Anisotropic Diffusion Technique Research Articles

Feature fusion based VGGFusionNet model to detect COVID-19 patients utilizing computed tomography scan images

COVID-19 is one of the most life-threatening and dangerous diseases caused by the novel Coronavirus, which has already afflicted a larger human community worldwide. This pandemic disease recovery is possible if detected in the early stage. We proposed an automated deep learning approach from Computed Tomography (CT) scan images to detect COVID-19 positive patients by following a four-phase paradigm for COVID-19 detection: preprocess the CT scan images; remove noise from test image by using anisotropic diffusion techniques; make a different segment for the preprocessed images; and train and test COVID-19 detection using Convolutional Neural Network (CNN) models. This study employed well-known pre-trained models, including AlexNet, ResNet50, VGG16 and VGG19 to evaluate experiments. 80% of images are used to train the network in the detection process, while the remaining 20% are used to test it. The result of the experiment evaluation confirmed that the VGG19 pre-trained CNN model achieved better accuracy (98.06%). We used 4861 real-life COVID-19 CT images for experiment purposes, including 3068 positive and 1793 negative images. These images were acquired from a hospital in Sao Paulo, Brazil and two other different data sources. Our proposed method revealed very high accuracy and, therefore, can be used as an assistant to help professionals detect COVID-19 patients accurately.

Quantifying corrosion inhibition on mild steel surface using run length statistics-based texture analysis

Automatic detection of corrosion inhibition efficacy by processing the microscopic images of the sample surface by using image texture analysis is a very useful technique in the contemporary research field. The reported technique is less time consuming with the capability of pattern recognition via quantification with preferences of suitable software, coding and various numerical methods ultimately represents data (vide acquisition) more precisely and accurately at agiven condition. An optimal anisotropic diffusion-based pre-processing for image enhancement and run length statistics (RLS) based texture analysis for feature extraction are performed on the microscopic images of mild steel surface treated in 1 M HCl solution with different inhibitors viz., (E)-1-(2,4-dinitrophenyl)-2-(pyridin-4-yl)methylene)hydrazine (abbreviated as DHMP and represented as L1 ), (E)-1-(4-isocyanophenyl)-2-((pyridin-4-yl)methylene)hydrazine) (abbreviated as IMH and represented as L2 ) and (E)-1-(2,4-dinitrophenyl)-2-((pyridin-4-yl)methylene)phenol (abbreviated as DMP and represented as L3 ). Extracted normalized or relative features can be quantified the corrosion inhibition efficiency in an excellent way using multi-regression analysis having R-square values in the range of 0.997 to 0.999. Highlights (E)-1-(2,4-dinitrophenyl)-2-(pyridin-4-yl)methylene)hydrazine (abbreviated as DHMP and represented as L1), (E)-1-(4-isocyanophenyl)-2-((pyridin-4-yl)methylene)hydrazine) (abbreviated as IMH and represented as L2) and (E)-1-(2,4-dinitrophenyl)-2-((pyridin-4-yl)methylene)phenol (abbreviated as DMP and represented as L3) are used as three different corrosion inhibitors over the mild steel surface in presence of 1M HCl solution. Automatic detection of corrosion inhibition efficiency by processing the microscopic images of the targeted sample surface by via image texture analysis. An optimal anisotropic diffusion technique is adopted in this present study for image enhancement before processing the image. Run length statistics (RLS) based texture analysis has been incorporated in this present investigation for extracting run length features to detect the corrosion inhibition efficiency. After selecting the RLS-based features, a multivariable regression model is developed and correlations are set up with the features of conventional parameters of the different inhibitor’s performance.

A Review on Performance Analysis of PDE based Anisotropic Diffusion Approaches for Image Enhancement

Partial differential equation based anisotropic diffusion techniques are used extensively in computer vision for image enhancement and edge detection. Anisotropic Diffusion which is found to be a low computational complexity approach which has overcome the undesirable effects of linear smoothing filters and now is popular in prominent research areas of enhancing the quality of low contrast images and speckle noise reduction from geological, industrial and medical images. This paper presents a comprehensive survey on various state-of-the-art anisotropic diffusion techniques for image enhancement. The capability of anisotropic diffusion for enhancing the quality of low contrast images and speckle noise reduction from medical images are further explored. Various objective image quality measures are studied which are used to validate the performance of enhancement approaches. The major research issuesand possible future scopes in this diffusion filtering approach are also discussed.

