An Overview of Medical Image Segmentation Algorithms

Abstract

Many medical imaging applications rely on image segmentation to automate or facilitate the delineation of anatomical features and other regions of interest. The results of segmentation have a significant impact on all subsequent image analysis operations, including object representation and description, feature measurement, and even higher-level tasks like object classification. As a result, image segmentation is the most important and fundamental step in aiding the identification, characterization, and visualization of regions of interest in any medical image. The radiologist's manual segmentation of the medical image is not only a time-consuming and inefficient procedure, but it is also inaccurate, especially with the growing number of medical imaging modalities and the unmanageable number of medical images to be analyzed. It is also vital to assess current picture segmentation approaches, especially for medical images, to ensure that automated algorithms are accurate and require as little user intervention as possible. The anatomical structure or region of interest must be delineated and extracted during the segmentation process so that it can be seen separately. This study focuses on the concept that underpins the basic approaches used. Image segmentation may be divided into two types: semi-interactive and entirely automatic, and the techniques developed fit into either of these categories.