Numerical Image Analysis Research Articles

Radiomics in colorectal cancer patients.

The main therapeutic options for colorectal cancer are surgical resection and adjuvant chemotherapy in non-metastatic disease. However, the evaluation of the overall adjuvant chemotherapy benefit in patients with a high risk of recurrence is challenging. Radiological images can represent a source of data that can be analyzed by using automated computer-based techniques, working on numerical information coded within Digital Imaging and Communications in Medicine files: This image numerical analysis has been named "radiomics". Radiomics allows the extraction of quantitative features from radiological images, mainly invisible to the naked eye, that can be further analyzed by artificial intelligence algorithms. Radiomics is expanding in oncology to either understand tumor biology or for the development of imaging biomarkers for diagnosis, staging, and prognosis, prediction of treatment response and diseases monitoring and surveillance. Several efforts have been made to develop radiomics signatures for colorectal cancer patient using computed tomography (CT) images with different aims: The preoperative prediction of lymph node metastasis, detecting BRAF and RAS gene mutations. Moreover, the use of delta-radiomics allows the analysis of variations of the radiomics parameters extracted from CT scans performed at different timepoints. Most published studies concerning radiomics and magnetic resonance imaging (MRI) mainly focused on the response of advanced tumors that underwent neoadjuvant therapy. Nodes status is the main determinant of adjuvant chemotherapy. Therefore, several radiomics model based on MRI, especially on T2-weighted images and ADC maps, for the preoperative prediction of nodes metastasis in rectal cancer has been developed. Current studies mostly focused on the applications of radiomics in positron emission tomography/CT for the prediction of survival after curative surgical resection and assessment of response following neoadjuvant chemoradiotherapy. Since colorectal liver metastases develop in about 25% of patients with colorectal carcinoma, the main diagnostic tasks of radiomics should be the detection of synchronous and metachronous lesions. Radiomics could be an additional tool in clinical setting, especially in identifying patients with high-risk disease. Nevertheless, radiomics has numerous shortcomings that make daily use extremely difficult. Further studies are needed to assess performance of radiomics in stratifying patients with high-risk disease.

Numerical simulation and image analysis of thermal behavior of spacer fabric

Numerical simulation and image analysis of thermal behavior of spacer fabric

Automatic veins analysis of susceptibility weighted image in hypoxic-ischaemic encephalopathy

Background and objectiveThe purpose of this study is to evaluate venous vascular structure and distribution as prognostic indicators of developmental outcomes for infants with neonatal hypoxic-ischaemic encephalopathy (HIE) by detecting and analysing ridges representing vessels on susceptibility-weighted magnetic resonance images (SWIs). MethodsForty-two infants with neonatal HIE underwent SWI in the neonatal period and neurodevelopmental assessment at age 2 years. Normalised histograms of the width, intensity, length and Hessian eigenvalues extracted from the ridge analysis of each patient's SWI are applied as feature vectors to feed into supervised classifiers such as the kNN and random forest (RF) classifiers to predict their neurodevelopmental outcomes. Here we also propose a supervised classifier for automatic prognosis of automated detection of SWI signs of HIE. Our classifier proposed in this paper demonstrates a superior performance in HIE prognosis for the datasets associated with cognitive and motor outcomes and it also enables to determination of brain regions which have been affected by hypoxia-ischaemia by extracting appropriate features from SWI images. ResultsThe feature vectors containing width, intensity, length, and eigenvalue show a promising classification accuracy of 78.67% ± 2.58Linear regression, polynomial regression, and support vector regression (SVR) models predicted outcomes and the lower mean relative errors (MRE) for motor and cognitive outcomes are 0.088 ± 0.073 and 0.101 ± 0.11 respectively. ConclusionThe features derived from the vascular ridges improve the prognostic value of SWI in HIE. Our findings suggest that it is possible to predict neurological, motor, and cognitive outcomes by numerical analysis of neonatal SW images and to identify brain regions on SWI affected by hypoxia-ischaemia.

