Decomposition Technique for Bio-Transmittance Imaging Based on Attenuation Coefficient Matrix Inverse

Abstract

Human body tissue disease diagnosis will become more accurate if transmittance images, such as X-ray images, are separated according to each constituent tissue. This research proposes a new image decomposition technique based on the matrix inverse method for biological tissue images. The fundamental idea of this research is based on the fact that when k different monochromatic lights penetrate a biological tissue, they will experience different attenuation coefficients. Furthermore, the same happens when monochromatic light penetrates k different biological tissues, as they will also experience different attenuation coefficients. The various attenuation coefficients are arranged into a unique k×k-dimensional square matrix. k-many images taken by k-many different monochromatic lights are then merged into an image vector entity; further, a matrix inverse operation is performed on the merged image, producing N-many tissue thickness images of the constituent tissues. This research demonstrates that the proposed method effectively decomposes images of biological objects into separate images, each showing the thickness distributions of different constituent tissues. In the future, this proposed new technique is expected to contribute to supporting medical imaging analysis.

Keywords: attenuation coefficient; biological tissue; image decomposition technique; matrix inverse; monochromatic light; near-infrared; transmittance image.

Figure 1

Illustration of how to construct the $\left[\mathit{ln}{T}_{\lambda }^{-1}(x,y)\right]$ vector from three biological images taken with three different monochromatic light sources with equal frames.

Figure 2

Transformation illustration from $\left[\mathit{ln}{T}_{\lambda }^{-1}(x,y)\right]$ to $\left[d_k(x,y)\right]$ by using ${\left[{\mu }_{k\lambda }\right]}^{-1}$.

Figure 3

Experimental setup: the image is captured in transmittance mode.

Figure 4

Reference beam profile image of an empty glass box, ${\mathrm{I}}_{0@808}(\mathrm{x},\mathrm{y})$

Figure 5

Beam profile image of the 0.5 cm thick chicken meat specimen ${\mathrm{I}}_{\mathrm{c}\mathrm{m}0.5@808}\left(\mathrm{x},\mathrm{y}\right),$ taken at an 808 nm wavelength.

Figure 6

The tissue attenuation coefficient plane of the chicken meat specimen ${\mathsf{\mu }}_{\mathrm{c}\mathrm{m}@808}(\mathrm{x},\mathrm{y})$ matrix.

Figure 7

Several measurements were made to determine the final value of the chicken meat attenuation coefficient ${\mathsf{\mu }}_{\mathrm{c}\mathrm{m}@808}(\mathrm{x},\mathrm{y})$ with various specimen thicknesses.

Figure 8

Image of the chicken tissue specimen consisting of three tissue substances, i.e., bone, meat, and skin, arranged side by side. The image was captured using a visible, polychromatic light source.

Figure 9

Image decomposition of Figure 8, consisting of three chicken tissues, i.e., (a) bone, (b) meat, and (c) skin.

Figure 10

(a) A chicken tissue specimen consisting of three tissues, namely, bone, meat, and skin, arranged side by side. (b) Specimen is completely covered by chicken skin tissue. Images were captured using a visible polychromatic light source.

Figure 11

Image decomposition of Figure 10 according to the distribution thickness images of each chicken tissue, i.e., (a) bone, (b) meat, and (c) skin.

Figure 12

The illustration of procedural steps in estimating the quantitative accuracy of a decomposition image based on matching area. (a) Marking the edge boundary of bone tissue in Figure 9a; (b) overlaid on the bone area of Figure 8 and results in (c).

Figure 13

Illustration of setting the illumination intensity of the laser light source to not exceed the camera saturation threshold by adjusting the bias current in the laser semiconductor.