## 3 Results  
### Illumination Profile, Tissue Segmentation, and Transmittance Computation  
The initial step of the framework was the processing of the white images, acquired at the beginning of each acquisition session, which were interpolated to estimate the illumination profile of the sample. The light intensities of the acquired white images in bright-field (\(I_{p}^{0}\)) and dark-field (\(I_{c}^{0}\)) presented a relative standard deviation with respect to the mean \(>\)20% and \(>\)30%, respectively. The images \(P_{p}^{0}\) and \(P_{c}^{0}\) obtained after interpolation presented a reduced variability, with a relative residual standard deviation with respect to the mean of <0.5% and <10% for bright-field and dark-field images, respectively. The correlation coefficient between the original white images and the respective interpolated profiles was >0.99 and >0.90 for bright-field and dark-field cases, respectively. Thus, the interpolant function preserved the low-frequency trend of the illumination field, while reducing high-frequency noise and artifacts, which were especially relevant for the dark-field white images characterized by a lower signal-to-noise ratio.  
Following the processing of the white images, the analysis proceeded with the processing of the histological images. The first step of the procedure is exemplified in Figure 3 for a single section image. The RGB histograms (Figure 3a) of the original image \(A_{p}^{i}\) (Figure 3b) had values spread across the whole intensity range because of the nonuniform field illumination, which manifested as a decrease in exposure in the periphery of the image (i.e., vignetting effect). In the histograms, the values corresponding to the luminal space formed a low and wide peak with overlap with tissue values, which hindered an automatic threshold-based separation between tissue and lumen values. The white-balanced image \(B^{i}\) (Figure 3d) was obtained applying Equation (8), which reduced the non-uniform illumination of the lumen. Following white-balance correction, RGB histograms (Figure 3c) presented a bimodal distribution with a sharp lumen peak, suitable for automatic analysis. The correction reduced the variability of the lumen pixels for [R G B] channels (expressed as relative standard deviation with respect to the mean) from [25.3 23.9 24.9] % in \(A_{p}^{i}\) to [2.6 3.2 6.1] % in \(B^{i}\). The peaks corresponding to lumen and tissue areas were automatically detected (Figure 3c), allowing normalization of \(B^{i}\) with respect to the lumen RGB levels. The white-balanced image represented the transmittance \(T_{s}^{i}\) of the sample (Figure 3d). The luminal space was segmented from the tissue area of the image by thresholding at the minimum level (\(t_{seg}\)) between the peaks. The obtained tissue mask \(mask_{tis}^{i}\) is contoured in black in Figure 3d. In the shown case, the tissue area covered 90.1% of the image area. The automatic segmentation assumes that the lumen/background peak is separated by a valley from the tissue values, and this also requires a minimal lumen area in the image. In the analyzed database, the condition was met in all the images, which presented a luminal area > 5% in the field of view.