RGB‐Color Image Analysis for Determination of Birefringence of Micro‐Films and Columnar Coatings

AbstractThe birefringence analysis of thin m SiO2 films deposited via evaporation at a glancing angle of to the normal on resist pillar arrays on Si and nanopatterned SiO2 substrates is carried out by spectral and color (red–green–blue [RGB]) imaging modes. Retardance and birefringence of the films deposited over flat and structured regions can be distinguished with only ≈1% difference between neighboring regions for the visible spectral range using RGB numerical analysis of images. The Michel‐Lévy color map is used for color rendering of birefringence to make quantitative measurements by numerical RGB color filtering. It is shown that by using at 530 nm waveplate inserted at angle to the cross polarizer‐analyzer setup, the range of changes in chromaticity xy‐coordinates expands approximately twice upon angular rotation of a birefringent sample. This facilitates a better signal‐to‐noise determination of birefringence. The proposed method with ‐plate color shifting can be directly used to determine birefringence from step‐like spectral features in reflection and transmission polariscopy. Direct measurement of birefringence () and retardance of SiO2 chevron film is carried out using Berek compensator as a benchmark.

Direct-Writable and Thermally One-Step Curable “Water-Stained” Epoxy Composite Inks

In this study, a simple method for preparing direct-writable and thermally one-step curable epoxy composite inks was proposed. Specifically, colloidal inks containing a mixture of ordinary epoxy resin and anhydride-type hardener with the suspended alumina microplates, as exemplary fillers, are “stained” with small amounts of water. This increases the elasticity of the ink via the interparticle capillary attraction and promotes curing of the epoxy matrix in low-temperature ranges, causing the three-dimensional (3D) printed ink to avoid structural disruption during one-step thermal curing without the tedious pre-curing step. The proposed mechanisms for the shape retention of thermally cured water-stained inks were discussed with thorough analyses using shear rheometry, DSC, FTIR, and SEM. Results of the computer-vision numerical analysis of the SEM images reveal that the particles in water-stained inks are oriented more in the vertical direction than those in water-free samples, corroborating the proposed mechanisms. The suggested concept is extremely simple and does not require any additional cost to the one required for the preparation of the common epoxy–filler composites, which is thus expected to be well-exploited in various applications where 3D printing of epoxy-based formulations is necessary.

Localized Statistical Shape Models for Large-Scale Problems With Few Training Data.

Statistical shape models have been successfully used in numerous biomedical image analysis applications where prior shape information is helpful such as organ segmentation or data augmentation when training deep learning models. However, training such models requires large data sets, which are often not available and, hence, shape models frequently fail to represent local details of unseen shapes. This work introduces a kernel-based method to alleviate this problem via so-called model localization. It is specifically designed to be used in large-scale shape modeling scenarios like deep learning data augmentation and fits seamlessly into the classical shape modeling framework. Relying on recent advances in multi-level shape model localization via distance-based covariance matrix manipulations and Grassmannian-based level fusion, this work proposes a novel and computationally efficient kernel-based localization technique. Moreover, a novel way to improve the specificity of such models via normalizing flow-based density estimation is presented. The method is evaluated on the publicly available JSRT/SCR chest X-ray and IXI brain data sets. The results confirm the effectiveness of the kernelized formulation and also highlight the models' improved specificity when utilizing the proposed density estimation method. This work shows that flexible and specific shape models from few training samples can be generated in a computationally efficient way by combining ideas from kernel theory and normalizing flows. The proposed method together with its publicly available implementation allows to build shape models from few training samples directly usable for applications like data augmentation.

