Erosion Operations Research Articles

A new perspective on quantitative assessment of photodynamic therapy mediated hydrogel nanocomposite in wound healing using objective biospeckle and morphological local-gradient

Skin wounding is a serious public health issue, especially when considering factors that accelerate tissue recovery. Consequently, the use of photodynamic therapy (PDT) as an effective wound-healing treatment has attracted more scientific attention. Although assessing the wound healing rate is crucial for appropriate monitoring of the probability of wound healing and evaluating the treatment efficiency, the currently used techniques lack the ability to provide such information. Therefore, this study has two aims, first, it contributes to the development of a new image-guided biospeckle system for quantitative monitoring of skin wound healing rate. Second, it evaluates the potential of using a novel synthesized PDT-mediated polyethylene glycol fabric with methylene blue (PEG-MB) hydrogel nanocomposite in accelerating wound healing. The proposed imaging system initially acquires raw biospeckle images from the wound regions of adult healthy albino mice treated with the synthesized hydrogel nanocomposite. Each raw biospeckle image is then converted into maps of morphological local-gradient matrices implemented from the combination of dilation and erosion operations at different radii up to 25 pixels. Subsequently, their intensity histogram statistics are computed, taking central moments as the feature set. Final characterization is achieved via a linear combination of the biospeckle statistics maintaining as much variance as possible using principal component analysis (PCA). The results confirmed by cytokine concentration measurement and histological investigation demonstrate that the innovative biospeckle image-guided system is ideal for investigating wound healing and suggest the potential of the hydrogel nanocomposite as an active dressing.

An active contour model for image segmentation using morphology and nonlinear Poisson’s equation

Active contour model (ACM) is considered as a feasible tool to handle image segmentation problems via an unsupervised learning approach. This paper compensates the shortcomings of well-known ACMs to introduce erosion and dilation operations in mathematical morphology to fit the intensity differential functions of contours on both sides of the image. A nonlinear Poisson’s equation is developed to deal with the interference of nonuniform noise within the image, and finally build a new model. This paper develops a new image processing approach using morphology and nonlinear Poisson’s equation, which yields a fast and efficient algorithm among known similar image segmentation methods, and has a high level of accuracy in processing image segmentation.

Sub-Pixel Surface Water Mapping for Heterogeneous Areas from Sentinel-2 Images: A Case Study in the Jinshui Basin, China

Mapping high-spatial-resolution surface water bodies in urban and suburban areas is crucial in understanding the spatial distribution of surface water. Although Sentinel-2 images are popular in mapping water bodies, they are impacted by the mixed-pixel problem. Sub-pixel mapping can predict finer-spatial-resolution maps from the input remote sensing image and reduce the mixed-pixel problem to a great extent. This study proposes a sub-pixel surface water mapping method based on morphological dilation and erosion operations and the Markov random field (DE_MRF) to predict a 2 m resolution surface water map for heterogeneous regions from Sentinel-2 imagery. DE_MRF first segments the normalized difference water index image to extract water pixels and then detects the mixed pixels by using combined morphological dilation and erosion operations. For the mixed pixels, DE_MRF considers the intra-pixel spectral variability by extracting multiple water endmembers and multiple land endmembers within a local window to generate the water fraction images through spectral unmixing. DE_MRF was evaluated in the Jinshui Basin, China. The results suggested that DE_MRF generated a lower commission error rate for water pixels compared to the comparison methods. Because DE_MRF considers the intra-class spectral variabilities in the unmixing, it is better in mapping sub-pixel water distribution in heterogeneous regions where different water bodies with distinct spectral reflectance are present.

