Automatic image analysis system to measure wound area in vitro

Abstract

In-vitro wound area measurement tracks the rate of wound healing. This project develops and validates an automatic image analysis system to calculate wound area from digital images of an in-vitro 3D tissue model wounded with a biopsy punch. The algorithms were evaluated for repeatability, reliability, and reproducibility, and validated against a known area. Repeatability was checked through repeated measurements under repeated conditions. Reproducibility was evaluated using a Bland Altman plot and paired t-test. Reliability was validated using an image of a known pixel area as a control. The validated image analysis system then calculated wound area from the digital camera and microscope images obtained from an in vitro photo biomodulation treatment experiment. A total of 48 wounded tissues were grouped into red and blue light treatment groups and untreated controls. All daily images were fed into the image analysis system to calculate wound area. The wound area (normalized by day 0) is plotted along the 2-week treatment experiment period to observe wound area in time. The normalised wound area plotted across treatment days show no change in wound area during the treatment period. Future work will adapt the imaging system for visualizing the reepithelialisation cell front marked by live dyes.

A novel chitosan–PEG hydrogel embedded with in situ silver nanoparticles of Clerodendrum glandulosum Lindl. extract: evaluation of its in vivo diabetic wound healing properties using an image-guided machine learning model

“…Collected images were processed using image processing techniques involving the following steps: image acquisition, pre-processing, thresholding, segmentation, morphological operation, feature extraction and wound area measurement. 45 Multiple images of the wound areas of each of the animal groups were captured using the digital camera under a standard lighting environment, and a dataset was created to train the developed machine learning model (codes included in the supplementary file). Gaussian smoothing is applied over the images to enhance the image quality.…”

“…The images are converted from RGB to HSV form. 45 Adaptive thresholding was done to segment the wound area from the background. Contour-based segmentation is done to separate the wound region from the rest of the image.…”

Healing Effects of Iranian Dwarf Elder on Full-thickness Epidermal Thermal Injury in Wistar Rats

“…Area at day n Area at day 0 Where n is the number of days (14). After performing the tissue procedures, slides were stained with H&E. Then, they were examined by light microscopy and GraphPad Prism software to detect new blood vessels.…”