Predicting build density in L-PBF through in-situ analysis of surface topography using powder bed scanner technology

The layerwise nature of additive manufacturing (AM) allows for in-situ monitoring of the consolidate material to identify defects on the fly and produce parts with improved reliability and performance. The main challenge in this paradigm, however, is that current methods have either limited measurement throughput or produce signals that are difficult to interpret and to relate to build properties. In this work, we present a new methodology that combines high-throughput in-situ measurements during laser powder bed fusion ( L -PBF) with robust and unbiased numerical image analysis to predict build density from the surface topography of the consolidated material. The method relies on high resolution and large field of view optical scans of the layer—acquired through our powder bed scanner (PBS) technology—which we segment into “superpixels” to capture local and distributed differences in surface morphology and roughness. The high accuracy of our predictions together with the fast data acquisition and analysis enabled by the PBS and the low-dimensionality of the optical dataset after segmentation make our methodology an ideal candidate for in-line monitoring of materials produced by L -PBF. In addition, the ability to indirectly deduce a specific material property—namely density—as opposed to inferring a qualitative descriptor related to it makes our methodology unique and transferable to commercial powder bed fusion processes.

Efficiency of Numerical Images Analysis in Selection of Durum Wheat [Triticum turgidum (L.) ssp. durum (Desf.) Husn.] Genotypes Growing under Semi-arid Conditions in Algeria

Background: This study was conducted during the 2020/2021 cropping season at Setif Agricultural Experimental Station, it aims to assess the efficiency of using numerical image analysis (NIA) in the selection of durum wheat genotypes in semi-arid areas. Methods: The genetic materials used in this study consist of 11 advanced lines and 4 genotypes of which 3 are local landraces used as control to evaluate their performance, the genotypes tested were sown in a randomized block design (RDB) with three replications. each plot consisted of 6 lines of 10 m long spaced of 0.2 m witdth makes 12 m2 as plot area. Result: Analysis of variance showed that all the parameters measured numerically (senescence and total reflectance) had a very high genotypic significance. The chlorophyll content at full heading showed a very highly significant genotypic effect. Thousand kernels weight, number of spikes per meter square, number of days to heading and plant height had a significant genotypic effect. The correlation study showed that all senescence parameters were significantly correlated. A significant and negative correlation was observed between chlorophyll contents; average of velocity and total reflectance. Grain yield was highly and significantly correlated with thousand kernels weight and number of spikes per meter square. Number of spikes per meter square was significantly and positively correlated with average of velocity and negatively correlated with sum of temperatures at mid-senescence. Number of days to heading was significantly and negatively correlated with senescence average and maximum of senescence average. A significant correlation was observed between plant height and sum of temperatures at mid-senescence.

Beç Tavuklarında (Numida meleagris) Sayısal Görüntü Analizi ve Matematiksel Formüller Kullanılarak Yumurta Kalite Özelliklerinin Belirlenmesi

A total of 200 Guinea fowl (Numidae meleagris) eggs were obtained from guinea fowl flock at the age of 42 weeks of age, which are breeding at an altitude of 1240 m in the Wild Animals Breeding Station affiliated to Ministry of Forest and Water Affairs in Yozgat/Turkey. Eggs were taken at a resolution of 72 pixels per inch and 5184 × 3456 pixels in size, with the individual egg weights. Average Projection area (16,07 cm2), Perimeter (15,82 cm), Circularity (0,81), Height (5,17 cm), Width (4,04 cm), Mean grey value (82,82), Semi axis (2,02 cm), First long half height (2,96 cm), Second short half height (2,20 cm) values were determined by numerical image analysis. Mean Elongation (1,28), Shape Index (78,27) were calculated over the obtained data. Surface area (55,43 cm2), Height (5,16 cm), Width (3,77 cm), Elongation (1,37), Shape Index (73,01), Volume (40,14 cm3), Surface / Volume ratio (1,38), Shell weight (3,17 g), Shell thickness (0,28 mm), pore numbers (6666,25; 5132,39; 5011,12), pore density (120,32; 92,56; 90,31), Yellow ratio (14,85), Yellow Weight (5,95 g), Albumen Weight (30,75), Albumen ratio (77,21) parameters have been calculated using individual egg weights. Eggs were divided into 3 groups as 90 in terms of gray value, eggs 79 in terms of shape index, and 43 in terms of weight. The effects on the properties were investigated. As a result, it is thought that the data obtained can be used in scientific studies.

