image processing and segmentation technique can be applied for weld discontinuity detection. The goal of this work is to detect the weld discontinuity on digital radiographic image using Foreground Marker Controlled Watershed. It is usually implemented in image processing because it always generates closed contour for each region in the image. In this paper, image enhancement on radiographic image is aim to remove image noise and improve image contrast. Then, marker controlled watershed with foreground markers is applied on the image to detect the discontinuity. The accuracy of the technique is evaluated using Receiver Operating Characteristic (ROC) curve. The accuracy of the technique has been compared with the ground truth and the result shows that the accuracy is 67% and area under the curve is 0.7134. The application of image processing technique in detecting weld discontinuity is able to assist radiographer to improve the inconsistent results in evaluating the radiographic image.

Tsallis entropy, a generalisation of Shannon entropy that depends on a parameter alpha, provides an alternative way of dealing with several characteristics of nonextensive physical systems given that the information about the intrinsic fluctuations in the physical system can be characterized by the nonextensivity parameter alpha. It is known that as the parameter alpha approaches 1, the Tsallis entropy corresponds to the Shannon entropy. Unlike for Shannon entropy, but similarly to R&eacute;nyi entropy (yet another generalisation of Shannon entropy that also depends on a parameter alpha and converges to Shannon entropy when alpha approaches 1), there is no commonly accepted definition for the conditional Tsallis entropy. In this work, we revisit the notion of conditional Tsallis entropy by studying some natural and desirable properties in the existing proposals: when alpha tends to 1, the usual conditional Shannon entropy should be recovered; the conditional Tsallis entropy should not exceed the unconditional Tsallis entropy; and the conditional Tsallis entropy should have values between 0 and the maximum value of the unconditional version. We also introduce a new proposal for conditional Tsallis entropy and compare it with the existing ones.

A In the medical field, the Image dispensation techniques are extensively employed for image amelioration in finding and treatment of lung disease in the big data environment, where the point in time feature is very paramount to determine the idiosyncrasy issues in intention images, particularly in lung disease such as cancer, pneumonia, COVID-19 etc, for early detection and treatment stages of lungs disease, Image processing technique are widely used for identification of genetic as well as environmental factors are very important in developing a novel method of lung disease prevention. The core factors of this research are quality, time, and precision of the dataset. The modification and evaluation of image quality depend on the segmentation techniques, an improved area of the object that is utilized as a rudimentary substructure of feature extraction is obtained and comparison is made on relying feature. Medical images are analyzed by different segmentation techniques of image processing. The segmentation techniques are used dataset to find patterns and retrieve information from the dataset for processing. The goal of this study is discussed various image processing techniques and big data analytics tools for lung disease has been given in the tabular form and provides comparative study. This study provides minutiae of big data analytics tools and image processing techniques, specifically discussed in the context of lung disease images.

Assessment of concrete compressive strength by image processing technique

Early blight is one of the major diseases of tomatoes that affects the leaves and fruit quality. Detection and estimation of the disease severity are performed using the visual observation method. Visual detection requires significant time for visual inspection of a large cultivated area. Thus, image processing techniques have proven to be an effective method as compared to visual analysis. In this study, digital image processing methods and techniques were used to detect early blight of tomato (EBT), estimate the disease severity, and classify tomato leaves. Totally, 198 infected plants were randomly taken from the Haramaya University research site "Rare" at four different times. Diseased potato leaf images were captured, resized, and stored for experimentation. The stored images were processed using median filtering to remove noise while preserving useful features in an image and image enhancement. The RGB images were transformed to gray scale and CIELAB color space, and the k-means clustering was applied to estimate the disease severity of the potato leaves, and Otsu’s thresholding algorithm was applied to estimate the disease severity of both the detached and live leaves. MATLAB algorithms will be developed to determine the total area and infected lesion area of the leaf samples.