Simplified Image Analysis Research Articles

Segmentand Classify Satellte Images to Analyze The Environment and Ensure Safety, Using Machine Learning

Image segmentation is a difficult task in computer vision. The process includes the classification of visual input to segments to simplify image analysis. There are many types of method for the image segmentation some of the common methods are edge detection based method region-based methods, clustering-based method, partial differential equation-based, watershed-based method, and neural network-based method. The proposed project work is mainly focused on image segmentation. Satellite images are given as the input of the proposed system. Machine learning techniques plan an important role in various domains. Here the remotely sensed data can be segmented by using the K-Means clustering method. Compared with other traditional methods this clustering technique yields better results. The system is implemented using the Matlab tool. Machine learning concepts drastically decrease the time needed to arrange an exact map. the project will be using K Nearest Neighbor (KNN) as existing and Support Vector Machine (SVM) as proposed system for classification and calculates results in terms of accuracy. From the results obtained its proved that proposed SVM works better than existing KNN.

Simple analysis of gel images with IOCBIO Gel

BackgroundCurrent solutions for the analysis of Western Blot images lack either transparency and reproducibility or can be tedious to use if one has to ensure the reproducibility of the analysis.ResultsHere, we present an open-source gel image analysis program, IOCBIO Gel. It is designed to simplify image analysis and link the analysis results with the metadata describing the measurements. The software runs on all major desktop operating systems. It allows one to use it in either a single-researcher environment with local storage of the data or in a multiple-researcher environment using a central database to facilitate data sharing within the research team and beyond. By recording the original image and all operations performed on it, such as image cropping, subtraction of background, sample lane selection, and integration boundaries, the software ensures the reproducibility of the analysis and simplifies making corrections at any stage of the research. The analysis results are available either through direct access to the database used to store it or through the export of the relevant data.ConclusionsThe software is not only limited to Western Blot image analysis and can be used to analyze images obtained as a part of many other widely used biochemical techniques such as isoelectric focusing. By recording the original data and all the analysis steps, the program improves reproducibility in the analysis and contributes to the implementation of FAIR principles in the related fields.

Satellite Image Segmenation and Classification For Environmental Analysis

Abstract: Image segmentation is a difficult task in computer vision. This process includes the classification of visual input to segments to simplify image analysis. There are many types of method for the image segmentation some of the common methods are edge detection-based method region-based methods, clustering-based method, partial differential equation-based, watershedbased method, and neural network-based method. This research work is mainly focused on image segmentation. Satellite images are given as the input of the proposed system. Machine learning techniques plan an important role in various domains. Here the remotely sensed data can be segmented by using the K-Means clustering method. Compared with other traditional methods this clustering technique yields better results. This system can be implemented by using the MATLAB software tool. Machine learning concepts drastically decrease the time needed to arrange an exact map.

Porosity Analytical Tool Based on Infrared (IR) Images of Concrete

Images analysis techniques are gaining popularity in the studies of civil engineering technology. However, the current image analysis methods often require advanced machinery and strict image acquisition procedures, which may be challenging in actual construction practices. Therefore, this study develops a simplified image analysis technique that uses the FLIR ONE infrared camera to capture the concrete surface compatible with the Infrared (IR) concrete images. The infrared image is then converted into contour plot images using MATLAB software and analysed using AutoCAD to determine the thermal colour's diameter on the infrared images. This study focuses on the white and yellow colours. The yellow thermal colour is the outer of the Thermal Diameter Reduction (TDR) rate of concrete, while the white thermal colour is the inner due to the highest temperature. However, to determine the accuracy of the MATLAB image processing, additional image editing is performed by utilising image exposure editing in conjunction with AutoCAD. The result from MATLAB was then gathered and compared to the image exposure editing to find the correlation between porosity, compressive strength and TDR rate of concrete. Experimental results showed that porosity affected the TDR rate with reliable accuracy of 90.00 %, compressive strength 70.00 %, and concrete density 60.00 %. The result analysis shows that concrete with high characteristic strength is in a less porous state, which means it is denser and transfers heat slowly to the surface, thus making the TDR rate value of the concrete minimum.