Facial landmark points detection using knowledge distillation-based neural networks

Facial landmark detection is a vital step for numerous facial image analysis applications. Although some deep learning-based methods have achieved good performances in this task, they are often not suitable for running on mobile devices. Such methods rely on networks with many parameters, which makes the training and inference time-consuming. Training lightweight neural networks such as MobileNets are often challenging, and the models might have low accuracy. Inspired by knowledge distillation (KD), this paper presents a novel loss function to train a lightweight Student network (e.g., MobileNetV2) for facial landmark detection. We use two Teacher networks, a Tolerant-Teacher and a Tough-Teacher in conjunction with the Student network. The Tolerant-Teacher is trained using Soft-landmarks created by active shape models, while the Tough-Teacher is trained using the ground truth (aka Hard-landmarks) landmark points. To utilize the facial landmark points predicted by the Teacher networks, we define an Assistive Loss (ALoss) for each Teacher network. Moreover, we define a loss function called KD-Loss that utilizes the facial landmark points predicted by the two pre-trained Teacher networks (EfficientNet-b3) to guide the lightweight Student network towards predicting the Hard-landmarks. Our experimental results on three challenging facial datasets show that the proposed architecture will result in a better-trained Student network that can extract facial landmark points with high accuracy.

Late Changes in the Extracellular Matrix of the Bladder after Radiation Therapy for Pelvic Tumors.

Radiation therapy is one of the cardinal approaches in the treatment of malignant tumors of the pelvis. It leads to the development of radiation-induced complications in the normal tissues. Thus, the evaluation of radiation-induced changes in the extracellular matrix of the normal tissue is deemed urgent, since connective tissue stroma degradation plays a crucial role in the development of Grade 3–4 adverse effects (hemorrhage, necrosis, and fistula). Such adverse effects not only drastically reduce the patients’ quality of life but can also become life-threatening. The aim of this study is to quantitatively analyze the bladder collagen state in patients who underwent radiation therapy for cervical and endometrial cancer and in patients with chronic bacterial cystitis and compare them to the normal bladder extracellular matrix. Materials and methods: One hundred and five patients with Grade 2–4 of radiation cystitis, 67 patients with bacterial chronic cystitis, and 20 volunteers without bladder pathology were enrolled. Collagen changes were evaluated depending on its hierarchical level: fibrils and fibers level by atomic force microscopy; fibers and bundles level by two-photon microscopy in the second harmonic generation (SHG) mode; general collagen architectonics by cross-polarization optical coherence tomography (CP OCT). Results: The main sign of the radiation-induced damage of collagen fibrils and fibers was the loss of the ordered “basket-weave” packing and a significant increase in the total area of ruptures deeper than 1 µm compared to the intact sample. The numerical analysis of SHG images detected that a decrease in the SHG signal intensity of collagen is correlated with the increase in the grade of radiation cystitis. The OCT signal brightness in cross-polarization images demonstrated a gradual decrease compared to the intact bladder depending on the grade of the adverse event. Conclusions: The observed correspondence between the extracellular matrix changes at the microscopic level and at the level of the general organ architectonics allows for the consideration of CP OCT as a method of “optical biopsy” in the grading of radiation-induced collagen damage.