Gully erosion model of the archaeological site of Eridu Ziggurat in the southern plateau of Iraq using RS-GIS technology

The metamorphic processes represented by the process of erosion of the grooves by rainwater is one of the most important processes that affect the structure of the archaeological hills, including the site of Zaqra Eridu. This worked on the activity of rain erosion operations during the rainy season of the year. The problem of the research is whether rain erosion leaves grooves that distort the facades or remnants of the Eridu ziggurat area, and can the severity and degree of erosion erosion be measured within the site of the study area using GIS technology, and is it possible to provide means of protection and maintenance of the archaeological site from the processes of gully erosion. The spatial analytical approach will be adopted and the mathematical quantitative approach based on the extrapolation and analysis of geographical data will be adopted, as well as the use of the descriptive approach. In order to achieve this goal, a field survey will be postponed to discover the site of the Eridu Ziggurat by observing and observing the grooves that the region was exposed to, preparing a digital spatial database for the region and building The digital elevation model, as well as building a gully erosion model, its degree and intensity for the location of the study area. GIS technology, in addition to providing a practical study and a spatial database, the results of which benefit from the General Authority for Antiquities of Dhi Qar Governorate. The study shows that the ziggurat of Arduwa is located within a range of a complex drainage network that reaches the sixth rank, and it is also guaranteed that it penetrates with a network of high density (340-580 m / km 2) according to the classification of the density of the river beds, and therefore it suffered from being cut by grooves that showed its buried features under the surface of the earth on one side Its outer walls were smashed on the other hand, that is, it is within the range of high erosion within the category (1501-2700) according to the degrees of erosion of Berkisma. This study seeks to preserve archaeological sites by following planning standards for their maintenance and restoration and exploitation of the attractions of archaeological tourism.

Region of Interest Extraction using K-Means and Edge Detection for DEXA Images

Region of interest in the world of medical images, there is a region in each image that containsunique features that distinguish each image from the other or distinguish one group of the image from anothergroup. This paper proposes a new method for extracting the region of interest for DEXA images via the K-meansand edge detection. firstly, the noise is reduced by the mean filter then segment the image into two clusters by kmeans, followed by edge detection to identify object boundaries and erosion operation to clarify boundaries and getthe correct coordinates of ROI. The results show that the accuracy of the proposed system is 99%. where 174images cropped correctly out of 176. the dataset used in this work is 'Osteoporosis DEXA Scans Images of Spinefrom Pakistan'.

Refined edge detection with cascaded and high-resolution convolutional network

Edge detection is represented as one of the most challenging tasks in computer vision, due to the complexity of detecting the edges or boundaries in real-world images that contains objects of different types and scales like trees, building as well as various backgrounds. Edge detection is represented also as a key task for many computer vision applications. Using a set of backbones as well as attention modules, deep-learning-based methods improved the detection of edges compared with traditional methods like Sobel or Canny. However, images of complex scenes still represent a challenge for these methods. Also, the detected edges using the existing approaches suffer from non-refined results with erroneous edges. In this paper, we attempted to overcome these challenges for refined edge detection using a cascaded and high-resolution network named (CHRNet). By maintaining the high resolution of edges during the training process, and conserving the resolution of the edge image during the network stage, sub-blocks are connected at every stage with the output of the previous layer. Also, after each layer, we use batch normalization layer with an active affine parameter as an erosion operation for the homogeneous region in the image. The proposed method is evaluated using the most challenging datasets including BSDS500, NYUD, and Multicue. The obtained results outperform the designed edge detection networks in terms of performance metrics and quality of output images.The code is available at: https://github.com/elharroussomar/chrnet/

Effects of the Pore Morphology on Multiphase Fluid Displacement in Porous Media-A High-Resolution Modeling Investigation.

Advances in computational and image acquisition capabilities have made direct simulation of multiphase fluid flow through porous media possible. An example is application of volume of fluid modeling on images produced using the X-ray computed micro-tomography technique. Analysis of such high-resolution (both temporal and spatial) data sets provides new insights into pore-scale dynamics of previously less well-known processes. We present the outcomes of a high-resolution direct-simulation two-phase fluid displacement study performed on a series of five two-dimensional images of sandy porous media produced using erosion and dilation algorithms. This has enabled us to study the pore-scale dynamics systematically in models that are similar in connectivity but different in the morphology (pore sizes and aspect ratio). Our results show that the drainage and imbibition processes result in very distinct fluid displacement patterns in these models at the pore scale. As a result of drainage, the more open (eroded grains) models accommodate large oil clusters, while the tighter (dilated grains) models trap smaller oil clusters. The imbibition process is dominated by oil trapping in two ways: (i) bypassing larger oil clusters in the eroded models and (ii) local trapping of smaller clusters in the dilated models. This behavior is shown to arise from the relatively larger average aspect ratios of the dilated models compared to those of the eroded models. This promotes snap-off at pore throats (in competition with piston-like displacement), resulting in local trapping of the non-wetting phase. Both of these pore-scale trapping regimes seen here allow trapping of oil in as much as 50% of the pore space. Through the use of erosion and dilation operations, we show that a power-law relationship exists between the average pore size and the average grain size for these sandy media. This relationship is useful in designing engineered porous materials where pore sizes need to be estimated based on grain sizes.

