Visualization of blood vessels in in vitro raw speckle images using an energy-based on DWT coefficients. (August 2021)

• Record Type: Journal Article
• Authors: Lopez-Tiro, Francisco Javier; Peregrina-Barreto, Hayde; Rangel-Magdaleno, Jose de Jesus; Ramirez-San-Juan, Julio Cesar
• Is Part Of: Biomedical signal processing and control. Volume 69(2021)
• Publication Date: 2021-08
• Subjects: Visualization of blood vessels -- Laser speckle contrast imaging -- Raw speckle image -- Wavelet approach -- Discrete Wavelet transform; Signal processing -- Periodicals; Biomedical engineering -- Periodicals; Signal Processing, Computer-Assisted -- Periodicals; Image Processing, Computer-Assisted -- Periodicals; Biomedical Engineering -- Periodicals; 610.28
• DOI: 10.1016/j.bspc.2021.102892
• Languages: English
• ISSNs: 1746-8094

Abstract

Highlights: A methodology to improve the visualization of blood vessels using the Discrete Wavelet Transform is performed. Improved visualization of blood vessels in in-vitro raw speckle images (0?m to 900 μm depth). Studies of localization (similarity index) and visualization (kurtosis values) of blood vessels are presented. The results follow a visualization improvement up to 400 μm depth. Abstract: The visualization and localization of blood vessels is an important task to determine the presence and the health status of microvasculature in the biological tissue. Laser Speckle Contrast Imaging is one of the most widely employed techniques to study blood vessels; even so, it has some drawbacks in analyzing deep blood vessels ( > 100 μ m ) since the image noise level increases. The Wavelet Approach is a model of automatic denoising for contrasted in vitro Raw Speckle images using an energy criterion. The criterion selects the more suitable denoising level from the Discrete Wavelet Transform decomposition using the detail coefficients. Then, the segmentation of low-noise images by mathematical morphology techniques establish the blood vessel and biological tissue location. Finally, the region corresponding to the blood vessel and the low-noise images are used to improve the visualization of blood vessels. Results show that a Wavelet Approach improves the visualization of blood vessels up to a depth of 400 μ m . Furthermore, the proposed model demonstrates that the automatic denoising criterion improves the localization of superficial ( ≤ 100 μ m ) and deep ( > 100 μ m ) blood vessels.