Analysis of Tomographic Images of the Soil Pore Space Structure by Integral Geometry Methods

The technique of numerical analysis of three-dimensional tomographic images of the pore space of soil objects has been used in this paper. It applies methods of integral geometry, topology and morphological analysis. To characterize quantitatively the transformation of the pore space structure, tomographic images of four undisturbed soils were analyzed, i.e., heavy loamy agro-gray soil (Retic Phaeozem), agromineral (Sapric Rheic Mineralic Histosols), and hypnum (brown moss Sapric Rheic Histosols) peat soils in dry and wet conditions. For samples of the subplow horizon in agro-gray soil, a decrease in both Betty numbers was observed on wetting, where the zero number (b0) stands for the amount of topologically simple closed pores, and the first number (b1) indicates a decrease in pore connectivity, which varies in a narrower range of pore sizes as compared to b0. When a sample of agromineral peat soil is moistened, the Euler–Poincaré characteristic is negative ​in the pore range of 0.1–0.16 mm, which points to the predominating complicated branched structure of the pore space and high pore connectivity. When hypnum moss is saturated, a lot of tunnel pores get narrower (“collapse”), and the connectivity decreases due to the structural specifics of long-stemmed plant residues. The number of pores and connections between them in peat soils is an order of magnitude higher than those in the subplow horizon A of the agro-gray soil. The provided quantitative changes in the considered parameters of tomographic images of the soil pore space confirm the possibility of applying them for estimating the transformation of the pore space in soils.

(Invited) Multi-Scale Analysis of Transport in Dry and Partially-Saturated Porous Media

Improvements in imaging techniques, such as micro- and nano-computer tomography (CT) and focus ion beam scanning electron microscopy (FIB-SEM), have enabled the reconstruction of complex porous media that can then be analyzed by computer simulation to estimate effective transport properties, e.g., ref. [1, 2]. Experimental setups have also been developed to measure dry and partially-saturated gas diffusivity and permeability of gas diffusion layers (GDLs) and catalyst layer (CLs), e.g., ref. [3, 4]. Based on the available literature, a comparison between micro-scale simulation results and experimental data must be performed to assess the accuracy of direct numerical simulation CT image analysis for dry and partially-saturated transport property estimation. In the later case, the validity of computational methods to predict water intrusion, such as full morphology/morphological image opening [1] and watershed segmentation [5], should also be assessed by comparing mercury intrusion experiments to numerical predictions, and by direct comparison of numerical results to CT images. Finally, these transport properties should be integrated into complete fuel cell models where volume-averaging techniques are used to properly estimate channel-porous media interactions. Such interactions however are seldom studied in literature.The proposed contribution aims at first studying the validity of two popular methods for numerical estimation of effective transport properties from three-dimensional reconstructed porous media, i.e., direct numerical simulation (DNS) and pore network modeling (PNM). Two fuel cell gas diffusion media and an electrolyzer porous transport layer are analyzed by CT and characterized by measuring mercury intrusion porosimetry (MIP), and dry permeability and diffusivity. A comparison of numerical and experimental results shows that DNS tools in OpenFCST [6] are capable of accurately predicting intrusion, and transport properties without using any fitting parameters. Accurate predictions are also achieved with the PNM implementation in OpenPNM [7] when the inscribed diameter method is used to estimate the pore size distribution, and the equivalent diameter is used to estimate pore transport properties. These tools open an avenue for computational design of porous materials.Effective transport properties must be integrated into volume-averaged fuel cell models where porous media-channel interactions are of paramount importance. These interactions however, as well as the method used for volume averaging fuel cell equations, are seldom studied in detail. Therefore, the second contribution of the talk aims at analyzing channel-porous media interactions by developing a compressible volume-averaged channel-porous media model in OpenFCST, and validating it with respect to permeability and diffusion bridge experiments in the literature. Comparison of numerical and experimental results show that, depending on the volume-averaging methodology used, experimentally obtained transport properties do not correspond to the input parameters needed in volume-averaged models. Finally, the numerical model is used to estimate flow by-pass in serpentine and inter-digitated channels, as well as compressibility effects [8].