Real-time classification for Φ-OTDR vibration events in the case of small sample size datasets

Efficient classification of vibration signals detected by phase-sensitive optical time domain reflectometer (Φ-OTDR) based on small samples is an effective method to reduce the false alarm rate without GPU or large data sets. This paper proposes a fiber optic system vibration event recognition method based on a combination of image segmentation pre-processing, texture, statistical, morphological feature extraction, and weighted support vector machine (WSVM), which can effectively classify-five types of vibration events in high-speed railway perimeter intrusion detection with small sample data and no parallel processing units. Erosion and dilation operations are applied to vibration signal image feature enhancement in image pre-processing. The vibration signal region and background are separated by the maximum inter-class variance method, then 35 features of the vibration signal region are calculated and finally employed to construct a WSVM. Experiments show that the method achieves 99 FPS and 98.8% accuracy on the test set with 330 vibration images as the training set to build the model without GPU and in the presence of interference signals. It provides a generalized Φ-OTDR vibration event recognition method for small samples.

Foreground-Back ground Segmentation using K-Means Clustering Algorithm and Support Vector Machine

Foreground-background image segmentation has been an important research problem. It is one of the main tasks in the field of computer vision whose purpose is detecting variations in image sequences. It provides candidate objects for further attentional selection, e.g., in video surveillance. In this paper, we introduce an automatic and efficient Foreground-background segmentation. The proposed method starts with the detection of visually salient image regions with a saliency map that uses Fourier transform and a Gaussian filter. Then, each point in the maps classifies as salient or non-salient using a binary threshold. Next, a hole filling operator is applied for filling holes in the achieved image, and the area-opening method is used for removing small objects from the image. For better separation of the foreground and background, dilation and erosion operators are also used. Erosion and dilation operators are applied for shrinking and expanding the achieved region. Afterward, the foreground and background samples are achieved. Because the number of these data is large, K-means clustering is used as a sampling technique to restrict computational efforts in the region of interest. K cluster centers for each region are set for training of Support Vector Machine (SVM). SVM, as a powerful binary classifier, is used to segment the interest area from the background. The proposed method is applied on a benchmark dataset consisting of 1000 images and experimental results demonstrate the supremacy of the proposed method to some other foreground-background segmentation methods in terms of ER, VI, GCE, and PRI.

Improved one-dimensional dilation-based top-hat algorithm for star segmentation under complicated background conditions.

The white top-hat transformation has been widely used in small bright target extraction. It usually applies an erosion operation to remove the target and then a dilation operation to recover the intensity of the processed image. A bright target will be extracted by subtracting the opening operation (erosion followed by dilation) from the raw image. The drawback of this method is that its denoising ability is poor because the estimated background threshold by an opening operation is smaller than the raw image. This study puts forward the viewpoint that by use of a proposed one-dimensional (1D) symmetrical line-shaped structuring element a bright target can also be removed by the dilation operation. Consequently, the white top-hat transformation can be implemented by subtracting only the dilation operation from the raw image. To the best knowledge of the authors, it is the first time to use this method to achieve the top-hat transformation. The simulation experiment shows that the proposed 1D top-hat algorithm has excellent performance in denoising ability and detection ability. Moreover, real night experiments demonstrate that our proposed algorithm can work reliably under both complicated background conditions and good weather conditions. It is noticeable that the performance of computational efficiency and resource consumption have been considerably improved because a 1D structuring element is employed and the erosion operation is not included.

