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

• Published in: Journal of imaging Vol. 10; no. 1; p. 22
• Main Authors: Priambodo, Purnomo Sidi, Aminoto, Toto, Basari, Basari
• Format: Journal Article
• Language: English
• Published: Switzerland MDPI AG 01.01.2024
• Subjects: Algorithms; attenuation coefficient; Attenuation coefficients; biological tissue; Cheese; Constituents; Decomposition; Diagnostic imaging; image decomposition technique; Image processing; Infrared imaging; Inverse method; Light; Linear equations; Magnetic resonance imaging; Mathematical analysis; matrix inverse; Medical imaging; Medical research; Methods; monochromatic light; Optical properties; Thickness; Tissues; Tomography; Transmittance; transmittance image; Indonesia; attenuation coefficient; transmittance image; biological tissue; matrix inverse; near-infrared; image decomposition technique; monochromatic light
• ISSN: 2313-433X; 2313-433X
• DOI: 10.3390/jimaging10010022

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 different monochromatic lights penetrate a biological tissue, they will experience different attenuation coefficients. Furthermore, the same happens when monochromatic light penetrates 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 -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.