Analysis of Sanda action based on CCNN model of IBS distance measurement

IBS is a similarity measurement method of symbol sequences based on order statistics, and CCNN is a method based on CNN feature extraction and IBS distance measurement, which can construct crossover probability graph through image crossover comparison. IBS distance and Noribs distance are used to define the similarity between crossover probability graphs. For Sanda movement, it is difficult to carry out big data analysis with a small number of samples and the movement style of athletes will change greatly. In this paper, a CCNN model based on IBS distance measurement is proposed to analyze the action of Sanda. Firstly, the motion pictures obtained were de-noised and then simplified into a sequence diagram of human skeleton nodes by Delaunay triangulation. Then, 80% of the samples were input into the CCNN network for training and 20% for algorithm verification. The experiment found that in the case of a small number of samples, the accuracy of the simplified image analysis was 10% higher than that of the unsimplified image. In addition, compared with the accuracy of K-distance classification, Bayesian network classification and decision tree classification, it is found that the model can improve the accuracy by 2%-11.75% due to the small number of samples, and has higher accuracy and applicability.

Image Analysis of Surface Porosity Mortar Containing Processed Spent Bleaching Earth.

Image analysis techniques are gaining popularity in the studies of civil engineering materials. However, the current established image analysis methods often require advanced machinery and strict image acquisition procedures which may be challenging in actual construction practices. In this study, we develop a simplified image analysis technique that uses images with only a digital camera and does not have a strict image acquisition regime. Mortar with 10%, 20%, 30%, and 40% pozzolanic material as cement replacement are prepared for the study. The properties of mortar are evaluated with flow table test, compressive strength test, water absorption test, and surface porosity based on the proposed image analysis technique. The experimental results show that mortar specimens with 20% processed spent bleaching earth (PSBE) achieve the highest 28-day compressive strength and lowest water absorption. The quantified image analysis results show accurate representation of mortar quality with 20% PSBE mortar having the lowest porosity. The regression analysis found strong correlations between all experimental data and the compressive strength. Hence, the developed technique is verified to be feasible as supplementary mortar properties for the study of mortar with pozzolanic material.

Assessing the impact of water infiltration on LNAPL mobilization in sand column using simplified image analysis method

Laboratory-scale column experiments were carried out to assess the influence of water infiltration on pooled light non-aqueous phase liquid (LNAPL) redistribution in porous media. A simplified image analysis method (SIAM) was used to evaluate the saturation distributions of the LNAPL and water in the entire domain under dynamic conditions. The experiments were conducted for high/low LNAPL volumes LNAPL volumes differentiated as low and high volumes. High resolution SIAM images of the soil column during LNAPL migration and water infiltration events were captured and analyzed. Results indicated that the capillary fringe is about 6–7 cm which was consistent with the capillary height derived from empirical equations. Moreover, SIAM provided an estimate of the field capacity (30%) of the sand. Once the LNAPL infiltration stage was started, the LNAPL was observed to rapidly migrate through the vadose zone. For the case of large LNAPL volume, the LNAPL penetrated further into capillary fringe zone. Analysis of SIAM images showed that the LNAPL redistribution was observed to vary significantly with the rate of infiltration. For higher water infiltration intensity, the injected water exerted a larger hydrodynamic force on the entrapped LNAPL forcing it move further downward into the capillary zone and the saturated zone. Overall, this study demonstrated that the SIAM technique is an accurate and cost-effective tool for the visualization of the time-dependent NAPL/water movement in laboratory-scale experiments and dynamic changes in fluid saturation in porous media.

