Gray Level Difference Research Articles

CT texture analysis predicts abdominal aortic aneurysm post-endovascular aortic aneurysm repair progression

The aim of this study is to investigate the role of early postoperative CT texture analysis in aneurysm progression. Ninety-nine patients who had undergone post-endovascular aneurysm repair (EVAR) infra-renal abdominal aortic aneurysm CT serial scans were enrolled from July 2014 to December 2019. The clinical and traditional imaging features were obtained. Aneurysm texture analysis was performed using three methods—the grey-level co-occurrence matrix (GLCM), the grey-level run length matrix (GLRLM), and the grey-level difference method (GLDM). A multilayer perceptron neural network was applied as a classifier, and receiver operating characteristic (ROC) curve analysis and area under the curve (AUC) analysis were employed to illustrate the classification performance. No difference was found in the morphological and clinical features between the expansion (+) and (−) groups. GLCM yielded the best performance with an accuracy of 85.17% and an AUC of 0.90, followed by GLRLM with an accuracy of 87.23% and an AUC of 0.8615, and GLDM with an accuracy of 86.09% and an AUC of 0.8313. All three texture analyses showed superior predictive ability over clinical risk factors (accuracy: 69.41%; AUC: 0.6649), conventional imaging features (accuracy: 69.02%; AUC: 0.6747), and combined (accuracy: 75.29%; AUC: 0.7249). Early post-EVAR arterial phase-derived aneurysm texture analysis is a better predictor of later aneurysm expansion than clinical factors and traditional imaging evaluation combined.

A reliable ensemble-based classification framework for glioma brain tumor segmentation

Glioma is one of the most frequent primary brain tumors in adults that arise from glial cells. Automatic and accurate segmentation of glioma is critical for detecting all parts of tumor and its surrounding tissues in cancer detection and surgical planning. In this paper, we present a reliable classification framework for detection and segmentation of abnormal tissues including brain glioma tumor portions such as edemas and tumor core. This framework learns weighted features extracted from the 3D cubic neighborhoods regarding to gray-level differences that indicate the spatial relationships among voxels. In addition to intensity values in each slice, we consider sets of three consecutive slices to extract information from 3D neighbors. Classification procedure is defined based on combination of support vector machines regarding to an ensemble learning method. In order to regularize and improve the output of the classifier framework, we design a post-process procedure based on statistical concepts. The proposed framework is trained and tested with BRATS datasets, and comparative analysis is implemented. Experimental results indicate competitive performance compared to the state-of-the-art methods. The achieved accuracy is characterized by the overall mean Dice index of 88%.

Characteristic Parameters Extraction Method of Hidden Karst Cave from Borehole Radar Signal

A hidden karst cave has a prominent influence on the design and construction of underground engineering. In order to improve the automatic identification accuracy of hidden karst cave areas and the acquisition accuracy of characteristic parameters in borehole radar signals, combined with the reflection characteristic relationship between radar electromagnetic waves and hidden karst caves, research methods on borehole radar image pre-processing and feature parameter extraction of hidden karst caves are carried out. First, based on the original signal of a borehole radar, the reflection characteristic relationship between the electromagnetic waves of the borehole radar and a hidden karst cave is established, and the geometric model of borehole radar detection of hidden karst caves is formed. Then, the reconstruction of borehole radar signals is realized by searching the peak position of Pmusic function combined with the subspace MUSIC method. Combining the continuity of hyperbola and the gray level difference of a nonreflective region, an improved variance method is proposed, which is more suitable for the actual borehole radar image segmentation. It realizes the suspected hidden karst cave inversion by setting a threshold of continuous pixels, a simultaneous gradient operator, and a maximum interspecific variance method. Finally, according to the geometric model of borehole radar detection, the energy-weighted fitting of a hyperbolic profile is carried out, and the improved Hough transform method is used to solve the multiple characteristic parameters of the hyperbolic model, so as to extract the characteristic parameters of the hidden cave from the borehole radar signal. The example analyzed proves that the method is feasible and accurate, which can provide important data support for underground engineering.

Fuzzy Gray Level Difference Histogram Equalization for Medical Image Enhancement.

