The Effect of Rider:Horse Bodyweight Ratio on the Superficial Body Temperature of Horse’s Thoracolumbar Region Evaluated by Advanced Thermal Image Processing

Appropriate matching of rider–horse sizes is becoming an increasingly important issue of riding horses’ care, as the human population becomes heavier. Recently, infrared thermography (IRT) was considered to be effective in differing the effect of 10.6% and 21.3% of the rider:horse bodyweight ratio, but not 10.1% and 15.3%. As IRT images contain many pixels reflecting the complexity of the body’s surface, the pixel relations were assessed by image texture analysis using histogram statistics (HS), gray-level run-length matrix (GLRLM), and gray level co-occurrence matrix (GLCM) approaches. The study aimed to determine differences in texture features of thermal images under the impact of 10–12%, >12 ≤15%, >15 <18% rider:horse bodyweight ratios, respectively. Twelve horses were ridden by each of six riders assigned to light (L), moderate (M), and heavy (H) groups. Thermal images were taken pre- and post-standard exercise and underwent conventional and texture analysis. Texture analysis required image decomposition into red, green, and blue components. Among 372 returned features, 95 HS features, 48 GLRLM features, and 96 GLCH features differed dependent on exercise; whereas 29 HS features, 16 GLRLM features, and 30 GLCH features differed dependent on bodyweight ratio. Contrary to conventional thermal features, the texture heterogeneity measures, InvDefMom, SumEntrp, Entropy, DifVarnc, and DifEntrp, expressed consistent measurable differences when the red component was considered.

Analysis of KRAS Mutation Status Prediction Model for Colorectal Cancer Based on Medical Imaging

This study retrospectively included some patients with colorectal cancer diagnosed by histopathology, to explore the feasibility of CT medical image texture analysis in predicting KRAS gene mutations in patients with colorectal cancer. Before any surgical procedure, all patients received an enhanced CT scan of the abdomen and pelvis, as well as genetic testing. To define patient groups, divide all patients into test and validation sets based on the order of patient enrollment. A radiologist took a look at the plain axial CT image of the tumor, as well as the portal vein CT image, at the corresponding level. The physician points the computer’s cursor to the relevant area in the image, and TexRAD software programs together texture parameters based on various spatial scale factors, also known as total mean, total variance, statistical entropy, overall total average, mean total, positive mean, skewness value, kurtosis value, and general skewness. Using the same method again two weeks later, the observer and another physician measured the image of each patient again to see if the method was consistent between observers. With regard to clinical information, the KRAS gene mutation group and the wild group of participants in the test set and validation set each had values for the texture parameter. In a study of patients with colorectal cancer, the results demonstrated that CT texture parameters were correlated with the presence of the KRAS gene mutation. The best CT prediction model includes the values of the medium texture image’s slope and the other CT fine texture image’s value of entropy, the medium texture image’s slope and kurtosis, and the medium texture image’s mean and the other CT fine texture image’s value of entropy. Regardless of the training set or the validation set, patients with and without KRAS gene mutations did not differ significantly in clinical characteristics. This method can be used to identify mutations in the KRAS gene in patients with colorectal cancer, making it practical to implement CT medical image texture analysis technology for that purpose.

Image Texture Analysis and Edge Detection Algorithm Based on Anisotropic Diffusion Equation

This paper uses partial differential equation image processing techniques to establish image texture analysis models based on nonlinear anisotropic diffusion equations for image denoising, image segmentation, and image decomposition. This paper proposes a class of denoising models based on the hybrid anisotropic diffusion equation from the characteristics of different noise types. The model exhibits anisotropic diffusion near the image boundary, which can protect the boundary well, and isotropic diffusion inside the image; so, it can remove the noise effectively. We use the immovable point theory to prove the uniqueness of the model solution and further discuss other properties such as asymptotics of the solution. We propose a class of image texture analysis algorithms based on anisotropic diffusion equations and discrete gray level sets. First, a class of nonconvex generalized functions is proposed to remove the noise from the original image to obtain a smooth image while sharpening the edges. Then, an energy generalization function based on the gray level set is proposed, and the existence of the global minimum of this energy generalization function is discussed. Finally, an equivalent form of this energy generalization is given in the discrete case, and an image texture analysis algorithm is designed based on the equivalent form. The algorithm is improved by initial position optimization, dynamic adjustment of parameters, and adaptive selection of thresholds so that the ants can search along the real edges. Experiments show that the improved algorithm for image edge detection can obtain more complete edges and better detection results. The energy generalization function is calculated directly on the discrete gray level set instead of solving the corresponding partial differential equation, which can avoid the selection of the initial level set and the reinitialization of the level set, thus greatly improving the segmentation efficiency. The new algorithm has a high improvement in segmentation efficiency and can efficiently handle large size complex images.

