Conditional spatial biased intuitionistic clustering technique for brain MRI image segmentation

IF 2.1 4区 医学 Q2 MATHEMATICAL & COMPUTATIONAL BIOLOGY Frontiers in Computational Neuroscience Pub Date : 2024-06-28 DOI:10.3389/fncom.2024.1425008
Jyoti Arora, Ghadir Altuwaijri, Ali Nauman, Meena Tushir, Tripti Sharma, Deepali Gupta, Sung Won Kim
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Abstract

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.
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用于脑磁共振成像图像分割的条件空间偏向直觉聚类技术
在临床研究中,分割脑磁共振(MR)图像对研究大脑内部组织至关重要。为了以可持续的方式应对这一挑战,有人提出了一种新方法,利用无监督聚类的力量,同时将图像的条件空间属性整合到直觉聚类技术中,用于分割脑部扫描的磁共振图像。在所提出的技术中,基于直觉的聚类方法结合了对图像数据内在不确定性的细致理解。不确定性的度量是通过计算犹豫度来实现的。该方法在引入直觉成员矩阵的同时,还引入了条件空间函数,从而能够考虑图像内部的空间关系。此外,通过计算加权直观成员矩阵,该算法还能根据局部环境调整其平滑行为。该算法的主要优点是增强了同质片段的鲁棒性,降低了对噪声、强度不均匀性的敏感性,并适应了现实世界数据集中可能存在的犹豫或不确定性。通过对磁共振脑图像的合成数据集和真实数据集进行比较分析,证明了所建议的方法比不同算法更有效。论文研究了所建议的研究方法在不同情况下在医疗行业中的表现,包括定性和定量参数,如分割准确率、相似性指数、真阳性率、假阳性率。实验结果表明,建议的算法在保留图像细节和实现分割准确性方面表现出色。
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来源期刊
Frontiers in Computational Neuroscience
Frontiers in Computational Neuroscience MATHEMATICAL & COMPUTATIONAL BIOLOGY-NEUROSCIENCES
CiteScore
5.30
自引率
3.10%
发文量
166
审稿时长
6-12 weeks
期刊介绍: Frontiers in Computational Neuroscience is a first-tier electronic journal devoted to promoting theoretical modeling of brain function and fostering interdisciplinary interactions between theoretical and experimental neuroscience. Progress in understanding the amazing capabilities of the brain is still limited, and we believe that it will only come with deep theoretical thinking and mutually stimulating cooperation between different disciplines and approaches. We therefore invite original contributions on a wide range of topics that present the fruits of such cooperation, or provide stimuli for future alliances. We aim to provide an interactive forum for cutting-edge theoretical studies of the nervous system, and for promulgating the best theoretical research to the broader neuroscience community. Models of all styles and at all levels are welcome, from biophysically motivated realistic simulations of neurons and synapses to high-level abstract models of inference and decision making. While the journal is primarily focused on theoretically based and driven research, we welcome experimental studies that validate and test theoretical conclusions. Also: comp neuro
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