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首页> 外文期刊>Journal of Engineering & Applied Sciences >Shape-based Automated Classification of Subdural and Extradural Hematomas
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Shape-based Automated Classification of Subdural and Extradural Hematomas

机译：基于形状的硬膜上和外血肿自动分类

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摘要

This study reports the classification of subdural and extradural hematomas in brain CT images. The major difference between subdural and extradural hematomas lies in their shapes, therefore eight shape descriptors are proposed to describe the characteristics of the two types of hematoma. The images will first undergo the pre-processing step which consists of two-level contrast enhancement separated by parenchyma extraction processes. Next, k-means clustering is performed to garner all Regions of Interest (ROIs) into one cluster. Prior to classification, shape features are extracted from each ROI. Finally for classification, fuzzy k-Nearest Neighbor (fuzzy k-NN) and Linear Discriminant Analysis (LDA) are employed to classify the regions into subdural hematoma, extradural hematoma or normal regions. Experimental results suggest that fuzzy k-NN produces the optimum accuracy. It manages to achieve over 93% correct classification rate on a set of 109 subdural and 247 extradural hematoma regions, as well as 629 normal regions.
机译：本研究报告了脑CT图像中子宫内容和外血肿的分类。硬膜上和外血肿之间的主要区别在于它们的形状，因此提出了八种形状描述符来描述两种类型的血肿的特征。图像将首先经过预处理步骤，该步骤由经实质提取过程分开的两级对比度增强。接下来，执行K-means聚类以将所有感兴趣区域（rois）加入一个群集。在分类之前，从每个ROI中提取形状特征。最后用于分类，采用模糊k最近邻（模糊K-NN）和线性判别分析（LDA）将区域分类为软骨血肿，外血肿或普通区域。实验结果表明模糊K-NN产生最佳精度。它可以在一组109个硬脑膜和247个外血肿地区以及629个正常地区实现超过93％的正确分类率。

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