Precisely identifying and delineating food regions automatically from images, a task known as food image segmentation, is crucial for enabling applications in food science such as automated dietary logging, accurate nutritional analysis, and food safety monitoring. However, accurately segmenting food images, particularly delineating food edges with precision, remains challenging due to the wide variety and diverse forms of food items, frequent inter-food occlusion, and ambiguous boundaries between food and backgrounds or containers. To overcome these challenges, we proposed a novel method called the Multi-view Edge Attention Network (MVEANet), which focuses on enhancing the fine-grained segmentation of food edges. The core idea behind this method is to integrate information obtained from observing food from different perspectives to achieve a more comprehensive understanding of its shape and specifically to strengthen the processing capability for food contour details. Rigorous testing on two large public food image datasets, FoodSeg103 and UEC-FoodPIX Complete, demonstrates that MVEANet surpasses existing state-of-the-art methods in segmentation accuracy, performing exceptionally well in depicting clear and precise food boundaries. This work provides the field of food science with a more accurate and reliable tool for automated food image segmentation, offering strong technical support for the development of more intelligent dietary assessment, nutritional research, and health management systems.