A Survey on Image Segmentation Using Deep Learning

Image segmentation is a critical process in computer vision. It involves dividing a visible input into segments to simplify image analysis. Segments represent objects or parts of objects and comprise sets of super-pixels. Image segmentation sorts pixels into larger components, eliminating the need to believe individual pixels as units of observation. Brain tumour segmentation is a crucial task in medical image segmentation. Early diagnosis of brain tumours plays a crucial role in improving treatment possibilities and increases the survival rate of the patients. Manual segmentation of the brain tumours for cancer diagnosis, from great deal of MRI images generated in clinical routine, may be a difficult and time-consuming task. There is a requirement for automatic brain tumour image segmentation. The method is proposed to segment normal tissues like substantial alba, grey matter, spinal fluid, and abnormal tissue like tumour part from a resonance Imaging (MRI) automatically. The system also uses to segment the tumour cells along the morphological filtering are going to be wont to remove background noises for smoothening of region. The project results will be presented as segmented tissues and classification using, Convolutional Neural Network (CNN) classifier.

Capillary Rise Determination Using Simplified Image Analysis Method

Capillary rise underestimation can reduce the long-term performance of civil engineering projects, especially those concerned with ground moisture, such as basements, roads, dams, and soil barrier liners. According to Peck et al. [1], the grain shape constant in empirical capillary rise estimation relationships varies between 10 and 50 mm2 and can be difficult to assume. Furthermore, the measurement of capillary rise in laboratory settings is also quite challenging because it occurs in the transition zone between saturated and unsaturated soils. This study focused on the application of the simplified image analysis method (SIAM) to capillary rise determination for the assessment of the grain shape constant as proposed by Peck et al. [1]. SIAM is a non-intrusive, non-destructive laboratory technique used to measure the temporal and spatial distribution of water saturation in whole domains. In this study, six one-dimensional tests were conducted using a 35 mm × 35 mm × 550 mm column to simulate capillary rise at fixed groundwater levels. The results show that values smaller than 25 mm2 should be used for coarser sand, whereas values greater than 25 mm2 should be used for finer sand. This study demonstrates that SIAM can be further utilized in studies of unsaturated soil, especially to assess soil saturation changes.

LNAPL saturation distribution under the influence of water table fluctuations using simplified image analysis method

Laboratory-scale column experiments were conducted to investigate the influence of water table fluctuation on light non-aqueous phase liquid (LNAPL) migration and redistribution in porous media. A simplified image analysis method (SIAM) was used to evaluate the saturation distributions of the LNAPL and water in the entire domain under dynamic conditions. Two LNAPL volumes differentiated as low and high volumes were exposed to two patterns of water table fluctuations. High-resolution SIAM images of the soil column during LNAPL migration and subsequent water table fluctuations were collected and analyzed. Results show that when the water table was lowered in the experiment of low LNAPL volume by 10 cm (from 28 to 18 cm) and then raised back to its initial location (28 cm), the water saturation dropped from 100% initially to around 30% while the LNAPL saturation increased and remained at 7% to 25% at 36 cm. By doubling the LNAPL volume to 50 ml, the initial LNAPL saturation above the water table increased and the capillary fringe height increased from 36 to 38 cm. Comparison between the two LNAPL volumes showed that the LNAPL covered a larger area for the case of high LNAPL. The LNAPL saturation extended up to around 53 cm while for the case of low LNAPL, the LNAPL extended up to height of 50 cm. Moreover, water saturation below was lesser for the case of high LNAPL. This was due to the increasing volume of LNAPL which caused the water to be pushed downward. However, similar downward migration of water was observed in both sets of experiments. This indicates that doubling the volume of LNAPL while maintaining the same groundwater fluctuation pattern did not affect the general LNAPL behavior. Overall, this study demonstrates that the SIAM technique is an accurate and cost-effective tool for the visualization of the time-dependent NAPL/water movement in laboratory-scale experiments and dynamic changes in fluid saturation in porous media.

