Image Texture Analysis Research Articles

Magnetic resonance imaging texture analysis for the evaluation of viable ovarian tissue in patients with ovarian endometriosis: a retrospective case-control study.

BackgroundTexture analysis has been used as a method for quantifying image properties based on textural features. The aim of the present study was to evaluate the usefulness of magnetic resonance imaging (MRI) texture analysis for the evaluation of viable ovarian tissue on the perfusion map of ovarian endometriosis.MethodsTo generate a normalized perfusion map, subtracted T1-weighted imaging (T1WI), T1WI and contrast-enhanced T1W1 with sequences were performed using the same parameters in 25 patients with surgically confirmed ovarian endometriosis. Integrated density is defined as the sum of the values of the pixels in the image or selection. We investigated the parameters for texture analysis in ovarian endometriosis, including angular second moment (ASM), contrast, correlation, inverse difference moment (IDM), and entropy, which is equivalent to the product of area and mean gray value.ResultsThe perfusion ratio and integrated density of normal ovary were 0.52±0.05 and 238.72±136.21, respectively. Compared with the normal ovary, the affected ovary showed significant differences in total size (p<0.001), fractional area ratio (p<0.001), and perfusion ratio (p=0.010) but no significant differences in perfused tissue area (p=0.158) and integrated density (p=0.112). In comparison of parameters for texture analysis between the ovary with endometriosis and the contralateral normal ovary, ASM (p=0.004), contrast (p=0.002), IDM (p<0.001), and entropy (p=0.028) showed significant differences. A linear regression analysis revealed that fractional area had significant correlations with ASM (r2=0.211), IDM (r2=0.332), and entropy (r2=0.289).ConclusionMRI texture analysis could be useful for the evaluation of viable ovarian tissues in patients with ovarian endometriosis.

T1 and ECV mapping texture analysis distinguishing hypertrophic cardiomyopathy from athletes heart better than median values

Abstract Funding Acknowledgements Type of funding sources: Public grant(s) – National budget only. Main funding source(s): Ph.D fellowship of the Research Foundation Flanders (FWO). The Master@Heart trial is funded by the FWO. Introduction Differentiating intensive training induced hypertrophy from hyperthropic cardiomyopathy (HCM) is important to identify those young athletes at risk of sudden cardiac death. Swoboda and colleagues demonstrated that T1 and ECV mapping can aid such a differentiation between athletic and pathological hypertrophy, particularly in subjects with indeterminate wall thickness (1). Recently texture analysis (TA) methods of CMR data have demonstrated improved diagnostic accuracy over conventional qualitative analysis in various heart diseases. Only few studies have applied TA to T1 and ECV mapping data (2-4). Here we aimed to demonstrate that a TA approach provides superior capacity to distinguish HCM from athlete’s heart over average native T1 and ECV values. Purpose It was our hypothesis that a texture analysis of T1 and ECV mapping images would identify features that could discriminate between a HCM and athlete’s heart with a higher classification accuracy (CA) than average T1 and ECV values. Methods This study included data from 97 subjects diagnosed with HCM (acc. to guidelines; 5) and 28 athletes that took part in the Master@Heart trial (an ongoing study assessing the beneficial effects of long-term endurance exercise for the prevention of coronary artery disease, 6). Long and short axis T1 mapping data was acquired on a 1.5T Philips Ingenia system using MOLLI (seconds scheme). After offline motion correction and T1 and ECV map calculation (7), the left ventricular myocardium was manually delineated (3D Slicer; 8). Texture analysis of the masked images resulted in 194 features (Pyradiomics, standard settings; 9). The dataset was then split (75/25%) for training and testing purposes keeping images from the same subject within the same set. A fast correlation based filter rank was applied to the training data to derive relevant features. A further reduction to only two features was based on the CA of a support vector machine (SVM) learning method (linear kernel; cost 0.9 regression loss epsilon 0.1; leave-one-out). Finally, ROC analysis on the test data was used to determine the diagnostic accuracy for the following predictors: (1) median T1 and ECV (2) two most relevant features (training) (3) combination of (1) and (2) (ROC AUC statistics (10)). Results The two most relevant features were the histogram feature ECV energy and the gray level size zone matrix (GLSZM) feature native T1 zone entropy, a measure of heterogeneity in the texture pattern. A model to distinguish HCM from athletes based on these features outperformed the model using only median T1 and ECV values with both higher sensitivity and specificity (table 1) and a significantly higher AUC in the ROC analysis (p &amp;lt; 0.05, figure 1). Combining these two features with median values did not improve the CA further. Conclusion Texture analysis of motion-corrected T1 and ECV mapping images out-performs classical analysis based on average values in distinguishing HCM from athlete"s heart.

