Oral leukoplakia (OL) evaluation through photographs can be performed with the aid of Artificial Intelligence (AI). Supervised Machine Learning (SML) processes, which are based on labeling, are indicated to ensure a reliable computational mechanism of lesion identification. Thus, OL classification and demarcation within a photograph are crucial for SML. Objective: To label OL lesions in homogeneous and non-homogeneous using photographs, and to test a segmentation procedure, aiming for its use in a trustworthy dataset. Methods: Fifty-five OL photographs were inserted into Fiji/ImageJ, and a region of interest (ROI) was defined to obtain a three-dimensional plot of pixel color clustering.