Selective Optical Imaging for Detection of Bacterial Biofilms in Tissues

Abstract

Significance: The development of an imaging technique to accurately identify biofilm regions on tissues and in wounds is crucial for the implementation of precise surface-based treatments, leading to better patient outcomes and reduced chances of infection.

Aim: The goal of this study was to develop an imaging technique that relies on selective trypan blue (TB) staining of dead cells, necrotic tissues, and bacterial biofilms, to identify biofilm regions on tissues and wounds.

Approach: The study explored combinations of ambient multi-colored LED lights to obtain maximum differentiation between stained biofilm regions and the underlying chicken tissue or glass substrate during image acquisition. The TB imaging results were then visually and statistically compared to fluorescence images using a shape similarity measure.

Results: The comparisons between the proposed TB staining method and the fluorescence standard used to detect biofilms on tissues and glass substrates showed up to 97 percent similarity, suggesting that the TB staining method is a promising technique for identifying biofilm regions.

Conclusions: The TB staining method demonstrates significant potential as an effective imaging technique for the identification of fluorescing and non-fluorescing biofilms on tissues and in wounds. This approach could lead to improved precision in surface-based treatments and better patient outcomes.

Keywords: biofilm imaging; image processing; segmentation; trypan blue; wounds.

Figure 1

Imaging setup after the sample undergoes trypan blue staining. Image acquisition is carried out using a Mightex monochrome camera viewing samples illuminated under various colored lights for automated segmentation of the biofilm region. (The LED colors explored for image optimization are shown on the left side of the panel).

Figure 2

Block diagram of image acquisition and processing for biofilm region detection. Images are acquired, artifacts are removed by glare reduction, noise removal, cropping and contrast enhancement algorithms image subtraction is done, and Otsu/manual thresholding of biofilm region is carried out. Image processing is done in MATLAB.

Figure 3

Shape similarity evaluation procedure.

Figure 4

(top) Fluorescent images of 3 different biofilm-glass samples with (bottom) their corresponding images taken by the proposed method. The images are obtained with a regular color sensor under 405 nm excitation light. The cyan region suggests the presence of biofilms, while the dark region in the bottom images suggest biofilm presence. The red-circled objects are just markers for image alignment purposes.

Figure 5

Three sets of biofilm region obtained after Otsu thresholding to remove the non-stained background. Each set contains a fluorescent biofilm image (top) and a trypan blue-based image (bottom). The darker the region indicate a heavier biofilm presence. The fluorescent images (standard) appear to reveal a more comprehensive biofilm region compared to the TB technique.

Figure 6

Images of a chicken-biofilm sample taken under different lights after trypan blue staining. The image containing the orange trace is the fluorescent image (standard) clearly revealing the bright biofilm regions. The colored lights that produce each image are shown at the top middle of the image. The colors are: (a) 405 nm UV (b) Cyan (c) Purple (d) Red (e) Blue (f) Green (g) White (h) Yellow.

Figure 7

Images produced by subtracting various combinations of TB images captured under different lighting conditions. (a) Cyan-Purple subtraction (b) Green-Red subtraction (c) Green-Purple subtraction. The individual images are shown in Figure 6.

Figure 8

Segmented biofilm regions of 3 chicken biofilm samples (a–c), fluorescent biofilm regions (left) and TB biofilm regions (right). Quantitative analysis is in Table 1.