Texture Analysis of Magnetic Resonance Images Enables Phenotyping of Potentially Painful Annular Fissures.

Retrospective analysis of prospectively collected data. To investigate whether intervertebral disc (IVD) image features, extracted from magnetic resonance (MR) images, can depict the extension and width of annular fissures and associate them to pain. Annular fissures are suggested to be associated with low back pain (LBP). Magnetic resonance imaging (MRI) is a sensitive method, yet fissures are sometimes unobservable in T2-weighted MR-images, even though fissure information is present in the image. Image features can mathematically be calculated from MR-images and might reveal fissure characteristics. Forty four LBP patients who underwent MRI, low-pressure discography (<50 psi), and computed tomography (CT) sequentially in 1 day, were reviewed. After semi-automated segmentation of 126 discs, image features were extracted from the T2-weighted images. The number of image features was reduced with principle component analysis (PCA). CT-discograms were graded and dichotomized regarding extension and width of fissures. IVDs were divided into fissures extending to outer annulus versus short/no fissures. Fissure width was dichotomized into narrow (<10%) versus broad fissures (>10%), and into moderately broad (10%-50%) versus very broad fissures (>50%). Logistic regression was performed to investigate if image features could depict fissure extension to outer annulus and fissure width. As a sub-analysis, the association between image features used to depict fissure characteristics and discography-provoked pain-response were investigated. Fissure extension could be depicted with sensitivity/specificity = 0.97/0.77 and area under curve (AUC) = 0.97. Corresponding results for width depiction were sensitivity/specificity = 0.94/0.39 and 0.85/0.62, and AUC = 0.86 and 0.81 for narrow versus broad and moderately broad versus very broad fissures respectively. Pain prediction with image features used for depicting fissure characteristics showed sensitivity/specificity = 0.90/0.36, 0.88/0.4, 0.93/0.33; AUC = 0.69, 0.75, and 0.73 respectively. Standard MR-images contains fissure information associated to pain that can be depicted with image features, enabling non-invasive phenotyping of potentially painful annular fissures.Level of Evidence: 2.

Variant of digital image processing application for recognition of textures on the optoelectronic image

Problem statement. Search of plants on the observable image is important function of the UAV onboard optoelectronic systems. One of methods of the observable surface image analysis is the textural analysis which allows to select homogeneous areas on the observable image and to define contours of the plants which are covered with various structures and not having boundaries accurately defined on the image.The purpose. To develop algorithm of the textural analysis of a site terrestrial or a water surface, fulfilled by the digital image processing. In paper the image textural analysis algorithm fulfilled on the basis of an evaluation of local spectral and statistical performances of the image is presented.Results. The algorithm according to which for each element of the analyzed image in a square environ from 1024 elements the local spectra oriented under different angles and local variances are calculated is developed. Calculated by digital image processing amplitudes of a spectrum and variances values characterize texture parameters in a environ of each element of the image. Points of considerable modifications of local properties define position of boundaries between various structures.The practical importance. Results of the considered algorithm trials as a part of simulation model have shown a possibility of its application in the UAV onboard image observed analysis system.

MRI Volumetrics and Image Texture Analysis in Assessing Systemic Treatment Response in Extra-Abdominal Desmoid Fibromatosis.

