Preprocessing Techniques for Optimizing Mammograms Segmentation: a Cellular Automaton Approach

Breast cancer is the most commonly diagnosed cancer among women worldwide, with early detection playing an essential role in improving survival rates. Detection of breast abnormalities at an early stage is best performed using mammography. This paper presents a new approach integrating advanced preprocessing techniques based on Cellular Automaton. It was applied on the Mini-MIAS dataset, and its performance led to improvements in the preprocessing and segmentation phases. The inclusion of Cellular Automaton and Fuzzy Logic facilitated more precise classification of pixels associated with regions of interest. The experimental results show that the proposed method improves segmentation performance, achieving 99.56% accuracy and producing segmentation results that closely align with the ground truth.