Anisotropic diffusion technique to eliminate speckle noise in continuous-wave Doppler ultrasound spectrogram

Paper described a simplified speckled elimination algorithm for the reconstruction of continuous-wave (CW) Doppler ultrasound spectrogram. Standard Nonlinear Anisotropic Diffusion (SNAD) technique fails to eliminate the speckle noise, blurs the interregional area of neighbouring speckle clusters and regulates the heat flow to preserve the systolic and diastolic peaks in the spectrogram. To resolve this problem, author modelled a kernel, conductance and diffusion function to regulate the blurring of speckle clusters and sharpen the high-contrast edges over low-contrast ones. Paper analysed the performance based on the variation of conductance (C), signal-to-noise ratio (SNR), peak signal-to-noise ratio (PSNR), mean square error (MSE), root mean square error (RMSE) and structural similarity index (SSIM) of each pairs of speckled and despeckled spectrograms. Quantitative analysis reveals that the minimised version of the kernel effectively regulates the flow of heat at edges with a high SNR (8 dB) and PSNR (>20 dB) for a low-frequency range of 20-25 Hz. SSIM (>0.9) shows an efficient structural reconstruction that identifies the lower and higher local SSIM indices as dark and bright pixels on SSIM maps. Hence, the algorithm performs its best at lower peak frequencies in the Doppler spectrograms.

Anisotropic diffusion based on Fermi-Dirac distribution function and its application in the Shack-Hartman wavefront sensor

In this study, the anisotropic diffusion technique is applied to estimate spots in the noise signals of the Shack-Hartmann wavefront sensor. Based on the analysis of the classical anisotropic diffusion function and on an improved algorithm, a diffusion function is proposed based on the Fermi-Dirac distribution. It is proved mathematically that the new function has a higher convergence speed and a better performance. Monte Carlo simulations are used to verify the applicability of the new function subject to the noise limit and signal level. The simulation and experimental results show that the anisotropic diffusion algorithm can effectively filter out the noise. The integrity of the spots can be maintained, and the centroid detection accuracy and signal-to-noise ratio are also improved.

Automatic estimation of mechanical properties from fractographs using optimal anisotropic diffusion and Voronoi tessellation

With the advent of materials informatics there is a high demand of establishing automatic structure-property correlationship of materials which is a popular research topics due to the advancement of image processing and pattern recognition algorithms. Therefore, in this work, an attempt is made to estimate mechanical properties (i.e. yield strength and ductility) of AISI 304LN stainless steel from the SEM images of fracture surfaces obtained from tensile tests carried out at different strain rates using image processing techniques. As the void morphologies of fracture surfaces change systematically with the change in strain rates, the automatic detection of voids and geometrical features estimation from detected voids from the obtained are key goals of this present study. Therefore, in this work, a novel method of optimal anisotropic diffusion technique along with contrast limited adaptive histogram equalization (CLAHE), Otsu's optimal thresholding and morphological thinning operation are applied over the fractographs for edge enhancement, overcoming inhomogeneous illumination, edge segmentation and thinning, respectively, to detect voids, automatically. Then, Voronoi tessellation technique, which is a geometrical texture analysis, is utilized on these edge images of fractographs to extract four features viz. Voronoi edges, mean area, mean elongation and mean perimeter of Voronoi polygons where the linear correlation values (R2) with mechanical properties are found in the range of 0.90–0.99. A high linear correlation of features (i.e. 0.97–0.99) with the ductility is noticed as ductility is a geometrical parameter measured during fracture.