Contrast enhancement methods are used to reduce image noise and increase the contrast of structures of interest. In medical images where the distinction between normal and abnormal tissue is subtle, accurate interpretation may become difficult if noise levels are relatively high. To provide accurate interpretation and clearer image for the observer with reduced noise levels "a novel adaptive fuzzy gray level difference histogram equalization algorithm" is proposed. At first, gray level difference of an input image is calculated using the binary similar patterns. Then, the gray level differences are fuzzified in order to deal the uncertainties present in the input image. Following the fuzzification, fuzzy gray level difference clip limit is computed to control the insignificant contrast enhancement. Finally, a fuzzy clipped histogram is equalized to obtain the contrast-enhanced MR medical image. The proposed algorithm is analysed both visually and analytically to calculate its performance against the other existing algorithms. Visual and analytical results on various test images affirm that the proposed algorithm outperforms all other existing algorithms and provide a clear path to analyse the fine details and infected portions effectively.

Correlation Between Mammographic Radiomics Features and the Level of Tumor-Infiltrating Lymphocytes in Patients With Triple-Negative Breast Cancer.

Objectives: Tumor-infiltrating lymphocytes (TILs) have been identified as a significant prognostic indicator of response to neoadjuvant therapy and immunotherapy for triple-negative breast cancer (TNBC) patients. Herein, we aim to assess the association between TIL levels and mammographic features in TNBC patients.Methods: Forty-three patients with surgically proven TNBC who underwent preoperative mammography from January 2018 to December 2018 were recruited. Pyradiomics software was used to extract 204 quantitative radiomics features, including morphologic, grayscale, and textural features, from the segmented lesion areas. The correlation between radiological characteristics and TIL levels was evaluated by screening the most statistically significant radiological features using Mann–Whitney U-test and Pearson correlation coefficient. The patients were divided into two groups based on tumor TIL levels: patients with TIL levels <50% and those with TIL levels ≥50%. The correlation between TIL levels and clinicopathological characteristics was assessed using the chi-square test or Fisher's exact test. Mann–Whitney U-test and Pearson correlation coefficient were used to analyze the statistical significance and Pearson correlation coefficient of clinical pathological features, age, and radiological features.Results: Of 43 patients, 32 (74.4%) had low TIL levels and 11 (25.6%) had high TIL levels. The histological grade of the low TIL group was higher than that of the high TIL group (p = 0.043). The high TIL group had a more negative threshold Ki-67 level (<14%) than the low TIL group (p = 0.017). The six most important radiomics features [uniformity, variance, grayscale symbiosis matrix (GLCM) correlation, GLCM autocorrelation, gray level difference matrix (GLDM) low gray level emphasis, and neighborhood gray-tone difference matrix (NGTDM) contrast], representing qualitative mammographic image characteristics, were statistically different (p < 0.05) among the low and high TIL groups. Tumors in the high TIL group had a more non-uniform density and a smoother gradient of the tumor pattern than the low TIL group. The changes in Ki-67, age, epidermal growth factor receptor, radiomic characteristics, and Pearson correlation coefficient were statistically significant (p < 0.05).Conclusion: Mammography features not only distinguish high and low TIL levels in TNBC patients but also can act as imaging biomarkers to enhance diagnosis and the response of patients to neoadjuvant therapies and immunotherapies.

Detection of Winding Faults Using Image Features and Binary Tree Support Vector Machine for Autotransformer

Autotransformer (AT) is the most core power supply equipment, and overvoltage and short circuit (SC) fault may lead to winding deformation, which will have a negative impact on its insulation and even affect the operation of a train. The frequency response analysis (FRA) is widely used for detecting winding faults in a transformer. However, the direct measure of FRA for each split winding fails because the split windings are adopted to satisfy the impedance requirement of a high-speed railway, where the windings are connected inside the tank. A novel fault interpretation method based on image features and binary tree support vector machine (SVM) is proposed, which can get the condition of three windings in one measurement. Winding faults caused by different windings are simulated, including SC defect, axial deformation, and series capacitance variation, and the FRA curves are measured under various faults. Then, the features of the gray-level gradient co-occurrence matrix and the gray-level difference statistics are got from the polar plot of FRA. Finally, the image features are used as the inputs to the binary tree SVM for fault type and faulty winding classification. The results show that the proposed method has high accuracy for identifying fault type and faulty winding in AT.

