Published Paper Details:

The rapid advancements in medical imaging and computational techniques have significantly improved brain tumor diagnosis and treatment. OpenCV, an open-source computer vision library, has emerged as a pivotal tool in enhancing brain tumor image segmentation. This review explores the recent developments in utilizing OpenCV for brain tumor segmentation, highlighting the integration of traditional image processing methods and modern machine learning algorithms. Key techniques such as thresholding, edge detection, and morphological operations are discussed in the context of their implementation via OpenCV. Additionally, the paper addresses the challenges encountered, including the variability in tumor appearance, the need for high computational resources, and the necessity for large annotated datasets. The review concludes by identifying future directions, emphasizing the potential of OpenCV in combination with deep learning frameworks to achieve more accurate and efficient brain tumor segmentation.