Multiscale Entropy Study of Medical Laser Speckle Contrast Images

Laser speckle contrast imaging (LSCI) is a noninvasive full-field optical imaging technique that gives a 2-D microcirculatory blood flow map of tissue. Due to novelty of commercial laser speckle contrast imagers, image processing of LSCI data is new. By opposition, the numerous signal processing works of laser Doppler flowmetry (LDF) data-that give a 1-D view of microvascular blood flow-have led to interesting physiological information. Recently, analysis of multiscale entropy (MSE) of LDF signals has been proposed. A nonmonotonic evolution of MSE with two distinctive scales-probably dominated by the cardiac activity-has been reported. We herein analyze MSE of LSCI data. We compare LSCI results with the ones of LDF signals obtained during the same experiment. We show that when time evolution of LSCI single pixels is studied, MSE presents a monotonic decreasing pattern, similar to the one of Gaussian white noises. By opposition, when the mean of LSCI pixel values is computed in a region of interest (ROI) and followed with time, MSE pattern becomes close to the one of LDF data, for ROI large enough. LSCI is gaining increased interest for blood flow monitoring. The physiological implications of our results require future study

The effect of calf neuromuscular electrical stimulation and intermittent pneumatic compression on thigh microcirculation

Objective: This study compares the effectiveness of a neuromuscular electrical stimulation (NMES) device and an intermittent pneumatic compression (IPC) device on enhancing microcirculatory blood flow in the thigh of healthy individuals, when stimulation is carried out peripherally at the calf. Materials and method: Blood microcirculation of ten healthy individuals was recorded using laser speckle contrast imaging (LSCI) technique. A region of interest (ROI) was marked on each participant thigh. The mean flux within the ROI was calculated at four states: rest, NMES device with visible muscle actuation (VMA), NMES device with no visible muscle actuation (NVMA) and IPC device. Results: Both NMES and IPC devices increased blood flow in the thigh when stimulation was carried out peripherally at the calf. The NMES device increased mean blood perfusion from baseline by 399.8% at the VMA state and 150.6% at the NVMA state, IPC device increased the mean blood perfusion by 117.3% from baseline. Conclusion: The NMES device at VMA state increased microcirculation by more than a factor of 3 in contrast to the IPC device. Even at the NVMA state, the NMES device increased blood flow by 23% more than the IPC device. Given the association between increased microcirculation and reduced oedema, NMES may be a more effective modality than IPC at reducing oedema, therefore further research is needed to explore this

Aging effect on microcirculation: a multiscale entropy approach on laser speckle contrast images

Purpose: It has long been known that age plays a crucial role in the deterioration of microvessels. The assessment of such deteriorations can be achieved by monitoring microvascular blood flow. Laser speckle contrast imaging (LSCI) is a powerful optical imaging tool that provides two-dimensional information on microvascular blood flow. The technique has recently been commercialized, and hence, few works discuss the postacquisition processing of laser speckle contrast images recorded in vivo. By applying entropy-based complexity measures to LSCI time series, we present herein the first attempt to study the effect of aging on microcirculation by measuring the complexity of microvascular signals over multiple time scales. Methods: Forearm skin microvascular blood flow was studied with LSCI in 18 healthy subjects. The subjects were subdivided into two age groups: younger (20–30 years old, n = 9) and older (50–68 years old, n = 9). To estimate age-dependent changes in microvascular blood flow, we applied three entropy-based complexity algorithms to LSCI time series. Results: The application of entropy-based complexity algorithms to LSCI time series can differentiate younger from older groups: the data fluctuations in the younger group have a significantly higher complexity than those obtained from the older group. Conclusions: The effect of aging on microcirculation can be estimated by using entropy-based complexity algorithms to LSCI time series

Study of Laser Speckle Scattering in Vitreous Humor Models

When a highly high coherent light propagates through a medium, interactions between light and the medium produces a unique intensity speckle pattern that is dependent on several factors such as particle size in the medium, wavelength of the light, concentration of medium, and scattering angle. Speckle patterns from either static or dynamic specimens have been studied using optical techniques due to its non-invasive nature. Speckle patterns from biological specimens (dynamic) are different from that of the static specimens since random movement of molecules (Brownian motion) in the biological specimen affect the light interactions and thereby the intensity of the speckles in the speckle pattern. Several studies have shown the optical properties of the biological specimen can be characterized using statistical properties from the speckle pattern. A histogram of intensity distribution of the speckle pattern can be used to extract certain optical properties of the specimen such as bioactivity, blood flow, and skin perfusion. In this thesis, a new approach for analyzing biological specimens is presented utilizing a peak shift in the histogram plot (called the Histogram Wavelength Analysis Method) of the intensity of the speckles when changing the wavelength of the incident light. Five different wavelengths were used in a modified slit-lamp equipment for the experiment. Also six different sizes of nanobeads embedded in vitreous humor (biological specimen) were studied. The theory developed for this experimental method matches well with the results and will be presented in the thesi

