Evolutionary Feature Construction for Ultrasound Image Processing and its Application to Automatic Liver Disease Diagnosis

Yu-Hsiang Wu, Jhu-Yun Huang, Shyi-Chyi Cheng, Chen-Kuei Yang, C. Lin
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引用次数: 16

Abstract

In this paper, the self organization properties of genetic algorithms are employed to tackle the problem of feature selection and extraction in ultrasound images, which can facilitate early disease detection and diagnosis. Accurately identifying the aberrant features at a particular location of clinical ultrasound images is important to find the possibly damaged tissues. Unfortunately, it is difficult to exactly detect the regions of interest (ROIs) from relatively low quality of clinical ultrasound images. The presented evolutionary optimization algorithm presents a novel approach to building features for automatic liver cirrhosis diagnosis using a genetic algorithm. The extracted features provide several advantages over other feature extraction techniques which include: automatically construct feature set and tune their parameters, ability to integrate multiple feature sets to improve the diagnosis accuracy, and ability to find local ROIs and integrate their local features into effective global features. As compared with past approaches, we span a new way to unify the processing steps in a clinical application using the evolutionary optimization algorithms for ultrasound images. Experimental results show the effectiveness of the proposed method.
超声图像处理的进化特征构建及其在肝脏疾病自动诊断中的应用
本文利用遗传算法的自组织特性来解决超声图像的特征选择和提取问题,有利于疾病的早期发现和诊断。准确识别临床超声图像特定部位的异常特征对于发现可能的损伤组织具有重要意义。不幸的是,很难从相对较低质量的临床超声图像中准确检测感兴趣区域(roi)。提出了一种利用遗传算法构建肝硬化自动诊断特征的新方法。与其他特征提取技术相比,所提取的特征具有自动构建特征集并调整其参数的优点,能够整合多个特征集以提高诊断准确性,能够发现局部roi并将其局部特征集成到有效的全局特征中。与过去的方法相比,我们跨越了一种新的方法,使用超声图像的进化优化算法来统一临床应用中的处理步骤。实验结果表明了该方法的有效性。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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