Purpose To determine whether MRI volumetric and image texture analysis correlates with treatment-induced biologic changes in desmoid fibromatosis (DF) earlier than conventional response criteria. Materials and Methods This retrospective study included 27 patients with histologically proven extra-abdominal DF who were managed with active surveillance or systemic therapy (from 2004 to 2016). MRI volumetric and image texture parameters were derived from manual tumor segmentations, and tumor signal intensity was normalized to muscle. Results were compared with objective response rates based on Response Evaluation Criteria in Solid Tumors (RECIST) version 1.1, World Health Organization (WHO) lesion response, volumetrics, and MRI-modified Choi criteria. Correlation coefficients (r) between image texture features and maximum tumor diameters were obtained by using a meta-analysis approach. Results The 27 included patients (mean age, 39 years; 74% women) were followed for an average of 4 years, comprising 207 distinct time-point assessments. The mean baseline tumor maximum diameter was 7.9 cm (range, 3.4-15.2 cm). Partial response (PR) rates as best response were 37%, 44%, 70%, and 81% by RECIST, WHO, volumetrics, and MRI-modified Choi criteria, respectively. Among the 10 tumors showing RECIST PR, a preceding MRI-modified Choi PR was observed in 70% (seven of 10), on average 1.3 years earlier. Multiple image texture parameters showed associations with objective measurements of tumor diameter including mean tumor-to-muscle signal ratio (r = 0.51; P = .004), median tumor-to-muscle signal ratio (r = 0.52; P = .003), energy (r = 0.48; P < .001), run entropy (r = 0.32, P = .04), and gray-level nonuniformity (r = 0.54; P ≤ .001). Conclusion Volumetric signal and image texture assessment allows more comprehensive analysis of DF biologic change and may permit early prediction of DF behavior and therapeutic response. Keywords: MR Imaging, Soft Tissues/Skin, Neoplasms-Primary © RSNA, 2021.

Texture analysis of PET images for predicting response to induction chemotherapy for oral squamous cell carcinoma

We evaluated whether texture analysis of positron emission tomography (PET) scans could predict the response to induction chemotherapy (ICT) in patients with oral squamous cell carcinoma (OSCC). Our retrospective cohort study included 21 patients with OSCC who underwent ICT. We extracted 31 textural indices from pretreatment PET scans and performed receiver operating characteristic curve (ROC) analysis to evaluate these indices’ efficacy in predicting ICT response. The area under the ROC curve was statistically significant for one texture index: short-zone emphasis (SZE; p = 0.041).

Research Progress in Remote Sensing Image Texture Analysis

Current remote sensing image texture analysis and research is based texture feature. Specialist, who home and abroad, has been continuously explore an optimal extraction process and texture analyze method or through a variety of methods to realize extraction of remote sensing texture information. There are three directions, which including the research of optimal scale, the fusion of method, the proposal and improvement of new algorithm, could comprehensive summary about recent research progress in remote sensing image texture analysis by sorting and analyzing information extraction of remote sensing texture features, the progress of texture analysis methods and the application of analysis methods in recent years. At the same time, a prediction was made for future development of remote sensing image texture analysis. Five development directions are summarized: fusion method, scale selection, continuation and improvement of traditional methods based on symbiotic gray matrix and wavelet analysis, research and exploration of object-oriented and deep learning methods, and research of new algorithms to improve the information extraction effect.

Chronic kidney disease stage identification using texture analysis of ultrasound images

Early detection of chronic kidney disease (CKD) stages is an important aspect of human healthcare. Clinical approach for identification of CKD stage is expensive, time consuming and mostly not accessible to common people living in remote locations. An automatic method based on ultrasound (US) imaging is more appropriate to receive the first-hand information of kidney health in quick time. Extraction of local features from US image and selection of classifier plays a major role to improve the CKD stage detection accuracy. In this paper, gray level co-occurrence matrix (GLCM) used to extract local features of US kidney images in terms of a fourteen-point feature-vectors. The classifier processes the extracted feature-vectors to identify CKD stage. Extensive simulation performed in MATLAB using different multiclass classifier on 300 sample US images collected by the radiologist to select the multiclass classifier suitable to identify CKD stages with higher accuracy. Simulation result shows that linear discriminant analysis (LDA) classifier performs better than other available multiclass classifiers to classify CKD stages with higher accuracy. Proposed scheme identifies, CKD stage-1, stage-2 and stage-3 with accuracies nearly 96.82%, 100% and 98.38%, respectively. The proposed scheme using the fourteen-point feature vector identify CKD stages with 16% higher accuracy over the exisitng schemes. However, the proposed scheme able to identify CKD stages with 24% higher accuracy than the existing scheme when both the fourteen-point feature-vector and the LDA classifier are used. Therefore, the proposed scheme could be an alternative to the existing scheme for automatic CKD stage identification.