A new approach to registering ice covers simulated on a sectional model of a bridge stay cable in laboratory conditions

This study introduces a new approach to capturing ice shapes accreted on a full-scale model of a bridge stay cable by using the digital photogrammetry technique combined with numerical image analysis. Based on the collected data, three-dimensional finite element models were developed that mapped the obtained icing profiles. Qualitative and quantitative description of the shapes of the ice covers were formulated. As a result, new non-melting iced cable models were made for further laboratory investigations on a smaller scale of 1:1.6 using a three-dimensional printing method. Six ice covers were extracted in a climate laboratory simulating various wet icing conditions. The obtained representative icing shapes were similar to those observed in nature. This study is a valuable example of how the photogrammetry technique can be applied to register ice shapes of a bridge cable, which can then be used for further aerodynamic or aeroelastic analysis.

Mapping of African urban settlements using Google Earth images

ABSTRACT The main purpose of this work is to explore a reproducible way of replacing manual digitalizations of urban agglomerations using textural image processing from free high-resolution Google Earth images. Photo-interpretation from very high-resolution satellite images is a reliable method for extracting urban agglomerations, but it is extremely time consuming. As an alternative to manual digitalization, a numerical image analysis method based on mathematical morphology has been developed to detect textural patterns associated with built-up areas. Hundreds of urban agglomerations were extracted using this method and then compared to an open geodatabase. The results were better for humid regions, and omission errors were consistently larger than commission errors for the whole dataset, underlining the challenge of detecting highly scattered habitats with texture analyses. However, the method could provide an appropriate estimate of overall urbanization. This research highlights the weaknesses and the strengths of the method compared to manual digitalization, illuminating paths for future improvement of alternative procedures to manual digitalization.

MSENet: Multi-Modal Squeeze-and-Excitation Network for Brain Tumor Severity Prediction

Classification of brain tumors is one of the daunting tasks in medical imaging and incorrect decisions during the diagnosis process may lead to increased human fatality. Latest advances in artificial intelligence and deep learning have opened the path for the success of numerous medical image analysis tasks, including the recognition of brain tumors. In this paper, we propose a simple architecture based on deep learning that results in strong generalization without needing much preprocessing. The proposed Multi-modal Squeeze and Excitation model (MSENet) receives multiple representations of a given tumor image, learns end-to-end and effectively predicts the severity level of tumor. Convolution feature descriptors from multiple deep pre-trained models are used to effectively describe the tumor images and are supplied as input to the proposed MSENet. The squeeze and excitation blocks of the MSENet allow the model to prioritize tumor regions while giving less emphasis to the rest of the image, serving as an attention mechanism in the model. The proposed model is evaluated on the benchmark brain tumor dataset that is publicly accessible from the Figshare repository. Experimental studies reveal that in terms of model parameters, the proposed approach is simple and leads to competitive performance compared to those of existing complex models. By increasing complexity, the proposed model leads to generalize better and achieves state-of-the-art accuracy of 96.05% on Figshare dataset. Compared to the existing models, the proposed model neither uses segmentation nor uses augmentation techniques and without much pre-processing achieves competitive performance.

Lensless digital holographic microscopy as an efficient method to monitor enzymatic plastic degradation

A big challenge of the 21st century is to cope with the huge amounts of plastic waste on Earth. Especially the oceans are heavily polluted with plastics. To counteract this issue, biological (enzymatic) plastic decomposition is increasingly gaining attention. Recently it was shown that polyethylene terephthalate (PET) can be degraded in a saltwater-based environment using bacterial PETase produced by a marine diatom. At moderate temperatures, plastic biodegradation is slow and requires sensitive methods for detection, at least at initial stages. However, conventional methods for verifying the plastic degradation are either complex, expensive, time-consuming or they interfere with the degradation process. Here, we adapt lensless digital holographic microscopy (LDHM) as a new application for efficiently monitoring enzymatic degradation of a PET glycol copolymer (PETG). LDHM is a cost-effective, compact and sensitive optical method. We demonstrate enzymatic PETG degradation over a time course of 43 days employing numerical analysis of LDHM images.

