Adaptive window space direction laser speckle contrast imaging to improve vascular visualization

Abstract

Vascular visualization is crucial in monitoring, diagnosing, and treating vascular diseases. Laser speckle contrast imaging (LSCI) is widely used for imaging blood flow in shallow or exposed vessels. However, traditional contrast computation using a fixed-sized sliding window introduces noise. In this paper, we propose dividing the laser speckle contrast image into regions and using the variance criterion to extract pixels more suitable for the corresponding regions for calculation, and changing the shape and size of the analysis window at the vascular boundary regions. Our results show that this method has a higher noise reduction and better image quality in deeper vessel imaging, revealing more microvascular structure information.

Study of adaptive window space direction contrast method in transmission speckle contrast imaging

“…In the analysis window, neighboring pixels with low similarity to the central pixel are excluded from the contrast calculation, while those with high similarity are considered for calculating the contrast value. Furthermore, awsdK method (Figure 5) [13] divides the laser speckle contrast image into different regions and uses the variance criterion to extract pixels that are more suitable for the corresponding region for calculation. In the dynamic region, the pixel with the minimum variance direction is selected to calculate the contrast value, while in the static region, the pixel with the maximum variance direction is chosen for contrast calculation, and in the dynamic and static transition regions, the contrast value was calculated using the variance median direction.…”

“…Dunn et al [12] also discovered that using T-LSCI allowed microvascular imaging of human finger joints without the need for other auxiliary methods. Recently, the adaptive window space direction contrast (awsdK) [13] method has been introduced. It divides laser speckle contrast images into different regions and utilizes variance criteria to extract pixels suitable for each corresponding region for calculation.…”

Improving Blood Vessel Segmentation and Depth Estimation in Laser Speckle Images Using Deep Learning

“…The adaptive processing of the RSI is one of the most studied approaches to deal with the problem of loss of spatial resolution in the CI besides noise reduction by adding invasive agents to the bloodstream [32][33][34][35][36], or through filtering [37,38]. Adaptive methods aim to select suitable analysis windows for the morphology of the blood vessels in the image, obtaining a more representative and larger sample to compute the statistics [39][40][41][42][43]. Although adaptive methods have improved the quality of CIs, it is necessary to improve the pixel selection process.…”

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“…In conventional LSCI techniques, the laser speckle spatial contrast analysis (LSSCA) [12] , laser speckle temporal contrast analysis (LSTCA) [13] , and spatiotemporal laser speckle contrast analysis (stLASCA) [14] all fall into the domain of filtration procedures that use the rectangular window to calculate the contrast that cannot achieve high-quality imaging, high statistical accuracy, and fast imaging. Recently, the Fourier-based Gaussian sliding window [15] and the adaptive window [16] were introduced into the calculation of LSCI, which has made a great breakthrough in developing LSCI technology. On the other hand, statistical accuracy, imaging quality, and reconstruction speed are closely related to the frame number of the raw speckle images [17][18][19] .…”