A model-based inversion method was used to obtain quantitative estimates of histological parameters from multispectral images of the colon and to examine their potential for discriminating between normal and pathological tissues. Pixel-wise estimates of the mucosal blood volume fraction, density of the scattering particles and thickness were derived using a two-stage method. In the first (forward) stage reflectance spectra corresponding to given instances of the parameter values were computed using Monte Carlo simulation of photon propagation through a multi-layered tissue. In the second (inversion) stage the parameter values were obtained via optimisation using an Iterated Conditional Modes (ICM) algorithm based on Discrete Markov Random Fields (DMRF). The method was validated on computer generated data contaminated with noise giving a mean normalized root-mean-square deviation (NRMSD) of 2.04. Validation on ex vivo images demonstrated that parametric maps show gross correspondence with histological features of mucosa characteristic of cancerous, pre-cancerous and non-cancerous colon lesions. The key signs of abnormality were shown to be the increase in the blood volume fraction and decrease in the density of scattering particles.
- Colon cancer; Multispectral imaging; Inverse problems; Diffuse reflectance model; Monte Carlo simulation; Discrete Markov Random Fields.