Specular Reflectance Suppression in Endoscopic Imagery via Stochastic Bayesian Estimation

A novel stochastic Bayesian estimation method is introduced for the purpose of suppressing specular reflectance in endoscopic imagery, benefiting both computer aided and manual analysis of endoscopic data. The maximum diffuse chromaticity, which is necessary for the calculation of the specular reflectance, is estimated via Bayesian least-squares minimization, with the posterior probability of maximum diffuse chromaticity given maximum chromaticity constructed via an adaptive Monte Carlo sampling approach. Experimental results using a set of clinical endoscopic imagery showed that the proposed method resulted in lower coefficient of variation values when compared to existing methods in homogeneous regions contaminated by strong specular highlights, which is indicative of improved specular reflectance suppression. These findings are further reinforced by visual assessment of the specular suppressed endoscopic imagery produced by the proposed method.