Superficial fibromatosis: MRI radiomics and T2 mapping correlate with treatment response

BackgroundSuperficial fibromatosis exhibits variable MR signal intensity due to collagenous and fibroproliferative components. Quantifying this signal heterogeneity using image texture analysis and T2-mapping could have prognostic and therapeutic implications. MethodsThis IRB-approved retrospective study included 13 patients with superficial fibromatosis, managed by observation, electron beam radiotherapy (EBT), or pentoxifylline/vitamin E. Two-dimensional regions of interest (ROIs) were drawn on proton-density or T2-weighted MRI for radiomics feature analysis, and corresponding T2-maps. Comparisons were made between baseline and follow-up T2 relaxation times and radiomics features: Shannon's entropy, kurtosis, skewness, mean of positive pixels (MPP), and uniformity of distribution of positive gray-level pixel values (UPP). ResultsThere were 19 nodules in 13 subjects. Mean patient age was 60 years; 62% (8/13) were female; mean follow-up was 9.7 months. Nodule diameter at baseline averaged 18.2 mm (std dev 16.2 mm) and decreased almost 10% to 16.6 mm (p = 0.1, paired t-test). Normalized T2 signal intensity decreased 23% from 0.71 to 0.55 (p = 0.03, paired t-test). T2 relaxation time decreased 16% from 46.5 to 39.1 ms (p < 0.001, paired t-test). Among radiomics features, skewness increased to 0.71 from 0.41 (p = 0.03, paired t-test), and entropy decreased from 8.37 to 8.03 (p = 0.05, paired t-test); differences in other radiomics features were not significant. ConclusionsRadiomics analysis and T2-mapping of superficial fibromatosis is feasible; robust decreases in absolute T2 relaxation time, and changes in image textural features (increased skewness and decreased entropy) offer novel imaging biomarkers of nodule collagenization and maturation.

Identify the triple-negative and non-triple-negative breast cancer by using texture features of medicale ultrasonic image: A STROBE-compliant study.

The study aimed to explore the value of ultrasound (US) texture analysis in the differential diagnosis of triple-negative breast cancer (TNBC) and non-TNBC.Retrospective analysis was done on 93 patients with breast cancer (35 patients with TNBC and 38 patients with non-TNBC) who were admitted to Taizhou people's hospital from July 2015 to June 2019. All lesions were pathologically proven at surgery. US images of all patients were collected. Texture analysis of US images was performed using MaZda software package. The differences between textural features in TNBC and non-TNBC were assessed. Receiver operating characteristic curve analysis was used to compare the diagnostic performance of textural parameters showing significant difference.Five optimal texture feature parameters were extracted from gray level run-length matrix, including gray level non-uniformity (GLNU) in horizontal direction, vertical gray level non-uniformity, GLNU in the 45 degree direction, run length non-uniformity in 135 degree direction, GLNU in the 135 degree direction. All these texture parameters were statistically higher in TNBC than in non-TNBC (P <.05). Receiver operating characteristic curve analysis indicated that at a threshold of 268.9068, GLNU in horizontal direction exhibited best diagnostic performance for differentiating TNBC from non-TNBC. Logistic regression model established based on all these parameters showed a sensitivity of 69.3%, specificity of 91.4% and area under the curve of 0.834.US texture features were significantly different between TNBC and non-TNBC, US texture analysis can be used for preliminary differentiation of TNBC from non-TNBC.

