An innovative methodology for segmenting vessel like structures using artificial intelligence and image processing
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Fecha
2025
Autores
Ayala Mantilla, Cristian Eduardo
Villarreal, Reynaldo
Chamorro-Solano, Sindy
Cantillo, Steffen
Pestana-Nobles, Roberto
Arquez, Sair
Vega-Sampayo, Yolanda
Pacheco-Londoño, Leonardo
Paez, Jheifer
Galan-Freyle, Nataly
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Springer Nature
Resumen
Innovation is currently driving enhanced performance and productivity across various fields through
process automation. However, identifying intricate details in images can often pose challenges due
to morphological variations or specific conditions. Here, artificial intelligence (AI) plays a crucial role
by simplifying the segmentation of images.This is achieved by training algorithms to detect specific
pixels, thereby recognizing details within images. In this study, an algorithm incorporating modules
based on Efficient Sub-Pixel Convolutional Neural Network forimage super-resolution, U-Net based
Neural baseline for image segmentation, and image binarization for masking was developed. The
combination of these modules aimed to identify capillary structures at pixel level. The method was
applied on different datasets containing images of eye fundus, citrus leaves, printed circuit boards
to test how well it could segment the capillary structures. Notably, the trained model exhibited
versatility in recognizing capillary structures across various image types.When tested with the Set 5
and Set 14 datasets, a PSNR of 37.92 and SSIM of 0.9219 was achieved, surpassing significantly other
image superresolution methods.The enhancement module processes the image using three different
varaiables in the same way, which imposes a complexity of O(n) and takes 308,734 ms to execute;
the segmentation module evaluates each pixel against its neighbors to correctly segment regions of
interes, generating an O(n2) quadratic complexity and taking 687,509 ms to execute; the masking
module makes several runs through the whole image and in several occasions it calls processes of
O(n log n) complexity at 581686 microseconds to execute, which makes it not only the most complex
but also the most exhaustive part of the program. This versatility, rooted in its pixel-level operation,
enables the algorithm to identify initially unnoticed details, enhancing its applicability across diverse
image datasets. This innovation holds significant potential for precisely studying certain structures’
characteristics while enhancing and processing images with high fidelity through AI-driven machine
learning algorithms.
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Villarreal, R., Chamorro-Solano, S., Cantillo, S. et al. An innovative methodology for segmenting vessel like structures using artificial intelligence and image processing. Sci Rep 14, 30332 (2024). https://doi.org/10.1038/s41598-024-81680-9