Adaptive neuro-fuzzy interface system based performance monitoring technique for hydropower plants

ABSTRACT Energy has played a significant role in developing civilization, but the continuous use of fossil fuels has hampered the environment. Hydropower is the alternative to fossil fuels. But most of the hydropower plants in hilly areas suffer from silt erosion problems. Erosion of underwater parts creates vibration and noise and reduces machine efficiency. Therefore, online monitoring of turbines and other equipment is necessary to minimize losses due to erosion and part-load operation. Various studies are reported in the literature and found that correlation-based machine efficiency monitoring is one of the popular techniques. ANN method is useful for system modeling with a wide range of applications. However, despite the excellent classification capacities, its development can be time-consuming, computer-intensive, and prone to overfitting. In this paper, an Adaptive Neuro-Fuzzy Interface System (ANFIS) has been utilized to develop a correlation that removes the drawbacks of ANN and can predict the efficiency of the machine with an R2-value of 0. 99,976 having a Mean Absolute Percentage Error (MAPE) of 0.0108% at 0.06482% Root Mean Square Percentage Error (RMSPE).

Automatic method for individual parcellation of manganese-enhanced magnetic resonance imaging of rat brain.

AimsTo construct an automatic method for individual parcellation of manganese-enhanced magnetic resonance imaging (MEMRI) of rat brain with high accuracy, which could preserve the inherent voxel intensity and Regions of interest (ROI) morphological characteristics simultaneously.Methods and resultsThe transformation relationship from standardized space to individual space was obtained by firstly normalizing individual image to the Paxinos space and then inversely transformed. On the other hand, all the regions defined in the atlas image were separated and resaved as binary mask images. Then, transforming the mask images into individual space via the inverse transformations and reslicing using the 4th B-spline interpolation algorithm. The boundary of these transformed regions was further refined by image erosion and expansion operator, and finally combined together to generate the individual parcellations. Moreover, two groups of MEMRI images were used for evaluation. We found that the individual parcellations were satisfied, and the inherent image intensity was preserved. The statistical significance of case-control comparisons was further optimized.ConclusionsWe have constructed a new automatic method for individual parcellation of rat brain MEMRI images, which could preserve the inherent voxel intensity and further be beneficial in case-control statistical analyses. This method could also be extended to other imaging modalities, even other experiments species. It would facilitate the accuracy and significance of ROI-based imaging analyses.

Skeletonization and impermeability characterization of discontinuity interface for reservoir with sandstone compartment

In the process of fluvial sedimentary evolution, mudstone interlayer is frequently developed in the interval of sandstone deposition, forming a reservoir of sandstone compartment. Some generally nonpermeable or semipermeable interlayers become discontinuity interfaces of a reservoir, which is one of the causes for the complexity in the distribution of remaining oil. The degree of the impermeability of the discontinuity interface on fluid flow is critical to the development of hydrocarbons. The discontinuity interface, a small-scale reservoir discontinuity comparing with the fault, is generally below the seismic vertical resolution ([Formula: see text]). The key to the study of the discontinuity interface is to characterize the variation of plane characteristics of sediments between isochronous seismic horizons. Therefore, we can extract seismic attributes between isochronous seismic horizons to analyze the subtle changes of seismic data, which reflect the distribution of the discontinuity interfaces. In the experiment of gradient vector to detect discontinuity interface in seismic attribute, the authors find that the width of the discontinuity interface is caused by the “lens” shape of the sandstone itself. This phenomenon is an indicator of the impermeability of the discontinuity interface. Here, we have developed a two-step method. First, we use the erosion operator of mathematical morphology to locate the skeleton of the discontinuity interface with the steering of the gradient direction. Second, we measured the width of the discontinuity interface to indicate its impermeability. The application to a field data set demonstrates that the results of this proposed method are consistent with the production data of the wells. The proposed method characterizes the reservoir heterogeneity refined from sandstone sets to a single sandstone bed, which improves the resolution of seismic geologic interpretation.