Democratized image analytics by visual programming through integration of deep models and small-scale machine learning

Analysis of biomedical images requires computational expertize that are uncommon among biomedical scientists. Deep learning approaches for image analysis provide an opportunity to develop user-friendly tools for exploratory data analysis. Here, we use the visual programming toolbox Orange (http://orange.biolab.si) to simplify image analysis by integrating deep-learning embedding, machine learning procedures, and data visualization. Orange supports the construction of data analysis workflows by assembling components for data preprocessing, visualization, and modeling. We equipped Orange with components that use pre-trained deep convolutional networks to profile images with vectors of features. These vectors are used in image clustering and classification in a framework that enables mining of image sets for both novel and experienced users. We demonstrate the utility of the tool in image analysis of progenitor cells in mouse bone healing, identification of developmental competence in mouse oocytes, subcellular protein localization in yeast, and developmental morphology of social amoebae.

A Novel Approach to Assess Salt Stress Tolerance in Wheat Using Hyperspectral Imaging.

Salinity stress has significant adverse effects on crop productivity and yield. The primary goal of this study was to quantitatively rank salt tolerance in wheat using hyperspectral imaging. Four wheat lines were assayed in a hydroponic system with control and salt treatments (0 and 200 mM NaCl). Hyperspectral images were captured one day after salt application when there were no visual symptoms. Subsequent to necessary preprocessing tasks, two endmembers, each representing one of the treatment, were identified in each image using successive volume maximization. To simplify image analysis and interpretation, similarity of all pixels to the salt endmember was calculated by a technique proposed in this study, referred to as vector-wise similarity measurement. Using this approach allowed high-dimensional hyperspectral images to be reduced to one-dimensional gray-scale images while retaining all relevant information. Two methods were then utilized to analyze the gray-scale images: minimum difference of pair assignments and Bayesian method. The rankings of both methods were similar and consistent with the expected ranking obtained by conventional phenotyping experiments and historical evidence of salt tolerance. This research highlights the application of machine learning in hyperspectral image analysis for phenotyping of plants in a quantitative, interpretable, and non-invasive manner.

Effects of water table fluctuation on diesel fuel migration in one-dimensional laboratory study

The effects of water table fluctuation on diesel fuel redistribution in unsaturated soil are investigated in the laboratory by one-dimensional column test. The simplified image analysis method is used to assess the saturation distributions of water and diesel in the flow domain under transient condition. The experiments are undertaken on two homogeneous sands to study the effects of different particle sizes. The two-phase experimental data illustrate soil–liquid characteristic curves (SLCCs) of water/air and diesel/air systems. The SLCCs of diesel/water systems can be predicted by scaling procedure. A descending order of matric suction at a given saturation of wetting phase is in the order of air/water, diesel/water and air/diesel systems. Coarser particle size gives smaller entry pressure, residual matric suction and residual degree of saturation. The three-phase (air/water/diesel) experimental data illustrate that, during imbibition stages, air and diesel are entrapped below water table with the entrapped air saturations of 18–24% and entrapped diesel saturations of 6–10% for fine sand. Coarser particle size yields smaller entrapped diesel saturations and larger entrapped air saturations. The residual diesel saturations during drainage stages and entrapped diesel saturations during imbibition stages do not show any systematic difference as these stages are repeated. During imbibition stages, 37–47% of the total amount of diesel fuel is entrapped under the water table. Also, there is no systematic difference in the amount of diesel fuel entrapped under the water table when imbibition stages are repeated.

Migration of different LNAPLs in subsurface under groundwater fluctuating conditions by the simplified image analysis method

