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An efficient automated methodology for detecting and segmenting the ischemic stroke in brain MRI images

机译:一种有效的自动化方法,用于检测和分割脑部MRI图像中的缺血性卒中

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摘要

Brain tumor and brain stroke are two important causes of death in and around the world. The abnormalities in brain cell leads to brain stroke and obstruction in blood flow to brain cells leads to brain stroke. In this article, a computer aided automatic methodology is proposed to detect and segment ischemic stroke in brain MRI images using Adaptive Neuro Fuzzy Inference (ANFIS) classifier. The proposed method consists of preprocessing, feature extraction and classification. The brain image is enhanced using Heuristic histogram equalization technique. Then, texture and morphological features are extracted from the preprocessed image. These features are optimized using Genetic Algorithm and trained and classified using ANFIS classifier. The performance of the proposed ischemic stroke detection system is analyzed in terms of sensitivity, specificity, accuracy, positive predictive value, negative predictive value, and Mathew's correlation coefficient.
机译:脑瘤和脑中风是世界范围内以及世界范围内两个重要的死亡原因。脑细胞异常会导致脑中风,流向脑细胞的血液阻塞会导致脑中风。在本文中,提出了一种计算机辅助自动方法,该方法使用自适应神经模糊推理(ANFIS)分类器来检测和分割脑MRI图像中的缺血性卒中。所提出的方法包括预处理,特征提取和分类。使用启发式直方图均衡技术增强大脑图像。然后,从预处理后的图像中提取纹理和形态特征。这些特征使用遗传算法进行了优化,并使用ANFIS分类器进行了训练和分类。从敏感性,特异性,准确性,阳性预测值,阴性预测值和Mathew相关系数方面分析了拟议的缺血性卒中检测系统的性能。

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