Characterizing Brain Tumor Regions Using Texture Analysis in Magnetic Resonance Imaging.

PurposeTo extract texture features from magnetic resonance imaging (MRI) scans of patients with brain tumors and use them to train a classification model for supporting an early diagnosis.MethodsTwo groups of regions (control and tumor) were selected from MRI scans of 40 patients with meningioma or glioma. These regions were analyzed to obtain texture features. Statistical analysis was conducted using SPSS (version 20.0), including the Shapiro–Wilk test and Wilcoxon signed-rank test, which were used to test significant differences in each feature between the tumor and healthy regions. T-distributed stochastic neighbor embedding (t-SNE) was used to visualize the data distribution so as to avoid tumor selection bias. The Gini impurity index in random forests (RFs) was used to select the top five out of all features. Based on the five features, three classification models were built respectively with three machine learning classifiers: RF, support vector machine (SVM), and back propagation (BP) neural network.ResultsSixteen of the 25 features were significantly different between the tumor and healthy areas. Through the Gini impurity index in RFs, standard deviation, first-order moment, variance, third-order absolute moment, and third-order central moment were selected to build the classification model. The classification model trained using the SVM classifier achieved the best performance, with sensitivity, specificity, and area under the curve of 94.04%, 92.3%, and 0.932, respectively.ConclusionTexture analysis with an SVM classifier can help differentiate between brain tumor and healthy areas with high speed and accuracy, which would facilitate its clinical application.

Texture analysis of orbital magnetic resonance imaging for monitoring and predicting treatment response to glucocorticoids in patients with thyroid-associated ophthalmopathy.

PurposeTo evaluate the value of MRI-based texture analysis of extraocular muscle (EOM) and orbital fat (OF) in monitoring and predicting the response to glucocorticoid (GC) therapy in patients with thyroid-associated ophthalmopathy (TAO).MethodsThirty-seven active and moderate-to-severe TAO patients (responders, n = 23; unresponders, n = 14) were retrospectively enrolled. MRI-based texture parameters (entropy, uniformity, skewness and kurtosis) of EOM and OF were measured before and after GC therapy, and compared between groups. Correlations between the changes of clinical activity score (CAS) and imaging parameters before and after treatment were assessed. Receiver operating characteristic curves were used to evaluate the predictive value of identified variables.ResultsResponsive TAOs showed significantly decreased entropy and increased uniformity at EOM and OF after GC therapy (P < 0.01), while unresponders showed no significance. Changes of entropy and uniformity at EOM and OF were significantly correlated with changes of CAS before and after treatment (P < 0.05). Responders showed significantly lower entropy and higher uniformity at EOM than unresponders before treatment (P < 0.01). Entropy and uniformity of EOM and disease duration were identified as independent predictors for responsive TAOs. Combination of all three variables demonstrated optimal efficiency (area under curve, 0.802) and sensitivity (82.6%), and disease duration alone demonstrated optimal specificity (100%) for predicting responsive TAOs.ConclusionMRI-based texture analysis can reflect histopathological heterogeneity of orbital tissues. It could be useful for monitoring and predicting the response to GC in TAO patients.

Artificial Intelligence and Radiomics in Head and Neck Cancer Care: Opportunities, Mechanics, and Challenges.

The advent of large-scale high-performance computing has allowed the development of machine-learning techniques in oncologic applications. Among these, there has been substantial growth in radiomics (machine-learning texture analysis of images) and artificial intelligence (which uses deep-learning techniques for "learning algorithms"); however, clinical implementation has yet to be realized at scale. To improve implementation, opportunities, mechanics, and challenges, models of imaging-enabled artificial intelligence approaches need to be understood by clinicians who make the treatment decisions. This article aims to convey the basic conceptual premises of radiomics and artificial intelligence using head and neck cancer as a use case. This educational overview focuses on approaches for head and neck oncology imaging, detailing current research efforts and challenges to implementation.

Texture Analysis of Fractional Water Content Images Acquired during PET/MRI: Initial Evidence for an Association with Total Lesion Glycolysis, Survival and Gene Mutation Profile in Primary Colorectal Cancer.