Quantitative Muscle Ultrasonography Using 2D Textural Analysis: A Novel Approach to Assess Skeletal Muscle Structure and Quality in Chronic Kidney Disease

Chronic kidney disease (CKD) is characterized by progressive reductions in skeletal muscle function and size. The concept of muscle quality is increasingly being used to assess muscle health, although the best means of assessment remains unidentified. The use of muscle echogenicity is limited by an inability to be compared across devices. Gray level of co-occurrence matrix (GLCM), a form of image texture analysis, may provide a measure of muscle quality, robust to scanner settings. This study aimed to identify GLCM values from skeletal muscle images in CKD and investigate their association with physical performance and strength (a surrogate of muscle function). Transverse images of the rectus femoris muscle were obtained using B-mode 2D ultrasound imaging. Texture analysis (GLCM) was performed using ImageJ. Five different GLCM features were quantified: energy or angular second moment (ASM), entropy, homogeneity, or inverse difference moment (IDM), correlation, and contrast. Physical function and strength were assessed using tests of handgrip strength, sit to stand-60, gait speed, incremental shuttle walk test, and timed up-and-go. Correlation coefficients between GLCM indices were compared to each objective functional measure. A total of 90 CKD patients (age 64.6 (10.9) years, 44% male, eGFR 33.8 (15.7) mL/minutes/1.73 m2) were included. Better muscle function was largely associated with those values suggestive of greater image texture homogeneity (i.e., greater ASM, correlation, and IDM, lower entropy and contrast). Entropy showed the greatest association across all the functional assessments ( r = −.177). All GLCM parameters, a form of higher-order texture analysis, were associated with muscle function, although the largest association as seen with image entropy. Image homogeneity likely indicates lower muscle infiltration of fat and fibrosis. Texture analysis may provide a novel indicator of muscle quality that is robust to changes in scanner settings. Further research is needed to substantiate our findings.

Quantification of SPECT concentric ring artifacts by radiomics and radial features

Background: Conspicuous concentric ring artifacts in phantom reconstructions triggers retuning SPECT systems. These evaluations are visual, not quantitative. Our study was undertaken to determine the degree to which observers agree about SPECT concentric ring artifacts, and to test whether quantitative texture analysis metrics correspond to significant artifacts.Methods: Test data were acquired as part of quarterly quality assurance using standardized SPECT phantoms containing solid spheres, solid rods and volumes of uniform activity concentration loaded with 99mTc. Forty SPECT studies were identified as having concentric ring artifacts or were acquired to assess whether artifacts were resolved following camera retuning after obtaining an unacceptably non-uniform result. Transaxial reconstructions were reviewed independently by two medical physicists who graded severity of artifacts on a 5-point scale. Counts were tabulated in volumes of interest created in uniform phantom sections, from which were computed 72 radiomics image texture analysis metrics. Radial contrast (RContrast) derived from the radial profile of summed slices transformed into polar coordinates and radial noise-to-signal (RNSR) also calculated.Results: Artifacts were considered sufficiently severe to require camera retuning in 10 rods sections, 17 sphere sections, and 16 uniform sections. In uniform sections, there was “good agreement” for inter-observer and intra-rater assessments (κ = 0.66, Fisher exact p < 0.0001 and κ = 0.61, Fisher exact p = 0.001, respectively). While 3 radiomics image analysis features agreed significantly (p = 0.001) with visual detection of significant artifacts in uniform sections, the parameters most strongly associated with severe artifacts were RContrast > 4.75% and RNSR > 2.7%, for which ROC AUC accuracy = 88%±5%, sensitivity = 83%, specificity = 83%, p < 0.0001. Accuracy was 76%-78% for the 3 radiomics features, with significantly lower specificity (48%-61%, p < 0.05) than RContrast and RNSR. Increasing magnitude of RContrast and RNSR correlated significantly with increasingly severe artifact scores (ρ = 0.71-0.72, p < 0.0001).Conclusion: There was good agreement among physicists as to the presence of circular ring artifacts in uniform sections of SPECT quality assurance scans, with artifacts accurately detected by radial contrast and noise-to-signal ratio measurements.