Internet of Things and Synergic Deep Learning Based Biomedical Tongue Color Image Analysis for Disease Diagnosis and Classification

In recent times, internet of things (IoT) and wireless communication techniques become widely used in healthcare sector. Biomedical image processing is commonly employed to detect the existence of diseases using biomedical images. Tongue diagnosis is an efficient, non-invasive model to perform auxiliary diagnosis any time anywhere that is support the global necessity in the primary healthcare system. Conventionally, medical practitioners investigate the tongue features based on their expert's knowledge comes from experience. In order to eradicate the qualitative aspects, tongue images can be quantitatively examined, offering an effective disease diagnostic process in such a way that the physical harm of the patients can be minimized. Numerous tongue image analysis approaches exist in the literature, it is required to develop automated deep learning (DL) models to diagnose the diseases using tongue image analysis. In this view, this paper designs an automated IoT and synergic deep learning based tongue color image (ASDL-TCI) analysis model for disease diagnosis and classification. The proposed ASDL-TCI model operates on major stages namely data acquisition, pre-processing, feature extraction, classification, and parameter optimization. Primarily, the IoT devices are used to capture the human tongue images and transmitted to the cloud for further analysis. In addition, median filtering based image pre-processing and SDL based feature extraction techniques are employed. Moreover, deep neural network (DNN) based classifier is applied to determine the existence of the diseases. Lastly, enhanced black widow optimization (EBWO) based parameter tuning process takes place to enhance the diagnostic performance. For assessing the effectual performance of the ASDL-TCI model, a set of simulations take place on benchmark tongue images and examined the results under distinct dimensions. The simulation outcome verified the enhanced diagnostic performance of the ASDL-TCI model over the compared methods with the maximum precision, recall, and accuracy of 0.984, 0.973, and 0.983.

Multi-scaling Properties of 2D Reservoir Micro-pore Heterogeneity Based on Digital Casting Thin-Section Images

In recent years, in order to study the micro-pore structures of reservoirs, to define the types of reservoir space and to classify and evaluate reservoir capabilities, the technology of numerical image analysis has been concerned widely with the study of pore heterogeneity with fractals and multifractals. This is of great challenge due to developments in image resolutions and quantitative characterization methods. In this paper, the pore structure of digital casting thin-section images of six samples of volcanic and volcano-sedimentary rocks from the Dagang area (Huanghua depression, China) was studied, and then the pore spaces of the reservoir samples were characterized automatically using multifractal parameters advantageous to describe the irregularity and disorder of geometric objects. The method of moments was used to derive the parameters of multifractality and heterogeneity of pore structures. The results show well that, together with cluster analysis, 12 fractal and multifractal parameters can be applied to describe effectively the irregular degrees of pore spaces. The six rock samples have been classified into three groups with various reservoir capabilities. The work presented here can provide new avenues for analysis of the spatial arrangement of mineral particles, aggregates and pores in spaces, and that the multi-scaling nature of casting thin-section images of rocks suggests using these algorithms as a basis for reservoir capability assessment.

Quantitative Analysis of Pore Space Structure in Dry and Wet Soil by Integral Geometry Methods

We present a methodology for a numerical analysis of three-dimensional tomographic images in this paper. The methodology is based on integral geometry, topology, and morphological analysis methods. It involves calculating cumulative and non-cumulative pore size distributions of Minkowski functionals and Betti numbers. We investigated 13 samples in dry and wet (saturated beyond the field capacity) conditions within different horizons of the Phaeozem albic. For samples of the arable horizon, an increase in the Euler characteristic was observed in the process of wetting. For samples from the A2, AB and B2 horizons, the Euler-Poincare characteristic decreased during wetting. It has been proven that both Betti numbers (number of isolated pores and number of “tunnels”) decrease with swelling of the AB and B2 horizons at a depth of 20–90 cm. For samples from the arable horizon, another dependence was observed: A Betti number of zero increased first but decreased during wetting. Based on the change in topological characteristics, two methods of changing the topology of the void space of the soil were demonstrated. The above-described quantitative changes of proposed parameters of pore space tomographic images prove the possibility and progressiveness of their usage for the pore space transformation estimate.