Video despeckling using Shearlet tensor-based anisotropic diffusion

This paper provides an effective method for video speckle noise reduction based on 3D Tensor-based Anisotropic Diffusion technique in the Shearlet domain (V-STAD). The proposed model exploits the multi-scale geometric representation and the sparsity property of the shearlet transform to apply a robust tensorial diffusion at each shearlet coefficients. In fact, the robustness of diffusion tensor image smoothing was not well investigated. Therefore, we adopted the robust Tukey's biweight function in the proposed tensor-based anisotropic diffusion. By this way, the filter benefits from robust statistics and sparse directional image representation property of the shearlet transform in addition to the intrinsic temporal correlations between frames to be adopted to the anisotropic nature of diffusion tensor. The experimental results demonstrate promising despeckling solution as compared to well-known state-of-the-art video denoising methods, like VBM3D and VIDOLSAT. The proposed method has clearly shown superior despeckling capability and it simultaneously demonstrated better local image structures preservation without introducing artifacts.

Ultrasound image segmentation using a novel multi-scale Gaussian kernel fuzzy clustering and multi-scale vector field convolution

Ultrasound imaging is most popular technique used for breast cancer screening. Lesion segmentation is challenging step in characterization of breast ultrasound (US) based Computer Aided Diagnosis (CAD) systems due to presence of speckle noise, shadowing effect etc. The aim of this study is to develop an automatic lesion segmentation technique in breast US with high accuracy even in presence of noises, artifacts and multiple lesions. This article presents a novel clustering method called Multi-scale Gaussian Kernel induced Fuzzy C-means (MsGKFCM) for segmentation of lesions in automatically extracted Region of Interest (ROI) in US to delimit the border of the mass. Further, a hybrid approach using MsGKFCM and Multi-scale Vector Field Convolution (MsVFC) is proposed to obtain an accurate lesion margin in breast US images. Initially, the images are filtered using speckle reducing anisotropic diffusion (SRAD) technique. Subsequently, MsGKFCM is applied on filtered images to segment the mass and detect an appropriate cluster center. The detected cluster center is further used by MsVFC to determine the accurate lesion margin. The proposed technique is evaluated on 127 US images using measures such as Jaccard Index, Dice similarity, Shape similarity, Hausdroff difference, Area difference, Accuracy, F-measure and analysis of variance (ANOVA) test. The empirical results suggest that the proposed approach can be used as an expert system to assist medical professionals by providing objective evidences in breast lesion detection. Results obtained are so far looking promising and effective in comparison to state-of-the-art algorithms.

Speckle removal and change preservation by distance-driven anisotropic diffusion of synthetic aperture radar temporal stacks

We propose a method that smoothes spatially and uses, but also preserves, temporal information of synthetic aperture radar image stacks. To provide smoothing of such imagery without effacing temporal changes in the scene, we put forth an anisotropic diffusion technique, using a PDE approach. This approach smoothes uniform areas and preserves and enhances edges, such as roads or other features. In order to use this anisotropic diffusion technique, a homogeneous region must first be selected in the image to calculate statistics of the speckle noise. We propose a method that uses the temporal information to automatically select a homogeneous region prior to smoothing. Our proposed smoothing method calculates the distance between either pixel time-series or pixel CDFs as a measure of similarity, or uniformity. Results demonstrate the efficacy of the approach on real and synthetic data, showing lower mean squared ratio and higher structural similarity index than leading methods, as well as temporal change preservation.

Multimodality registration of two-dimensional echocardiography and cardiac CT for mitral valve diagnosis and surgical planning.

A registration method to fuse two-dimensional (2-D) echocardiography images with cardiac computed tomography (CT) volume is presented. The method consists of two major procedures: temporal and spatial registrations. In temporal registration, the echocardiography frames at similar cardiac phases as the CT volume were interpolated based on electrocardiogram signal information, and the noise of the echocardiography image was reduced using the speckle reducing anisotropic diffusion technique. For spatial registration, an intensity-based normalized mutual information method was applied with a pattern search optimization algorithm to produce an interpolated cardiac CT image. The proposed registration framework does not require optical tracking information. Dice coefficient and Hausdorff distance for the left atrium assessments were [Formula: see text] and [Formula: see text], respectively; for left ventricle, they were [Formula: see text] and [Formula: see text], respectively. There was no significant difference in the mitral valve annulus diameter measurement between the manually and automatically registered CT images. The transformation parameters showed small deviations ([Formula: see text] deviation in translation and [Formula: see text] for rotation) between manual and automatic registrations. The proposed method aids the physician in diagnosing mitral valve disease as well as provides surgical guidance during the treatment procedure.

