Arora Jyoti, Altuwaijri Ghadir, Nauman Ali, Tushir Meena, Sharma Tripti, Gupta Deepali, Kim Sung Won
MSIT, New Delhi, India.
Department of Computer Engineering, College of Computer and Information Sciences, Majmaah University, Majmaah, Saudi Arabia.
Front Comput Neurosci. 2024 Jun 28;18:1425008. doi: 10.3389/fncom.2024.1425008. eCollection 2024.
In clinical research, it is crucial to segment the magnetic resonance (MR) brain image for studying the internal tissues of the brain. To address this challenge in a sustainable manner, a novel approach has been proposed leveraging the power of unsupervised clustering while integrating conditional spatial properties of the image into intuitionistic clustering technique for segmenting MRI images of brain scans. In the proposed technique, an Intuitionistic-based clustering approach incorporates a nuanced understanding of uncertainty inherent in the image data. The measure of uncertainty is achieved through calculation of hesitation degree. The approach introduces a conditional spatial function alongside the intuitionistic membership matrix, enabling the consideration of spatial relationships within the image. Furthermore, by calculating weighted intuitionistic membership matrix, the algorithm gains the ability to adapt its smoothing behavior based on the local context. The main advantages are enhanced robustness with homogenous segments, lower sensitivity to noise, intensity inhomogeneity and accommodation of degree of hesitation or uncertainty that may exist in the real-world datasets. A comparative analysis of synthetic and real datasets of MR brain images proves the efficiency of the suggested approach over different algorithms. The paper investigates how the suggested research methodology performs in medical industry under different circumstances including both qualitative and quantitative parameters such as segmentation accuracy, similarity index, true positive ratio, false positive ratio. The experimental outcomes demonstrate that the suggested algorithm outperforms in retaining image details and achieving segmentation accuracy.
在临床研究中，对磁共振（MR）脑图像进行分割对于研究脑内部组织至关重要。为了以可持续的方式应对这一挑战，人们提出了一种新颖的方法，该方法利用无监督聚类的能力，同时将图像的条件空间属性整合到直觉聚类技术中，以分割脑部扫描的MRI图像。在所提出的技术中，基于直觉的聚类方法纳入了对图像数据中固有不确定性的细微理解。不确定性的度量是通过计算犹豫度来实现的。该方法在直觉隶属矩阵的基础上引入了一个条件空间函数，从而能够考虑图像中的空间关系。此外，通过计算加权直觉隶属矩阵，该算法能够根据局部上下文调整其平滑行为。主要优点包括增强了同质子区域的鲁棒性，对噪声、强度不均匀性的低敏感性以及对现实世界数据集中可能存在的犹豫度或不确定性的适应性。对MR脑图像的合成数据集和真实数据集进行的对比分析证明了所提方法相对于不同算法的有效性。本文研究了所建议的研究方法在不同情况下（包括定性和定量参数，如分割精度、相似性指数、真阳性率、假阳性率）在医疗行业中的表现。实验结果表明，所建议的算法在保留图像细节和实现分割精度方面表现更优。
