Biomedical segmentation refers to the process of partitioning or dividing medical images into meaningful and distinct regions or structures. This segmentation is a crucial step in medical image analysis and is widely used in various applications such as disease diagnosis, treatment planning, and medical research. The goal is to identify and delineate different anatomical structures or regions of interest within medical images accurately. Various algorithms are employed for the actual segmentation of biomedical images. These algorithms can be broadly categorized into two types:
- Traditional Methods: These include techniques such as thresholding, region-based methods, and edge-based methods. Thresholding involves setting a specific intensity value to distinguish between different tissues or structures. Region-based methods group pixels based on certain criteria, while edge-based methods identify boundaries between different regions.
- Machine Learning and Deep Learning: With advancements in artificial intelligence, machine learning (ML) and deep learning (DL) techniques, especially convolutional neural networks (CNNs), have gained prominence. These methods can automatically learn and adapt to the complex patterns and variations in medical images, making them particularly effective for segmentation tasks. Biomedical segmentation plays a vital role in modern healthcare, aiding clinicians and researchers in understanding and analyzing medical images to make informed decisions for patient care and advancing medical knowledge.
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