Simple SummaryThere is a need to demonstrate additional clinical value/utility for PET/MRI in oncology which moves beyond simple diagnosis. We believe that our currently submitted work represents an initial step in realizing the goal of moving PET/MRI beyond simple diagnosis. We report how texture analysis of parametric images depicting tumor fractional water content derived from routine PET/MRI Dixon acquisitions shows good inter-operator agreement, generates biologically relevant information related to total lesion glycolysis and gene mutation count, and provides prognostic information that can potentially unlock new clinical applications for patients with colorectal cancer. This research study resulted from a long-standing multi-disciplinary collaboration between the nuclear medicine physicians at our institute and experts in quantitative imaging, MRI radiology, GI histology, clinical oncology in colorectal cancer, and several gastrointestinal surgeons at various local hospitals.To assess the capability of fractional water content (FWC) texture analysis (TA) to generate biologically relevant information from routine PET/MRI acquisitions for colorectal cancer (CRC) patients. Thirty consecutive primary CRC patients (mean age 63.9, range 42–83 years) prospectively underwent FDG-PET/MRI. FWC tumor parametric images generated from Dixon MR sequences underwent TA using commercially available research software (TexRAD). Data analysis comprised (1) identification of functional imaging correlates for texture features (TF) with low inter-observer variability (intraclass correlation coefficient: ICC > 0.75), (2) evaluation of prognostic performance for FWC-TF, and (3) correlation of prognostic imaging signatures with gene mutation (GM) profile. Of 32 FWC-TF with ICC > 0.75, 18 correlated with total lesion glycolysis (TLG, highest: rs = −0.547, p = 0.002). Using optimized cut-off values, five MR FWC-TF identified a good prognostic group with zero mortality (lowest: p = 0.017). For the most statistically significant prognostic marker, favorable prognosis was significantly associated with a higher number of GM per patient (medians: 7 vs. 1.5, p = 0.009). FWC-TA derived from routine PET/MRI Dixon acquisitions shows good inter-operator agreement, generates biological relevant information related to TLG, GM count, and provides prognostic information that can unlock new clinical applications for CRC patients.

Polarization imaging feature characterization of different endometrium phases by machine learning

The advance in microscopy and artificial intelligence enables the application of digital pathology in various classification situations to help pathologists reduce the challenge of performing diagnosis purely based on their visualization experience. Human endometrium is receptive to the embryo only during a defined period in a menstrual cycle. The endometrial phase characterization is crucial for the formation of a healthy pregnancy. Polarization imaging is an emerging label-free and non-invasive technique that is good at characterizing the microstructures of biological tissues. In this study, polarization imaging was combined with digital pathology to characterize the microstructures of endometrium samples at the typical proliferative phase and typical secretory phase. The involved polarization parameters include Muller matrix polar decomposition (MMPD) derived parameters δ, θ and a set of rotation invariant parameters PL, DL, qL, rL and their corresponding angular parameters αP, αD, αq and αr. The approaches for the digitalization of the polarization parameter images include the statistical mean analysis that does not involve image texture information, the Local Binary Pattern (LBP) analysis that involves partial image texture information, and the machine learning classifications that make full use of the polarization parameter image information. A class distance Score was defined to evaluate the performance of polarization parameters in the statistical mean and the image texture analysis. The statistical mean analysis indicates parameter DL that relate to the dichroism of the endometrial tissues shows the best class separation ability with the highest class distance Score. Image texture analysis indicates parameter DL still has the highest class distance Score. And compared with the statistical mean method, the class distance Score for DL increased after LBP process. The results of machine learning classification show parameter αD classified by Convolutional Neural Network (CNN) architecture 1 and parameter αP classified by CNN architecture 2 have the same highest accuracy of 87%. This study shows the potential of applying the digital pathology techniques on polarization parameter images to achieve endometrial phase characterization.