On the influence of anisotropic diffusion filter on dorsal hand authentication using eigenveins

In this paper, we present a competent approach for dorsal hand vein features extraction from near infrared images and PCA matching. Which is the anisotropic diffusion filter; we present first a review about this filter most used for the image enhancement. The physiological features characterize the dorsal venous network of the hand. These networks are single to each individual and can be used as a biometric system for person identification/authentication. An active near infrared method is used for image acquisition. The proposed approach uses an anisotropic diffusion technique for contrast enhancement and morphological filtering to extract the venous network and principal component analysis.

RETRACTED ARTICLE: Noise-estimation-based anisotropic diffusion approach for retinal blood vessel segmentation

Recently, numerous research works in retinal-structure analysis have been performed to analyze retinal images for diagnosing and preventing ocular diseases such as diabetic retinopathy, which is the first most common causes of vision loss in the world. In this paper, an algorithm for vessel detection in fundus images is employed. First, a denoising process using the noise-estimation-based anisotropic diffusion technique is applied to restore connected vessel lines in a retinal image and eliminate noisy lines. Next, a multi-scale line-tracking algorithm is implemented to detect all the blood vessels having similar dimensions at a selected scale. An openly available dataset, called “the STARE Project’s dataset,” has been firstly utilized to evaluate the accuracy of the proposed method. Accordingly, our experimental results, performed on the STARE dataset, depict a maximum average accuracy of around 93.88%. Then, an experimental evaluation on another dataset, named DRIVE database, demonstrates a satisfactory performance of the proposed technique, where the maximum average accuracy rate of 93.89% is achieved.

Ultrasound speckle reduction based on fractional order differentiation.

Ultrasound images show a granular pattern of noise known as speckle that diminishes their quality and results in difficulties in diagnosis. To preserve edges and features, this paper proposes a fractional differentiation-based image operator to reduce speckle in ultrasound. An image de-noising model based on fractional partial differential equations with balance relation between k (gradient modulus threshold that controls the conduction) and v (the order of fractional differentiation) was constructed by the effective combination of fractional calculus theory and a partial differential equation, and the numerical algorithm of it was achieved using a fractional differential mask operator. The proposed algorithm has better speckle reduction and structure preservation than the three existing methods [P-M model, the speckle reducing anisotropic diffusion (SRAD) technique, and the detail preserving anisotropic diffusion (DPAD) technique]. And it is significantly faster than bilateral filtering (BF) in producing virtually the same experimental results. Ultrasound phantom testing and in vivo imaging show that the proposed method can improve the quality of an ultrasound image in terms of tissue SNR, CNR, and FOM values.

Fundus image denoising using FPGA hardware architecture

Image processing algorithms, implemented in hardware, have recently emerged as the most viable solution for improving the performance of image processing systems. In this paper, a version of an anisotropic diffusion technique is used to reduce noise from retinal images, namely Speckle Reducing Anisotropic Diffusion SRAD. The SRAD filter can improve images corrupted by multiplicative or additive noise, but it has been the most computationally complex and it has not been suitable for software implementation in real-time processing. In this paper, an efficient Field-Programmable Gate Array FPGA-based implementation of the SRAD filter is presented to accelerate the processing time. A comparison of the most used classical suppression filters like Gaussian, Median, Perona and Malik anisotropic diffusion has been carried out. The experimental results reveal a 38× performance improvement over the original MATLAB implementation and a 1.33× performance improvement over the hardware implementation using the Xilinx System Generator tool.

Bas-Relief Map Using Texture Analysis with Application to Live Enhancement of Ultrasound Images

