Adaptive Feature Extraction for Blood Vessel Segmentation and Contrast Recalculation in Laser Speckle Contrast Imaging

Abstract

Microvasculature analysis in biomedical images is essential in the medical area to evaluate diseases by extracting properties of blood vessels, such as relative blood flow or morphological measurements such as diameter. Given the advantages of Laser Speckle Contrast Imaging (LSCI), several studies have aimed to reduce inherent noise to distinguish between tissue and blood vessels at higher depths. These studies have shown that computing Contrast Images (CIs) with Analysis Windows (AWs) larger than standard sizes obtains better statistical estimators. The main issue is that larger samples combine pixels of microvasculature with tissue regions, reducing the spatial resolution of the CI. This work proposes using adaptive AWs of variable size and shape to calculate the features required to train a segmentation model that discriminates between blood vessels and tissue in LSCI. The obtained results show that it is possible to improve segmentation rates of blood vessels up to 45% in high depths (≈900 μm) by extracting features adaptively. The main contribution of this work is the experimentation with LSCI images under different depths and exposure times through adaptive processing methods, furthering the understanding the performance of the different approaches under these conditions. Results also suggest that it is possible to train a segmentation model to discriminate between pixels belonging to blood vessels and those belonging to tissue. Therefore, an adaptive feature extraction method may improve the quality of the features and thus increase the classification rates of blood vessels in LSCI.

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

“…On the other hand, the adaptive method aK with a d = 11 obtained the lowest RMSE (0.0095 ± 0.0281). This may be because the method generates more stable images where the contrast values in the blood vessel change less in its center, as discussed in [49]. In addition, the reference that the ET and LA provide to the network may cause an improvement in RMSE.…”

“…Most common methods in the literature include the global thresholding that assumes a bimodal distribution in the image to discriminate between the blood vessel and the static region [47]. This k-means method can be either performed on the contrast image or by using features from it such as the range, standard deviation, or entropy (k-means of features) [48,49]. On the other hand, the morphological approach combines a two-step methodology that first segments the blood vessels and then applies a process to decrease the blobs in the image by using morphological operations [44].…”