Brain MR images are composed of three main regions such as gray matter, white matter and cerebrospinal fluid. Radiologists and medical practitioners make decisions through evaluating the developments in these regions. Study of these MR images suffers from two major issues such as: (a) the boundaries of their gray matter and white matter regions are ambiguous and unclear in nature, and (b) their regions are formed with unclear inhomogeneous gray structures. These two issues make the diagnosis of critical diseases very complex. To solve these issues, this study presented a method of image segmentation based on the neutrosophic set (NS) theory and neutrosophic entropy information (NEI). By nature, the proposed method is adaptive to select the threshold value and is entitled as neutrosophic-entropy based adaptive thresholding segmentation algorithm (NEATSA). In this study, experimental results were provided through the segmentation of Parkinson's disease (PD) MR images. Experimental results, including statistical analyses showed that NEATSA can segment the main regions of MR images very clearly compared to the well-known methods of image segmentation available in literature of pattern recognition and computer vision domains.

脑部MR图像由灰质、白质和脑脊液三个主要区域组成。放射科医生和医生通过评估这些地区的发展来做出决定。对这些 MR 图像的研究存在两个主要问题，例如：（a）它们的灰质和白质区域的边界在本质上是模糊不清的；（b）它们的区域形成了不清晰的不均匀的灰色结构。这两个问题使危重疾病的诊断变得非常复杂。为了解决这些问题，本研究提出了一种基于中智集（NS）理论和中智熵信息（NEI）的图像分割方法。从本质上讲，所提出的方法可以自适应地选择阈值，并被称为基于中智熵的自适应阈值分割算法 (NEATSA)。在这项研究中，通过分割帕金森病 (PD) MR 图像提供了实验结果。包括统计分析在内的实验结果表明，与模式识别和计算机视觉领域文献中可用的众所周知的图像分割方法相比，NEATSA 可以非常清楚地分割 MR 图像的主要区域。