This study investigates the development of an automated image analysis method for assessing the structural quality of gluten-free bread enriched with acorn flour (Quercus rotundifolia L.). The unique techno-functional properties of acorn flour in bread dough pose significant challenges for bakery and pastry applications, making this research timely and relevant. The primary objective of this study is to evaluate key structural parameters of bread, including void count, average void size, and porosity, using various image segmentation algorithms. The algorithms tested include Default, IsoData, Li, Moments, and Otsu, with IsoData identified as the most stable and reliable for this application. The results demonstrate that increasing the acorn flour content leads to higher void count, larger void size, and increased porosity, with 100% acorn flour showing the most pronounced effects. Additionally, increasing the water content in the dough reduces void count while increasing void size and porosity, further highlighting the role of acorn flour in influencing bread texture and structure. These findings underscore the potential of acorn flour to improve the quality of gluten-free bread and contribute to the development of healthier, innovative bakery products. This study offers valuable insights into optimizing the use of acorn flour in bakery applications and promotes its potential as a sustainable ingredient in gluten-free and functional food products.