Age Differences Classification Associated with Corpus Callosum Measurement

Shafaf Ibrahim, Nur Aina Shahirah Mat Jelaini, Nor Azura Md. Ghani, R. Janor, Mohd Hanif Ali
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Abstract

A medical visualization is a tool used in medicine to detect aspects of the human body in terms of digital health. The corpus callosum is a large white matter structure that separates the two hemispheres of the brain. It is an extremely essential structural and functional component of the brain. Assessing the corpus callosum measurement could reveal the information on age differences category of each individual, as well as atypical growth such as multiple sclerosis (MS), Alzheimer’s, and autism spectrum disorder (ASD). Thus, this study proposed the use of Magnetic Resonance Imaging (MRI) sagittal brain images to classify age differences associated with corpus callosum measurement. Three age differences were studied; children (0-10 years), adolescent (10-18 years), and adult (18-25 years). The present results provided evidence that adult and children differ in terms of developmental trajectories for the brain structure, with significant age-related changes discernable from infancy to early adulthood. A few steps of MRI corpus callosum image collection, Median Filtering image enhancement, Otsu binarization, and K-Means clustering segmentation, corpus callosum measurement, and Support Vector Machine (SVM) classification were involved. The performance of the corpus callosum classification was evaluated using a confusion matrix. The overall mean percentage of accuracy reflected a very high accuracy which are 97.72%, 95.56%, and 97.72% for children, adolescent, and adult respectively. It can be deduced that the proposed techniques of corpus callosum classification are found to be successful.
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胼胝体测量的年龄差异分类
医学可视化是医学中用于检测人体数字健康方面的工具。胼胝体是一个巨大的白质结构,它将大脑的两个半球分开。它是大脑中极其重要的结构和功能组成部分。评估胼胝体测量可以揭示每个个体的年龄差异类别信息,以及非典型生长,如多发性硬化症(MS),阿尔茨海默氏症和自闭症谱系障碍(ASD)。因此,本研究提出使用磁共振成像(MRI)矢状脑图像来分类与胼胝体测量相关的年龄差异。研究了三种年龄差异;儿童(0-10岁)、青少年(10-18岁)和成人(18-25岁)。目前的结果提供了证据,证明成人和儿童在大脑结构的发育轨迹方面存在差异,从婴儿期到成年早期都有明显的年龄相关变化。涉及MRI胼胝体图像采集、中值滤波图像增强、Otsu二值化、K-Means聚类分割、胼胝体测量和支持向量机(SVM)分类等几个步骤。使用混淆矩阵评估胼胝体分类的性能。总体平均准确率反映了很高的准确率,儿童、青少年和成人的准确率分别为97.72%、95.56%和97.72%。可以推断,所提出的胼胝体分类技术是成功的。
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