# Texture Analysis of Near-Infrared Vein Images During Reactive Hyperemia in Healthy Subjects
## 2 Materials and Methods
### Analytics  
The video files of the participants were decomposed into individual frames using Matlab R2015a (MathWorks, Natick, MA, USA). Given the high anatomical variability in the number, size, and arrangement of superficial veins on the dorsal hand, a standardized and systematic approach to region of interest (ROI) selection was adopted. In each participant, three clearly visible dorsal metacarpal veins were identified, along with two associated tributary veins. For each of these veins, a rectangular ROI was manually selected in every frame, resulting in a total of 126 ROIs analyzed across the dataset (Figure 2). In each ROI, the width perpendicular to the vein axis was manually measured using ImageJ software (version 1.54g; National Institutes of Health, Bethesda, MD, USA) at seven predefined time points: three times during baseline (minutes 1, 3, and 5), twice during occlusion (minutes 6 and 7), and three times during recovery (minutes 9, 11, and 13). These time points were selected to capture representative phases of the protocol. Each ROI was converted to a grayscale image, from which a set of textural parameters was extracted, namely contrast, correlation, energy, homogeneity, and entropy, as well as fractal dimension and lacunarity. No pre-processing of images was performed before assessing textural parameters. Texture features were extracted from grayscale ROIs using standard gray-level co-occurrence matrix (GLCM) methodology. The co-occurrence matrices were normalized as probability distributions, but no normalization of grayscale intensity values was applied to the input ROIs prior to texture analysis. The spatial resolution of the images was estimated at approximately 0.15 mm/pixel, based on calibration using a reference object within the imaging field. This resolution was consistent across all frames and participants due to the fixed position of the imaging system. All analyses were conducted on rectangular ROIs of identical pixel dimensions, manually positioned using consistent anatomical landmarks to ensure reproducibility and minimize variability due to lighting or movement. Recognizing that isolated texture parameters might not fully capture the venous response to congestion and reperfusion, we additionally computed derived indices that combine morphological (vein width) and textural information. This approach was motivated by the observation that correlation increased during occlusion, while entropy remained globally stable. Based on this physiological rationale, the composite indices were constructed heuristically rather than derived from data-driven multivariate models. Therefore, we hypothesized that expressing texture in relative terms, normalized to vein size or entropy, could offer further insight into vascular adaptation. Accordingly, we computed the following composite indices: entropy-to-width ratio, correlation-to-width ratio, total entropic content, and correlation-to-entropy ratio. These derived parameters were designed to capture the relationship between internal texture organization and structural expansion  
Figure 1: Posture adopted by the participants throughout the procedure. The NIRI device lamp was placed 30 cm above the test hand of the participants. during venous filling. Due to the manual nature of width measurements, these indices were calculated at discrete time points rather than continuously across the full sequence. Other first-order histogram features such as mean intensity, skewness, and kurtosis were explored but did not reveal meaningful phase-dependent changes and were thus not included in the final analysis. A full description of all extracted and derived variables is presented in Table 2. Normality of the data were assessed using the Shapiro-Wilk test, which indicated that most parameters did not follow a normal distribution. Therefore, comparisons between experimental phases were performed using the Wilcoxon signed-rank test for related samples. A significance level of 0.05 was adopted for all statistical analyses.