Edge Detection Using Watershed Algorithm for Polycystic Ovary Image Analysis: A Comprehensive Study

Polycystic ovary syndrome (PCOS) is a common endocrine disorder that affects reproductive-age women. Image analysis techniques, particularly edge detection, play a crucial role in PCOS diagnosis and treatment monitoring. This research paper presents a comprehensive study on edge detection using the Watershed algorithm for analyzing Polycystic Ovary (PCO) images. The paper explores the application of the Watershed algorithm in detecting and segmenting ovarian cysts, follicles, and other key structures in PCO images. Various approaches, modifications, and pre-processing techniques are investigated to enhance the performance of the Watershed algorithm in PCO image analysis. Additionally, the paper discusses the challenges, future directions, and potential applications of Watershed-based edge detection in PCOS research and clinical practice.