Analytical Low-Dose CBCT Reconstruction Using Non-local Total Variation Regularization for Image Guided Radiation Therapy.

Purpose: Conventional iterative low-dose CBCT reconstruction techniques are slow and tend to over-smooth edges through uniform weighting of the image penalty gradient. In this study, we present a non-iterative analytical low-dose CBCT reconstruction technique by restoring the noisy low-dose CBCT projection with the non-local total variation (NLTV) method.Methods: We modeled the low-dose CBCT reconstruction as recovering high quality, high-dose CBCT x-ray projections (100 kVp, 1.6 mAs) from low-dose, noisy CBCT x-ray projections (100 kVp, 0.1 mAs). The restoration of CBCT projections was performed using the NLTV regularization method. In NLTV, the x-ray image is optimized by minimizing an energy function that penalizes gray-level difference between pair of pixels between noisy x-ray projection and denoising x-ray projection. After the noisy projection is restored by NLTV regularization, the standard FDK method was applied to generate the final reconstruction output.Results: Significant noise reduction was achieved comparing to original, noisy inputs while maintaining the image quality comparable to the high-dose CBCT projections. The experimental validations show the proposed NLTV algorithm can robustly restore the noise level of x-ray projection images while significantly improving the overall image quality. The improvement in normalized mean square error (NMSE) and peak signal-to-noise ratio (PSNR) measured from the non-local total variation-gradient projection (NLTV-GPSR) algorithm is noticeable compared to that of uncorrected low-dose CBCT images. Moreover, the difference of CNRs from the gains from the proposed algorithm is noticeable and comparable to high-dose CBCT.Conclusion: The proposed method successfully restores noise degraded, low-dose CBCT projections to high-dose projection quality. Such an outcome is a considerable improvement to the reconstruction result compared to the FDK-based method. In addition, a significant reduction in reconstruction time makes the proposed algorithm more attractive. This demonstrates the potential use of the proposed algorithm for clinical practice in radiotherapy.

X-ray digital detector array radiology to infer sagging depths in welded assemblies

X-ray digital radiology is increasingly used in aerospace industries because of their numerous advantages such as the non-use of consumables and chemicals products, capability to store information via digital media, control time reduction, etc. Nowadays, X-ray digital radiology provides new application perspectives for industrials. In this study, X-ray digital detector array radiology (DDA) was used to infer geometrical welding flaws such as sagging depths by measuring grey level differences. Two representative samples batches of real parts are tested according to different alloys (Nickel and Cobalt-based alloy). Sagging depths of welded samples were measured by X-ray technique, Laser profilometer and micrographic cuts, providing good correlation, in the range of 30–250 μm. These results show the X-ray DDA technique ability to evaluate such welding flaws and provide new perspectives for applications on real parts.

Steganography of Quantum Color Images Based on Blocking and Gray Level Difference

Steganography of Quantum Color Images Based on Blocking and Gray Level Difference

Impact of contouring variability on oncological PET radiomics features in the lung

Radiomics features extracted from oncological PET images are currently under intense scrutiny within the context of risk stratification for a variety of cancers. However, the lack of robustness assessment poses problems for their application across institutions and for broader patient populations. The objective of the current study was to examine the extent to which radiomics parameters from oncological PET vary in response to manual contouring variability in lung cancer. Imaging data employed in the study consisted of 26 PET scans with lesions in the lung being created through the use of an anthropomorphic phantom in conjunction with Monte Carlo simulations. From each of the simulated lesions, 25 radiomics features related to the gray-level co-occurrence matrices (GLCOM), gray-level size zone matrices (GLSZM), and gray-level neighborhood difference matrices (GLNDM) were extracted from ground truth contour and from manual contours provided by 10 raters in regard to four intensity discretization schemes with number of gray levels of 32, 64, 128, and 256, respectively. The impact of interrater variability in tumor delineation upon the agreement between raters on radiomics features was examined via interclass correlation and leave-p-out assessment. Only weak and moderate correlations were found between segmentation accuracy as measured by the Dice coefficient and percent feature error from ground truth for the vast majority of the features being examined. GLNDM-based texture parameters emerged as the top performing category of radiomcs features in terms of robustness against contouring variability for discretization schemes engaging number of gray levels of 32, 64, and 128 while GLCOM-based parameters stood out for discretization scheme engaging 256 gray levels. How and to what extent interrater reliability of radiomics features vary in response to the number of raters were largely feature-dependent. It was concluded that impact of contouring variability on PET-based radiomics features is present to varying degrees and could be experienced as a barrier to convey PET-based radiomics research to clinical relevance.