Enhanced flow-motion complexity of skin microvascular perfusion in Sherpas and lowlanders during ascent to high altitude

An increased and more effective microvascular perfusion is postulated to play a key role in the physiological adaptation of Sherpa highlanders to the hypobaric hypoxia encountered at high altitude. To investigate this, we used Lempel-Ziv complexity (LZC) analysis to explore the spatiotemporal dynamics of the variability of the skin microvascular blood flux (BF) signals measured at the forearm and finger, in 32 lowlanders (LL) and 46 Sherpa highlanders (SH) during the Xtreme Everest 2 expedition. Measurements were made at baseline (BL) (LL: London 35 m; SH: Kathmandu 1300 m) and at Everest base camp (LL and SH: EBC 5,300 m). We found that BF signal content increased with ascent to EBC in both SH and LL. At both altitudes, LZC of the BF signals was significantly higher in SH, and was related to local slow-wave flow-motion activity over multiple spatial and temporal scales. In SH, BF LZC was also positively associated with LZC of the simultaneously measured tissue oxygenation signals. These data provide robust mechanistic information of microvascular network functionality and flexibility during hypoxic exposure on ascent to high altitude. They demonstrate the importance of a sustained heterogeneity of network perfusion, associated with local vaso-control mechanisms, to effective tissue oxygenation during hypobaric hypoxia

Mathematical Morphology for Quantification in Biological & Medical Image Analysis

Mathematical morphology is an established field of image processing first introduced as an application of set and lattice theories. Originally used to characterise particle distributions, mathematical morphology has gone on to be a core tool required for such important analysis methods as skeletonisation and the watershed transform. In this thesis, I introduce a selection of new image analysis techniques based on mathematical morphology. Utilising assumptions of shape, I propose a new approach for the enhancement of vessel-like objects in images: the bowler-hat transform. Built upon morphological operations, this approach is successful at challenges such as junctions and robust against noise. The bowler-hat transform is shown to give better results than competitor methods on challenging data such as retinal/fundus imagery. Building further on morphological operations, I introduce two novel methods for particle and blob detection. The first of which is developed in the context of colocalisation, a standard biological assay, and the second, which is based on Hilbert-Edge Detection And Ranging (HEDAR), with regard to nuclei detection and counting in fluorescent microscopy. These methods are shown to produce accurate and informative results for sub-pixel and supra-pixel object counting in complex and noisy biological scenarios. I propose a new approach for the automated extraction and measurement of object thickness for intricate and complicated vessels, such as brain vascular in medical images. This pipeline depends on two key technologies: semi-automated segmentation by advanced level-set methods and automatic thickness calculation based on morphological operations. This approach is validated and results demonstrating the broad range of challenges posed by these images and the possible limitations of this pipeline are shown. This thesis represents a significant contribution to the field of image processing using mathematical morphology and the methods within are transferable to a range of complex challenges present across biomedical image analysis

Human Blastocyst\u27s Zona Pellucida Segmentation via Boosting Ensemble of Complementary Learning

Characteristics of Zona Pellucida (ZP), particularly its thickness, is a key indicator of human blastocyst (day-5embryo) quality. Therefore, ZP segmentation is an important step towards achieving automatic embryo qualityassessment. In this paper, a novel approach based on boosting ensemble of hybrid complementary learning isproposed to segment Zona Pellucida in human blastocyst images. First, an inner-ZP localization method isproposed to separate the ZP from the heavily textured area inside a blastocyst. Then, a deep Hierarchical NeuralNetwork (HiNN) is proposed to segment ZP area. The hierarchical nature of the proposed network enableslearning features with respect to their spatial location in the embryo. Finally, a Self-supervised Image-SpecificRefinement (SISR) strategy is proposed as a complementary step to boost the performance. The proposed systemis a hybrid approach in the sense that the HiNN learns the intra-correlation among images, while the SISR takesinto account the inter-correlation within the query image. Experimental results confirm that the proposed method is capable of identifying ZP area with average Precision, Recall, Accuracy and Jaccard Index of 85.2%, 92.0%, 95.6% and 78.1%, respectively. The proposed HiNN system outperforms state of the art by 4.9% in Precision, 11.2% in Recall, 3.6% in Accuracy and 10.7% in Jaccard Index