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A CAD System for Brain Haemorrhage Detection in Head CT Scans

机译:用于头部CT扫描中脑出血检测的CAD系统

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Medical imaging is an important medical diagnostic tool that provides visual information of the interior of the human body. In recent years computer-aided detection/diagnosis (CAD) systems became a key component of routine clinical practice in several medical areas, such as mammography and colonoscopy. However, research on brain CAD systems is still limited compared to other areas. This paper presents a solution that uses image processing techniques on brain computed tomography (CT) scans to create a CAD system that detects fresh bleeds. The system also features a basic classification method that distinguishes between an intra-axial and an extra-axial haemorrhage with the only limitation being subarachnoid haemorrhage (SAH), which is not always properly classified due to its complex structure. The techniques implemented include noise reduction methods, morphological operations and segmentation algorithms. The developed CAD system was tested on 36 brain CT sets obtained from the general hospital in Malta. Results show that the system achieves a sensitivity of 94.4%, a specificity of 94.4%, a precision of 91.259% and a classification accuracy of 88.89%.
机译:医学成像是提供人体内部视觉信息的重要医学诊断工具。近年来,计算机辅助检测/诊断(CAD)系统已成为乳腺X线摄影和结肠镜检查等多个医学领域常规临床实践的关键组成部分。但是,与其他领域相比,对大脑CAD系统的研究仍然很有限。本文提出了一种解决方案,该解决方案在脑计算机断层扫描(CT)扫描中使用图像处理技术来创建可检测新鲜出血的CAD系统。该系统还具有基本的分类方法,可以区分轴向内出血和轴向外出血,唯一的局限性是蛛网膜下腔出血(SAH),由于蛛网膜下腔出血(SAH)的结构复杂,因此无法始终对其进行正确分类。实施的技术包括降噪方法,形态学运算和分割算法。在从马耳他总医院获得的36台脑部CT装置上测试了开发的CAD系统。结果表明,该系统的灵敏度为94.4%,特异性为94.4%,精度为91.259%,分类精度为88.89%。

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