A Defect Detection Method for the Image With Intersecting Feature

Since the intersecting feature between the defect and the background of the image, the defect detection often results in under-segmentation or over-segmentation. To solve this problem, we propose a new defect extraction method by calculating the maximum mutual information of intersecting features. Firstly, we construct a new two-dimensional histogram according to the defect features. The new histogram is called Gray Level and Local Spatial Difference histogram (GLSD), which is constructed by grayscale and the improved local gray difference with the spatial relationship. Secondly, considering the geometric distribution of high-probability background events, we improve the segmentation shape of the background event distribution and divide the GLSD histogram preliminary. Finally, we calculate the maximum mutual information of the intersecting feature between the defect and the background. At this point, the boundary of the intersecting feature interval of the GLSD histogram is determined. To verify the effectiveness of the proposed method, we used two sets of databases for performance evaluation. The experimental results show that the proposed method is suitable for non-obvious defect detection under the local uniform background. Meanwhile, it can improve the sensitivity, specificity, and accuracy of defect detection compared with the classical threshold segmentation methods.

Application of Migration Image Registration Algorithm Based on Improved SURF in Remote Sensing Image Mosaic

The classical SURF algorithm has many disadvantages, such as high dimension of feature descriptor, large amount of computation, and low matching accuracy when the angle of rotation and angle of view is too large. To solve the above problems, an improved algorithm is proposed. Firstly, image preprocessing is carried out by image binarization, feature points are extracted by Hes-sian matrix, and then feature description is carried out by using circular neighborhood of feature points. Har wavelet response is used to establish descriptors for each feature point, and the normalized gray-level difference and second-order gradient in the neighborhood are calculated simultaneously to form a new feature descriptor. Finally, the RANSAC algorithm is used to eliminate the mismatch points. The algorithm does not Compared with the classical SURF algorithm, it has the advantage of speed, and makes full use of the gray level information and the detail information, so it has higher accuracy. Experimental results show that the algorithm has good robustness and stability to image blur, illumination difference, angle rotation, field of view transformation and so on. The algorithm is applied to remote sensing image stitching to obtain the stitched image with no obvious geometric shift and good edge connection. This algorithm is a kind of image registration algorithm with short time and high precision, which can meet the registration requirements of remote sensing image stitching.

Structure-Adaptive Clutter Suppression for Infrared Small Target Detection: Chain-Growth Filtering

Robust detection of infrared small target is an important and challenging task in many photoelectric detection systems. Using the difference of a specific feature between the target and the background, various detection methods were proposed in recent decades. However, most methods extract the feature in a region with fixed shape, especially in a rectangular region, which causes a problem: when faced with complex-shape clutters, the rectangular region involves the pixels inside and outside the clutters, and the significant grey-level difference among these pixels leads to a relatively large feature in the clutter area, interfering with the target detection. In this paper, we propose a structure-adaptive clutter suppression method, called chain-growth filtering, for robust infrared small target detection. The well-designed filtering model can adjust its shape to fit various clutter structures such as lines, curves and irregular edges, and thus has a more robust clutter suppression capability than the fixed-shape feature extraction strategy. In addition, the proposed method achieves a considerable anti-noise ability by employing guided filter as a preprocessing approach and enjoys the capability of multi-scale target detection without complex parameter tuning. In the experiment, we evaluate the performance of the detection method through 12 typical infrared scenes which contain different types of clutters. Compared with seven state-of-the-art methods, the proposed method shows the superior clutter-suppression effects for various types of clutters and the excellent detection performance for various scenes.

