Medical Image Segmentation Methods, Algorithms, and Applications

TLDR

Medical images have greatly impacted medicine, diagnosis, and treatment, with image segmentation being a crucial component of image processing. The paper describes the latest segmentation methods applied in medical image analysis. The authors review each segmentation algorithm, detailing its advantages, disadvantages, application to MRI and CT, and features for grey‑level images, and evaluate results using popular benchmark metrics. The paper presents many image segmentation methods for medical image analysis.

Abstract

Medical images have made a great impact on medicine, diagnosis, and treatment. The most important part of image processing is image segmentation. Many image segmentation methods for medical image analysis have been presented in this paper. In this paper, we have described the latest segmentation methods applied in medical image analysis. The advantages and disadvantages of each method are described besides examination of each algorithm with its application in Magnetic Resonance Imaging and Computed Tomography image analysis. Each algorithm is explained separately with its ability and features for the analysis of grey-level images. In order to evaluate the segmentation results, some popular benchmark measurements are presented in the final section.