Development of image texture analysis technique for boulder distribution measurements: Applications to asteroids Ryugu and Itokawa

Recent asteroid missions have revealed that many sub-kilometer asteroids are rubble piles. Large parts of their surfaces are covered with boulders larger than tens of centimeters. An evaluation of the abundance and size distribution of boulders provides clues to understand surface processes on boulder-covered asteroids. Here we report a new method that automatically measures the abundance of small boulders (sub-pixel to a few pixels), whose boundaries cannot be recognized with visual inspection, by quantifying the surface radiance variation that occurs during the spinning of the asteroid. After validating our approach with previous boulder counting data, we apply this method to images of the asteroids Ryugu and Itokawa, which were visited by JAXA's Hayabusa and Hayabusa2, and obtain a global distribution of the boulders larger than 0.75–3 ​m, which corresponds to 1.5–6 pixels. We find that the boulder number density of this size range is smaller (1) on the western bulge than on the eastern hemisphere and (2) on the equatorial ridge than on the higher latitudes, both of which exceed the number density of boulders ​> ​5 ​m by an order of magnitude. The boulder size distribution at 1.25–20 ​m shows that the boulders smaller than 1 ​m are more abundant at the equator than at mid-latitudes, while those larger than 1 ​m in diameter are more abundant at mid-latitudes than at the equator. This contrast suggests size-dependent migration of boulders in the latitudinal direction. We also find that the typical boulder size (the size reaching the cumulative areal coverage of 50%) is 1.9 ​m at the equatorial region (10°S-10°N) while it is 2.6 ​m at mid-latitudes (40°S-50°S, 40°N-50°N). The typical boulder size is also smaller in the western bulge (2.0–2.2 ​m). We construct global maps of the power-law index of the size frequency distribution of boulders and find minor variations over the entire surface of Ryugu (−2.53 ​± ​0.03) for boulders larger than 1.25 ​m. This small variation suggests homogeneous size sorting processes on Ryugu. Surface roughness does not show a significant correlation with the v-band albedo but shows a high anti-correlation (R ​= ​−0.73) with the current geological slope on the eastern hemisphere. Our method is useful enhancement of smooth area detection and boulder distribution characterization that will be applicable to other planetary explorations in the future, including those of Phobos and other asteroids.

Quantification of Lithological Heterogeneity Within Opalinus Clay: Toward a Uniform Subfacies Classification Scheme Using a Novel Automated Core Image Recognition Tool

The Opalinus Clay is notable in Switzerland as being the selected host rock for deep geological disposal of radioactive waste. Since the early 1990’s, this argillaceous mudstone formation of Jurassic age has been intensively studied within the framework of national and international projects to characterize its geological, hydrological, mechanical, thermal, chemical, and biological properties. While there is no formal stratigraphic subdivision, the Opalinus Clay lithology is classically divided into several, dam- to m-scale sub-units (or facies), depending on location. Recent multi-proxy studies (combining petrographic, petrophysical, geochemical, and mineralogical analyses) have however demonstrated that high, intra-facies, lithological heterogeneity occurs at the dm- to cm-scale. To constrain this small-scale heterogeneity into distinct lithological units (subfacies), the present study aims at defining and presenting a convenient subfacies classification scheme covering the overall Opalinus Clay lithology across northern Switzerland. Petrographic (macro- and microfacies), mineralogical (X-ray diffraction) and textural (image analysis, machine learning and 3D X-ray computed tomography) analyses are performed on diverse drill cores from the Mont Terri rock laboratory (northwestern Switzerland), and results are extended further to the east (Riniken, Weiach, and Benken). Most of the investigated Opalinus Clay can be described by the use of five distinctive subfacies types (SF1 to SF5), which are visually and quantitatively distinguishable by texture (grain size, bedding, fabric, and color) and composition (nature and mineralogy of components). The five subfacies types can be further refined by additional attributes and sedimentary characteristics (biogenic, diagenetic, and structural). Eventually, the widespread and consistent use of standardized Opalinus Clay subfacies types provides the means to harmonize petrographic descriptions within multidisciplinary research projects, enhance reproducibility of in situ experiments, and further evidence the tight relations between lithology and various rock properties.