Performance analysis of wind turbines with leading-edge erosion and erosion-safe mode operation

For offshore wind turbines, Leading-Edge Erosion (LEE) due to rain is posing a serious risk to structural integrity and can lead to a performance loss of the order of a few percent of the Annual Energy Production (AEP). A proposed mitigation strategy is the so-called Erosion-Safe Mode (ESM). In this work, the AEP losses caused by LEE or by operating in the ESM are compared for two reference turbines, i.e. the IEA 15MW and the NREL 5MW turbines. For both turbines, the performance is evaluated in uniform and sheared inflow conditions. The effects of erosion are modeled by creating clean and rough airfoil polars in XFOIL. It is assumed that erosion occurs once a critical blade element section speed is exceeded. Power curves for LEE and ESM are calculated by using the free-wake vortex method CACTUS. Results show that LEE negatively affects the power production below rated capacity, while operating in ESM predominantly sheds performance at rated power of the turbine. This study, therefore, shows that a break-even point for the ESM exists. The AEP loss due to erosion can be successfully mitigated with the ESM at sites with low mean wind speed, however, at sites with higher mean wind speed, operation with erosion leads to a lower AEP loss. The break-even point shows little sensitivity to the blade design and to mean shear variations, but strongly depends on the frequency ESM needs to be applied. The latter is driven by the predicted amount of damaging rain events. In conclusion, erosion-optimal operation is strongly governed by the site characteristics and much less by turbine design, and the viability of an ESM strategy can be significantly expanded by a better understanding of blade damage mechanisms and improved forecasting of the related weather events.

A Dilated Segmentation Network with the Morphological Correction Method in Farming Area Image Series

Farming areas are made up of diverse land use types, such as arable lands, grasslands, woodlands, water bodies, and other surrounding agricultural architectures. They possess imperative economic value, and are considerably valued in terms of farmers’ livelihoods and society’s flourishment. Meanwhile, detecting crops in farming areas, such as wheat and corn, allows for more direct monitoring of farming area production and is significant for practical production and management. However, existing image segmentation methods are relatively homogeneous, with insufficient ability to segment multiple objects around the agricultural environment and small-scale objects such as corn and wheat. Motivated by these issues, this paper proposed a global-transformer segmentation network based on the morphological correction method. In addition, we applied the dilated convolution technique to the backbone of the model and the transformer technique to the branches. This innovation of integrating the above-mentioned techniques has an active impact on the segmentation of small-scale objects. Subsequently, the backbone improved by this method was applied to an object detection network based on a corn and wheat ears dataset. Experimental results reveal that our model can effectively detect wheat ears in a complicated environment. For two particular segmentation objects in farming areas, namely water bodies and roads, we notably proposed a morphological correction method, which effectively reduces the number of connected domains in the segmentation results with different parameters of dilation and erosion operations. The segmentation results of water bodies and roads were thereby improved. The proposed method achieved 0.903 and 13 for mIoU and continuity. This result reveals a remarkable improvement compared with the comparison model, and the continuity has risen by 408%. These comparative results demonstrate that the proposed method is eminent and robust enough to provide preliminary preparations and viable strategies for managing farming area resources and detecting crops.

A Morphological Image Segmentation Algorithm for Circular Overlapping Cells

Cell segmentation is an important topic in medicine. A cell image segmentation algorithm based on morphology is proposed. First, some morphological operations, including top-hat transformation, bot-hat transformation, erosion operation, dilation operation, opening operation, closing operation, majority operation, skeleton operation, etc., are applied to remove noise or enhance cell images. Then the small blocks in the cell image are deleted as noise, the medium blocks are removed and saved as normal cells, and the large blocks are segmented as overlapping cells. Each point on the edge of the overlapping cell area to be divided is careful checked. If the shape of the surrounding area is a corner and its angle is smaller than the specified value, the overlapping cell will be divided along the midline of the corner. The length of each division is about a quarter of the diameter of a normal cell. Then small blocks are deleted, and medium blocks are removed and saved, after the edges of all blocks are smoothed. This step is repeated until no dividing point is found. The last remaining image, plus the saved blocks, is the final segmentation result of the cell image. The experimental results show that this algorithm has high segmentation accuracy for lightly or moderately overlapping cells.

Perceptually Unimportant Information Reduction and Cosine Similarity-Based Quality Assessment of 3D-Synthesized Images.