For ultrasound imaging, speckle is one of the most important factors in the degradation of contrast resolution because it masks meaningful texture and has the potential to interfere with diagnosis. It is expected that researchers would explore appropriate ways to reduce the speckle noise, to find the edges of structures and enhance weak borders between different organs in ultrasound imaging. Inspired by the principle of differential interference contrast microscopy, a “bas-relief map” is proposed that depicts the texture structure of ultrasound images. Based on a bas-relief map, an adaptive bas-relief filter was developed for ultrafast despeckling. Subsequently, an edge map was introduced to enhance the edges of images in real time. The holistic bas-relief map approach has been used experimentally with synthetic phantoms and digital ultrasound B-scan images of liver, kidney and gallbladder. Based on the visual inspection and the performance metrics of the despeckled images, it was found that the bas-relief map approach is capable of effectively reducing the speckle while significantly enhancing contrast and tissue boundaries for ultrasonic images, and its speckle reduction ability is comparable to that of Kuan, Lee and Frost filters. Meanwhile, the proposed technique could preserve more intra-region details compared with the popular speckle reducing anisotropic diffusion technique and more effectively enhance edges. In addition, the adaptive bas-relief filter was much less time consuming than the Kuan, Lee and Frost filter and speckle reducing anisotropic diffusion techniques. The bas-relief map strategy is effective for speckle reduction and live enhancement of ultrasound images, and can provide a valuable tool for clinical diagnosis.

Fast Inpainting Algorithm of Woodblock New Year Pictures Damaged Area Based on Combination of Texture Distribution Analysis and Anisotropic Diffusion Technique

According to the problem of New Year woodblock pictures damaged area in complicated cases and low efficiency of inpainting, fast inpainting algorithm based on combination of texture distribution analysis and anisotropic diffusion technique is proposed. To digital image of New Year woodblock pictures by using this algorithm to repair has similar effects with the traditional algorithm, but the operation time is far lower than traditional methods. Experiments show that this algorithm not only reduce the numerical computational complexity of the model and inpainting effect is good.

Anisotropic Diffusion in ITK

Anisotropic Non-Linear Diffusion is a powerful image processing technique, which allows to simultaneously remove the noise and enhance sharp features in two or three dimensional images. Anisotropic Diffusion is understood here in the sense of Weickert, meaning that diffusion tensors are anisotropic and reflect the local orientation of image features. This is in contrast with the non-linear diffusion filter of Perona and Malik, which only involves scalar diffusion coefficients, in other words isotropic diffusion tensors. In this paper, we present an anisotropic non-linear diffusion technique we implemented in ITK. This technique is based on a recent adaptive scheme making the diffusion stable and requiring limited numerical resources.

Digital Grey Scale Image Enhancement based on Anisotropic Non-Linear Diffusion Technique

era of image processing has influenced every part of the universe right from medical field to space research. Digital image enhancement is the process to improve quality metrics of a noisy image. In this presented paper based on anisotropic non-linear diffusion technique to enhance the input noisy gray scale images, here mainly focused on speckle noise, which aremainly cause by today's modern image processing sensing devices. The value of quality matrices of an enhanced image are PSNR 10 dB and SNR 11 dB. After comparison the values of image parameters with different technique, the presented work not only attained the better result but also itreduce the complexity of digital circuit as its fall under spatial domain category. Finally the paper concluded that anisotropic non- linear diffusion technique is efficient forgrey scale image enhancement, especially for reduction of speckle noise. KeywordsEnhancement, Anisotropic, Diffusion, PSNR etc.

Mammogram Enhancement Using Anisotropic Diffusion and Weighted K-Means Clustering

 Abstract—Mammogram is used to diagnose the breast cancer. However the region in digital mammograms are usually noisy and have low contrast, they might affect the interpretation. This study proposed a combination of an anisotropic diffusion technique and a weighted K-means clustering. The results from the real mammogram presented that the proposed method succeeded to improve the performance of Original Anisotropic Diffusion method to reduce the noise and enhance the image contrast.

A SELF-DUAL FILTERING TOGGLE OPERATOR FOR SPECKLE NOISE FILTERING

In this work, we formalize an alternative way to build self-dual morphological filters that extends some results obtained for morphological centers to a different class of toggle operators. Thus, a wider range of primitives can be considered without causing oscillations, a common problem in toggle mappings. We also show that the combination of the morphological filters generated by using the proposed approach with the well-known anisotropic diffusion technique yields sound results where homogeneous regions are smoothed without degrading edge information. We explore the filtering of speckle noise, an interference pattern that causes a granular aspect in the image, thus limiting its interpretation and making it difficult further image processing tasks. Experimental tests on both synthetic and real-world images show promising results when compared to some well-known methods related to this type of filtering.