An integrated texture analysis and machine learning approach for durability assessment of lightweight cement composites with hydrophobic coatings modified by nanocellulose

The aim of the study was to determine a set of image texture features of the lightweight cementitious composites (LLC) with hydrophobic coatings modified with nanocellulose and use them to assess the materials' durability. A novel method based on a combination of image texture analysis and machine learning methods was proposed. Textural features were extracted from the images obtained with a scanning microscope. The best classification model was built by the Support Vector Machine method using 16 features selected by the Sequential Forward Selection algorithm. The model recognizes one of the four ranges of the contact angle, which is closely related to the degree of resistance of the analyzed material, with an accuracy of 82%. The results obtained show a relationship between the effectiveness of hydrophobic coatings in LCC and images of their surfaces. This relationship can be used with machine learning methods for conducting strength diagnostics of building materials.

The use of digital texture image analysis in determining the masticatory efficiency outcome.

The mixture level of gum samples consisting of two colours can be assessed visually, using the electronic colorimetric method, employing digital image processing techniques and specially designed software. The study investigates the possibility of an alternative method called "digital texture image analysis" (DTIA) to assess improvement of masticatory efficiency in denture wearers. The objectives were i) to evaluate whether DTIA discriminates changes in the colour mixing ability within a group over time; ii) to determine whether DTIA can be used to detect improvement in chewing ability; iii) to select the most appropriate DTIA feature that sufficiently describes masticatory efficiency in CDs wearers. The study was designed as an intra-individual evaluation of masticatory efficiency, which was assessed in participants with new dentures in three follow-up times. A set of four texture features was used in the current study. Uniformity, Contrast, Homogeneity and Entropy of the obtained chewing-gum samples were correlated to the degree of gum comminution. A statistically significant difference in masticatory efficiency was observed based on the values of the analysed DTIA variables of gum samples-Uniformity, Contrast, Homogeneity, and Entropy-have changed in the participants during the observation period. The improvement of the masticatory function in relation to the mixing ability of two-coloured chewing gum could be traced by monitoring changes in the values of DTIA variables. The most increasement of masticatory efficiency was observed by monitoring DTIA parameters such as contrast, and homogeneity.

CT Texture Analysis for Differentiating Bronchiolar Adenoma, Adenocarcinoma In Situ, and Minimally Invasive Adenocarcinoma of the Lung.

Purpose: This study aimed to investigate the potential of computed tomography (CT) imaging features and texture analysis to distinguish bronchiolar adenoma (BA) from adenocarcinoma in situ (AIS)/minimally invasive adenocarcinoma (MIA).Materials and Methods: Fifteen patients with BA, 38 patients with AIS, and 36 patients with MIA were included in this study. Clinical data and CT imaging features of the three lesions were evaluated. Texture features were extracted from the thin-section unenhanced CT images using Artificial Intelligence Kit software. Then, multivariate logistic regression analysis based on selected texture features was employed to distinguish BA from AIS/MIA. Receiver operating characteristics curves were performed to determine the diagnostic performance of the features.Results: By comparison with AIS/MIA, significantly different CT imaging features of BA included nodule type, tumor size, and pseudo-cavitation sign. Among them, pseudo-cavitation sign had a moderate diagnostic value for distinguishing BA and AIS/MIA (AUC: 0.741 and 0.708, respectively). Further, a total of 396 quantitative texture features were extracted. After comparation, the top six texture features showing the most significant difference between BA and AIS or MIA were chosen. The ROC results showed that these key texture features had a high diagnostic value for differentiating BA from AIS or MIA, among which the value of a comprehensive model with six selected texture features was the highest (AUC: 0.977 or 0.976, respectively) for BA and AIS or MIA. These results indicated that texture analyses can effectively improve the efficacy of thin-section unenhanced CT for discriminating BA from AIS/MIA.Conclusion: CT texture analysis can effectively improve the efficacy of thin-section unenhanced CT for discriminating BA from AIS/MIA, which has a potential clinical value and helps pathologist and clinicians to make diagnostic and therapeutic strategies.