Medical images are essential in contemporary medicine because they provide practicable entropy, which is used to diagnose medical conditions. It is useful to visualize abnormality in several parts of the body. Image segmentation in the medical has an important function in various applications in diagnosis systems. Researchers have become interested in segmentation algorithms as a result of Computed Tomography (CT) and Magnetic Resonance Imaging (MRI). The Region of Interest (ROI) extracts used in medical applications depend heavily on processes including cancer identification, bulk detection, and organ segmentation. Due to its capacity to deal with uncertainty and imprecision, Neutrosophic image processing (NIP) is a significant domain for uncertainty in medical image processing. Its methods in medicine demonstrate their transcendence. In the suggested work, the primary medical domains that NIP can create for image segmentation from DICOM pictures are highlighted. Due to the way it handles uncertain information, it has been found to be a better method.