Three-dimensional (3D) reconstruction of an organ or tissue from a stack of histologic serial sections provides valuable morphological information. The procedure includes section preparation of the organ or tissue, micrographs acquisition, image registration, 3D reconstruction, and visualization. However, the brightness and contrast through the image stack may not be consistent due to imperfections in the staining procedure, which may cause difficulties in micro-structure identification using virtual sections, region segmentation, automatic target tracing, etc. In the present study, a reference-free method, Sequential Histogram Fitting Algorithm (SHFA), is therefore developed for adjusting the severe and irregular variance of brightness and contrast within the image stack. To apply the SHFA, the gray value histograms of individual images are first calculated over the entire image stack and a set of landmark gray values are chosen. Then the histograms are transformed so that there are no abrupt changes in progressing through the stack. Finally, the pixel gray values of the original images are transformed into the desired ones based on the relationship between the original and the transformed histograms. The SHFA is tested on an image stacks from mouse kidney sections stained with toluidine blue, and captured by a slide scanner. As results, the images through the entire stack reveal homogenous brightness and consistent contrast. In addition, subtle color differences in the tissue are well preserved so that the morphological details can be recognized, even in virtual sections. In conclusion, compared with the existing histogram-based methods, the present study provides a practical method suitable for compensating brightness, and improving contrast of images derived from a large number of serial sections of biological organ.