# Texture Analysis is a Useful Tool to Assess the Complexity Profile of Microcirculatory Blood Flow
## 1 Introduction  
The regulation of the cardiovascular system results from the adjustment of several biophysical phenomena, both electrical and mechanical. Their coordination is highly complex, depending on the continuous feedback and cross-talk between effector organs and controlling systems [1]. The heart pump and respiration are the most notorious \"central\" processes governing the performance of the cardiovascular system, while on a more \"peripheral\" level, the phenomena that control vascular tone, such as the myogenic activity of the vessel wall, the sympathetic activity and the endothelial release of vasoactive substances, play crucial roles [2; 3; 4]. Microcirculation signals are composed of both these central and peripheral components and therefore provide an \"integrated\" view of cardiovascular function [4; 5]. The contributions from these multiple regulation systems and their intricate interplay explain the oscillatory properties of microcirculation signals. From an analytical standpoint, these oscillatory properties are increasingly considered as a means to extract more sensible informationnot apparent from the general analysis of the raw signals, in particular their spectral origin and the significance of their fractal and chaotic profiles [5; 6; 7]. In recent years, much attention has been given to the study of the complexity of physiological signals, a property that reflects the adaptability of the systems that generated them. Intuitively, the term complexity is often associated with \"structural richness\" [8], which can be applied to the study of biological signals and images. Complexity is typically assessed as \"entropy,\" a general measure of \"disorganization\" in physical, chemical and biological phenomena [9]. In biomedical research, entropy is used as a quantitative parameter, and is typically assessed in continuous, but temporally unpredictable, physiological signals, such as electrocardiography [10], electroencephalography [11], blood pressure [12], laser Doppler flowmetry (LDF) [13], and photoplethysmography (PPG) [14]. Physiological signals may be regular (e.g., more periodic) or irregular. The higher entropy measured in irregular signals reflects the adaptation capability to changing internal and external conditions of the biological system that generated them [15]. Overall, the entropy of physiological signals tends to decrease whenever the systems that generated them lose adaptability, which can result in a compromise of their function or even in disease. Therefore, a suitable metric of complexity should assign higher values to the output signal of a \"healthier\" system with a rich and meaningful structure and lower values to either random dynamics or predictable systems, which are often associated with disease [8; 16; 17]. This has been observed in the aging process [18; 19] and in cardiovascular, metabolic and neurological diseases [18], among several others. In addition to pathological states, some experimental procedures are sufficiently \"challenging\" to temporarily provoke tissue dysfunction, especially those that reduce perfusion [6]. For continuous signals, the multiscale entropy analysis (MSE) is considered a robust analytical tool, having shown superiority over other measures for its ability to assess entropy over different time scales [19]. Entropy can also be assessed in texturally rich biological images, reflecting their textural \"disorganization.\" Texturally rich images can be assessed with texture analysis (TA), an analytical tool allowing the detection of various features on a gray-level image to discriminate textural differences, such as texture entropy (TE), contrast, correlation, homogeneity, and energy. Entropy calculated from TA of images may reflect different information than that conveyed by the entropy assessment of continuous numerical series. When applied to a grayscale image, high entropy means that the pixels can adopt a high number of gray levels, expressing richer information [20].