The correct understanding of the dynamic behavior of Light Non-Aqueous Phase Liquids (LNAPLs) under fluctuating groundwater conditions, difficult to test with conventional methods, is important for the adequate remediation of contaminated soils. In this study, we verified the suitability of the Simplified Image Analysis Method (SIAM) as a tool to assess the saturation distribution of water and Non-Aqueous Phase Liquids (NAPLs) in granular soils, by testing its basic assumption, the existence of a linear relationship between water saturation (Sw), NAPL saturation (So) and optical density (Di), for nine different NAPLs. We then utilized SIAM to study the dynamic behavior of four different LNAPLs that were infiltrated to 1D columns filled with Toyoura sand, and later subjected to two cycles of drainage-imbibition of the water table. It was found that, under similar conditions, the depth of LNAPL infiltration was linearly correlated to the viscosity of the contaminants (R2 = 0.84), the difference between the depth of the mobile fraction after both drainage and imbibition stages was linearly correlated to the interfacial tension values (R2 = 0.79), and the viscosity was logarithmically correlated to the residual saturation ratios for all different NAPLs (R2 = 0.95), correlations that can help us understand and predict the behavior of different contaminants when spilled in the ground.

Contrast Imaging in Mouse Embryos Using High-frequency Ultrasound

Ultrasound contrast-enhanced imaging can convey essential quantitative information regarding tissue vascularity and perfusion and, in targeted applications, facilitate the detection and measure of vascular biomarkers at the molecular level. Within the mouse embryo, this noninvasive technique may be used to uncover basic mechanisms underlying vascular development in the early mouse circulatory system and in genetic models of cardiovascular disease. The mouse embryo also presents as an excellent model for studying the adhesion of microbubbles to angiogenic targets (including vascular endothelial growth factor receptor 2 (VEGFR2) or αvβ3) and for assessing the quantitative nature of molecular ultrasound. We therefore developed a method to introduce ultrasound contrast agents into the vasculature of living, isolated embryos. This allows freedom in terms of injection control and positioning, reproducibility of the imaging plane without obstruction and motion, and simplified image analysis and quantification. Late gestational stage (embryonic day (E)16.6 and E17.5) murine embryos were isolated from the uterus, gently exteriorized from the yolk sac and microbubble contrast agents were injected into veins accessible on the chorionic surface of the placental disc. Nonlinear contrast ultrasound imaging was then employed to collect a number of basic perfusion parameters (peak enhancement, wash-in rate and time to peak) and quantify targeted microbubble binding in an endoglin mouse model. We show the successful circulation of microbubbles within living embryos and the utility of this approach in characterizing embryonic vasculature and microbubble behavior.

Contrast imaging in mouse embryos using high-frequency ultrasound.

Ultrasound contrast-enhanced imaging can convey essential quantitative information regarding tissue vascularity and perfusion and, in targeted applications, facilitate the detection and measure of vascular biomarkers at the molecular level. Within the mouse embryo, this noninvasive technique may be used to uncover basic mechanisms underlying vascular development in the early mouse circulatory system and in genetic models of cardiovascular disease. The mouse embryo also presents as an excellent model for studying the adhesion of microbubbles to angiogenic targets (including vascular endothelial growth factor receptor 2 (VEGFR2) or αvβ3) and for assessing the quantitative nature of molecular ultrasound. We therefore developed a method to introduce ultrasound contrast agents into the vasculature of living, isolated embryos. This allows freedom in terms of injection control and positioning, reproducibility of the imaging plane without obstruction and motion, and simplified image analysis and quantification. Late gestational stage (embryonic day (E)16.6 and E17.5) murine embryos were isolated from the uterus, gently exteriorized from the yolk sac and microbubble contrast agents were injected into veins accessible on the chorionic surface of the placental disc. Nonlinear contrast ultrasound imaging was then employed to collect a number of basic perfusion parameters (peak enhancement, wash-in rate and time to peak) and quantify targeted microbubble binding in an endoglin mouse model. We show the successful circulation of microbubbles within living embryos and the utility of this approach in characterizing embryonic vasculature and microbubble behavior.

Poisson-event-based analysis of cell proliferation.