Quality assessment of 3D-synthesized images has traditionally been based on detecting specific categories of distortions such as stretching, black-holes, blurring, etc. However, such approaches have limitations in accurately detecting distortions entirely in 3D synthesized images affecting their performance. This work proposes an algorithm to efficiently detect the distortions and subsequently evaluate the perceptual quality of 3D synthesized images. The process of generation of 3D synthesized images produces a few pixel shift between reference and 3D synthesized image, and hence they are not properly aligned with each other. To address this, we propose using morphological operation (opening) in the residual image to reduce perceptually unimportant information between the reference and the distorted 3D synthesized image. The residual image suppresses the perceptually unimportant information and highlights the geometric distortions which significantly affect the overall quality of 3D synthesized images. We utilized the information present in the residual image to quantify the perceptual quality measure and named this algorithm as Perceptually Unimportant Information Reduction (PU-IR) algorithm. At the same time, the residual image cannot capture the minor structural and geometric distortions due to the usage of erosion operation. To address this, we extract the perceptually important deep features from the pre-trained VGG-16 architectures on the Laplacian pyramid. The distortions in 3D synthesized images are present in patches, and the human visual system perceives even the small levels of these distortions. With this view, to compare these deep features between reference and distorted image, we propose using cosine similarity and named this algorithm as Deep Features extraction and comparison using Cosine Similarity (DF-CS) algorithm. The cosine similarity is based upon their similarity rather than computing the magnitude of the difference of deep features. Finally, the pooling is done to obtain the objective quality scores using simple multiplication to both PU-IR and DF-CS algorithms. Our source code is available online: https://github.com/sadbhawnathakur/3D-Image-Quality-Assessment.

Analysis the digital images by using morphology operators

In this paper, we deal with morphology images that try to improve the use of images. On the one hand, the process is used to obtain the histogram of the image then converted it into a non-color image (gray scale). The next step is to perform the erosion, dilation, open and close operations on the images, how these methods have important effects, and how can be used on a variable number of images, and found the differences between them. These operations were applied on four different images, check images, four basic operations (dilation, erosion, open and close) for each image were performed. Then, retrieving process to the original state of the image (the colored copy) was applied. The results found that retrieving the original images is difficult, and there is the occurrence of some noises on the image when it was retrieved. Finally, conclusions of the work are presented.

Image enhancement methods on extracted texture features to detect prostate cancer by employing machine learning techniques

Prostate cancer (PCa) is the second most diagnosed cancer of men all over the world. The aim of this research was to improve PCa detection based on image enhancement methods including image adjustment and morphological erosion operations and then compute the texture features. We then employed robust Machine Learning (ML) techniques such as the Naïve Bayes, Support Vector Machine (SVM) kernels: Polynomial, Radial Base Function (RBF), Gaussian and Decision Tree (DT) based on extracted texture features. The Cross validation (Jack-Knife k-Fold) was performed, and performance was evaluated in term of Receiver Operating Curve (ROC), Specificity, Sensitivity, Positive Predictive Value (PPV), Negative Predictive Value (NPV), False Positive Rate (FPR). The highest detection performance based on morphological erosion operation on texture features was obtained using SVM polynomial with sensitivity (99.82%), specificity (96.63%), accuracy (98.59%) and AUC (0.9994). The image adjustment methods yielded the highest detection performance with sensitivity, specificity, and accuracy of 100% and AUC of 1.00 using ML SVM selected kernels. The results reveal that proposed image enhancement methods have the potential to accurately predict PCa. Thus, this approach can be better utilized by clinicians for early prediction of PCa for further diagnostic and treatment of the patients.

An active contour model based on local pre-piecewise fitting image

Active contour model (ACM) has been widely used in the field of image segmentation and successfully solved numerous problems. Segmentation of intensity inhomogeneity images is a major focus of current research. To seek a better solution, this paper proposes an active contour model based on local pre-piecewise fitting image. First of all, the piecewise function is pre-fitted by the dilation and erosion operator in gray morphology. Second, a novel energy function is drawn out with a rewritten image fitting function. Third, an optimized gradient descent flow formula is proposed to further improve the robustness of this model. Fourth, an improved regularization term is proposed to optimize the level set function. Experiments have proved that the model can effectively segment images with uneven grayscale. Compared with other models, the proposed model has obvious advantages in robustness to initialization and segmentation efficiency. In addition, the model can also choose various targets by selecting different initial contours.