Diagnosis of Cervical cancer using CLAHE and SGLDM on RGB Pap smear Images through ANN

Cervical cancer is one of the most preventable Cancers in the present day. PAP and HPV tests are followed for early diagnosis for screening procedure. In medical field, it is difficult to detect Pap smear images through microscope. Image processing is a vital role to take right decision by using various technique and algorithms. In this paper, the proposed technique is used to enhance the Pap smear images by comparing Histogram Equalization in Contrast Stretching algorithm, Power Law Transformation for Gamma Correction, Shading Correction, Contrast Limited Adaptive Histogram Equalization (CLAHE). The Quality measurement MSE, PSNR, SC and NAC value has been calculated to find performance analysis of enhanced Pap smear images. Then proposed four different feature extraction algorithms SGLDM- Spatial Gray Level Difference Method, RDM- Run Difference Method, LBP- Local Binary Pattern and HOG-Histogram of Oriented Gradients are used to extract features of Pap images. Experimental results are obtained for a data set of 215 Pap images taken from the Management and Decision Engineering Laboratory (MDE-LAB) database. MATLAB R2016a used as a programming tool. ANN Classification is used for each feature extraction algorithm to evaluate the accuracy level. Thus, CLAHE achieved the good result for enhancement and SGLDM feature extraction algorithm reached 93% accuracy using ANN.

Multispectral Image Segmentation Based on a Fuzzy Clustering Algorithm Combined with Tsallis Entropy and a Gaussian Mixture Model

Accurate multispectral image segmentation is essential in remote sensing research. Traditional fuzzy clustering algorithms used to segment multispectral images have several disadvantages, including: (1) they usually only consider the pixels’ grayscale information and ignore the interaction between pixels; and, (2) they are sensitive to noise and outliers. To overcome these constraints, this study proposes a multispectral image segmentation algorithm based on fuzzy clustering combined with the Tsallis entropy and Gaussian mixture model. The algorithm uses the fuzzy Tsallis entropy as regularization item for fuzzy C-means (FCM) and improves dissimilarity measure using the negative logarithm of the Gaussian Mixture Model (GMM). The Hidden Markov Random Field (HMRF) is introduced to define prior probability of neighborhood relationship, which is used as weights of the Gaussian components. The Lagrange multiplier method is used to solve the segmentation model. To evaluate the proposed segmentation algorithm, simulated and real multispectral images were segmented using the proposed algorithm and two other algorithms for comparison (i.e., Tsallis Fuzzy C-means (TFCM), Kullback–Leibler Gaussian Fuzzy C-means (KLG-FCM)). The study found that the modified algorithm can accelerate the convergence speed, reduce the effect of noise and outliers, and accurately segment simulated images with small gray level differences with an overall accuracy of more than 98.2%. Therefore, the algorithm can be used as a feasible and effective alternative in multispectral image segmentation, particularly for those with small color differences.

Edge Detection Using Integrate and Fire Neuron

Edge detection is one of the most basic stages of image processing and have been used in many areas. Its purpose is to determine the pixels formed the objects. Many researchers have aimed to determine objects' edges correctly, like as they are determined by the human eye. In this study, a new edge detection technique based on spiking neural network is proposed. The proposed model has a different receptor structure than the ones found in literature and also does not use gray level values of the pixels in the receptive field directly. Instead, it takes the gray level differences between the pixel in the center of the receptive field and others as input. The model is tested by using BSDS train dataset. Besides, the obtained results are compared with the results calculated by Canny edge detection method.

Structure optimization method of microfluidic paper chip based on image grey-level statistics for chromogenic reaction

Paper-based microfluidic chips are widely used in point-of-care testing (POCT). The structures of the paper chips impose significant impact on the detection accuracy. The mainstream of microfluidic paper chip designs can be divided into two categories, planar diffusion type and vertical diffusion type respectively. According to the two methods above, five different structures of paper chips were designed, namely Y-channel microfluidic chip, three-channel microfluidic chip, rotary array microfluidic chip, oblique double-channel microfluidic chip and multilayer solid microfluidic chip. The mean standard deviation and maximum standard deviation of gray level difference of the best multilayer solid structure comparing with the second best structure reduced by 17% and 39% respectively. Consequently, there was no significant relationship between the color reaction uniformity and the number of channels but a positive correlation with the reaction contact area. This paper offered a step toward designing paper microfluidic chip based on colorimetric method.