A protocol for the assessment of cell proliferation dynamics is presented. This is based on the measurement of cell division events and their subsequent analysis using Poisson probability statistics. Detailed analysis of proliferation dynamics in heterogeneous populations requires single cell resolution within a time series analysis and so is technically demanding to implement. Here, we show that by focusing on the events during which cells undergo division rather than directly on the cells themselves a simplified image acquisition and analysis protocol can be followed, which maintains single cell resolution and reports on the key metrics of cell proliferation. The technique is demonstrated using a microscope with 1.3 μm spatial resolution to track mitotic events within A549 and BEAS‐2B cell lines, over a period of up to 48 h. Automated image processing of the bright field images using standard algorithms within the ImageJ software toolkit yielded 87% accurate recording of the manually identified, temporal, and spatial positions of the mitotic event series. Analysis of the statistics of the interevent times (i.e., times between observed mitoses in a field of view) showed that cell division conformed to a nonhomogeneous Poisson process in which the rate of occurrence of mitotic events, λ exponentially increased over time and provided values of the mean inter mitotic time of 21.1 ± 1.2 hours for the A549 cells and 25.0 ± 1.1 h for the BEAS‐2B cells. Comparison of the mitotic event series for the BEAS‐2B cell line to that predicted by random Poisson statistics indicated that temporal synchronisation of the cell division process was occurring within 70% of the population and that this could be increased to 85% through serum starvation of the cell culture. © 2015 The Authors. Published by Wiley Periodicals, Inc.

A Simplified Image Analysis Method to Study LNAPL Migration in Porous Media

A novel Simplified Image Analysis Method was developed and tested to assess the saturation distribution values for water and LNAPLs (Light Non-Aqueous Phase Liquids) in granular soils subjected to fluctuating groundwater conditions. This method, based on the Beer-Lambert Law of transmissivity, determines the saturation of water (Sw) and LNAPLs (So) by comparing the average optical densities (Di) for each matrix element of the tested domain to the corresponding average optical densities for three base pictures of the same domain taken with two digital cameras attached to two different band-pass filters (λ=450 nm and 640 nm). Two equations with two unknowns (Sw and So) are defined for each mesh element, which enables the saturation distribution to be calculated under dynamic conditions. The three base conditions for the domain are: (i) fully saturated with water (Di10), (ii) fully saturated with LNAPL (Di01), and (iii) completely dry (Di00). The Simplified Image Analysis Method was then applied to analyze the behavior of two fluctuating groundwater systems, namely, two-phase air-water and three-phase air-water-LNAPL, in a one-dimensional column, 3.5×3.5×50 cm, filled with Toyoura sand. The mass balance of the drainage-imbibition three-phase air-water-LNAPL system showed a difference of just 4.7% in LNAPL, demonstrating that this non-intrusive and non-destructive method is reliable for providing water and LNAPL saturation distributions throughout the domain when studying the effects of porous soil contamination by LNAPLs subjected to dynamic conditions.

Angiogenesis in rat aortic rings stimulated by very low concentrations of serum and plasma.

Arterial ring cultures are evaluated for assay of angiogenic activity in physiological fluids. A simplified image analysis algorithm involving public domain software is used to measure microvessels sprouting from rat aortic rings embedded in fibrin. Low concentrations (<1% v/v) of serum and plasma stimulate angiogenesis primarily from the adventitia in a dose-dependent manner. Half-percent serum is more potent than optimal concentrations of some of the important known growth factors, including platelet derived growth factor, vascular endothelial growth factor and basic fibroblast growth factor. Sera and plasmas obtained from rodent, bovine, porcine or human sources have comparable angiogenic activities. The angiogenic activity is found to be heat stable, non-dialyzable and on gel chromatography is co-eluted with albumin. However, purified albumin and the essential fatty acids linoleic and linolenic acids have no effect on angiogenesis. It is concluded that rat aortic rings sprout microvessels, primarily from the adventitia, when cultured with serum or plasma in concentrations below those that support cell culture. Accordingly, the aortic ring assay is a promising system to support isolation of angiogenic factors from blood and other physiological fluids.

Combining multispectral image information using color

Color can be used to simplify image analysis with the goal of increasing the amount of information that can be merged into an image to enhance interpretation