Integrating a Fuzzy Fitness Function in Genetic Programming to Generate Breast Tissue Segmentation Models

Abstract

Genetic programming (GP) and fuzzy logic are used to automatically segment mammography images. GP allows the evolution of optimized segmentation models, guided by a fuzzy logic-based fitness function that incorporates medical criteria to improve the consistency and accuracy of the segmentation process. Unlike conventional approaches, this function optimizes the segmentation and provides a descriptive representation of the breast tissue, allowing a closer evaluation to that performed by specialists. The proposed method was evaluated in the INbreast and BCDR databases, obtaining a Jaccard index of 0.82 and 0.78, respectively, and a comparative analysis was performed using ROC curves, reaching an AUC of 0.91 in INbreast and 0.89 in BCDR, demonstrating the model’s ability to discriminate between fibroglandular and fat tissue. Its performance was compared with state-of-the-art methods, such as LIBRA, hybrid segmentation with Fuzzy C-Means, and NASGP-Net, showing that integrating fuzzy logic in genetic programming to lead the search allows competitive results with a lower computational burden. These results demonstrate the impact of fuzzy fitness functions in the evolution of segmentation models, highlighting the effectiveness of this approach in improving the segmentation and classification of medical images, in addition to the descriptive capabilities inherent to the fuzzy fitness function.

Keywords

• Genetic programming
• fuzzy fitness function
• medical image segmentation
• breast density
• mammography