Multichannel Local Ternary Co-occurrence Pattern for Content-Based Image Retrieval

In this paper, a novel content descriptor, multichannel local ternary co-occurrence pattern (MCLTCoP) is proposed for image retrieval. Standard local ternary co-occurrence pattern extracts the relationship between center pixel and surrounding pixels by computing gray-level differences in terms of ternary edges. In the modern scenario due to proliferation of color images, it is not sufficient to investigate image appearance variations by analyzing only one type of feature such as color or texture. In order to address this problem, proposed texture patterns are developed to unfold inter-channel variability of R, G and B channels with oppugnant color channel V of the HSV color space. Texture statistics extracted in such a manner not only explore two different color channels but mutual local directional information is also incorporated. This paves a way for improvement in retrieval results. Image retrieval experiments are performed to observe the effectiveness of the proposed feature and compare results with the existing multichannel techniques over benchmark image database, i.e., Corel 1k. Retrieval results show superiority of the proposed approach against state-of-the-art techniques in terms of average precision and average recall.

Pre-treatment 18F-FDG PET-based radiomics predict survival in resected non-small cell lung cancer

To assess the prognostic value of 2-[18F]-fluoro-2-deoxy-d-glucose (FDG) positron-emission tomography (PET)-based radiomics using a machine learning approach in patients with non-small cell lung cancer (NSCLC). Ninety-three patients with stage I-III NSCLC who underwent combined PET/computed tomography (CT) followed by curative resection. A total of 35 unique quantitative radiomic features was extracted from the PET images, which included imaging phenotypes such as pixel intensity, shape, and texture. Radiomic features were ranked based on score according to their correlation with disease recurrence status within a 3-year follow-up. The recurrence risk classification performances of machine learning algorithms (random forest, neural network, naive Bayes, logistic regression, and support vector machine) using the 20 best-ranked features were compared using the areas under the receiver operating characteristic curve (AUC) and validated by the random sampling method. Contrast and busyness texture features from neighbourhood grey-level difference matrix were found to be the two best predictors of disease recurrence. The random forest model obtained the best performance (AUC: 0.956, accuracy: 0.901, F1 score: 0.872, precision: 0.905, recall: 0.842), followed by the neural network model (AUC: 0.871, accuracy: 0.780, F1 score: 0.708, precision: 0.755, recall: 0.666). A PET-based radiomic model was developed and validated for risk classification in NSCLC. The machine learning approach with random forest classifier exhibited good performance in predicting the recurrence risk. Radiomic features may help clinicians to improve the risk stratification for clinical practice.

Carotid Artery Echolucency, Texture Features, and Incident Cardiovascular Disease Events: The MESA Study.

BackgroundWe hypothesized that measures of common carotid artery echolucency and grayscale texture features were associated with cardiovascular disease (CVD) risk factors and could predict CVD events.Methods and ResultsUsing a case‐cohort design, we measured common carotid artery ultrasound images from 1788 participants in Exam 1 of the MESA study (Multi‐Ethnic Study of Atherosclerosis) to derive 4 grayscale features: grayscale median, entropy, gray level difference statistic‐contrast, and spatial gray level dependence matrices‐angular second moment. CVD risk factor associations were determined by linear regression. Cox proportional hazard models with inverse selection probability weighting and adjustments for age, sex, race/ethnicity, CVD risk factors, and C‐reactive protein were used to determine if standardized values for grayscale median, entropy, gray level difference statistic‐contrast, and spatial gray level dependence matrices‐angular second moment could predict incident coronary heart disease, stroke, and total CVD events over a median 13 years follow‐up. Participants were mean (SD) 63.1 (10.3) years of age, 52.6% female, 32.1% white, 27.8% black, 23.3% Hispanic, and 16.8% Chinese. There were 283 coronary heart disease, 120 stroke, and 416 CVD events. Several associations of grayscale features with CVD risk factors were identified. In fully adjusted models, higher gray level difference statistic‐contrast was associated with a lower risk of incident coronary heart disease (hazard ratio 0.82, 95% CI 0.71–0.94, padj=0.005) and CVD events (hazard ratio 0.87, 95% CI 0.77–0.98, padj=0.018); higher spatial gray level dependence matrices‐angular second moment was associated with a higher risk of CVD events (hazard ratio 1.09, 95% CI 1.00–1.19, padj=0.044).ConclusionsGray level difference statistic‐contrast and spatial gray level dependence matrices‐angular second moment predicted CVD events independent of risk factors, indicating their potential use as biomarkers to assess future CVD risk.