Cervicography is a technique for visual screening of uterine cervix images for cervical cancer. One of our research goals is the automated detection in these images of acetowhite (AW) lesions, which are sometimes correlated with cervical cancer. These lesions are characterized by the whitening of regions along the squamocolumnar junction on the cervix when treated with 5% acetic acid. Image preprocessing is required prior to invoking AW detection algorithms on cervicographic images for two reasons: (1) to remove Specular Reflections (SR) caused by camera flash, and (2) to isolate the cervix region-of-interest (ROI) from image regions that are irrelevant to the analysis. These image regions may contain medical instruments, film markup, or other non-cervix anatomy or regions, such as vaginal walls. We have qualitatively and quantitatively evaluated the performance of alternative preprocessing algorithms on a test set of 120 images. For cervix ROI detection, all approaches use a common feature set, but with varying combinations of feature weights, normalization, and clustering methods. For SR detection, while one approach uses a Gaussian Mixture Model on an intensity/saturation feature set, a second approach uses Otsu thresholding on a top-hat transformed input image. Empirical results are analyzed to derive conclusions on the performance of each approach.