Mathematical Morphology

Mathematical morphology is a branch of image analysis employing set theory and lattice theory to analyze shapes and structures within images. Current research focuses on integrating morphological operators with deep learning architectures, such as convolutional neural networks and transformers, to improve image segmentation, classification, and feature extraction in diverse fields like medical imaging, astronomy, and agriculture. These advancements enhance the accuracy and efficiency of automated analysis, impacting applications ranging from disease diagnosis to high-throughput phenotyping and astronomical object classification.