原发性肺癌磨玻璃样病变形态提取

H. A. Nugroho, M. M. Sebatubun, T. B. Adji
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引用次数: 2

摘要

在确定肺癌肿瘤恶性程度时,需要认识到肺部病变的几个特征。特征包括几个组成部分,即肿瘤大小、增强、不规则的针状边缘、分叶状、空气支气管征、磨玻璃不透明(GGO)和密度。本研究使用从印度尼西亚Sardjito公立医院获得的CT图像数据集确定GGO病变特征。最初的阶段是由放射科医生进行裁剪过程,这样研究的重点就只放在病变上。接下来是利用灰度共生矩阵(GLCM)的能量、对比度、相关性和均匀性四个特征进行特征提取。在提取阶段之后进行分类阶段,然后进行特征选择。该方法从16个特征中选择了两个最显著的特征,准确率为88.8%,灵敏度为87.5%,特异性为90%。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Ground glass opacity lesion morphology extraction in primary lung cancer
In determining the level of tumour malignancy in lung cancer, several characteristics of lesion in the lungs need to be recognised. The characteristics include several components, namely tumour size, enhancement, irregular spiculated edge, lobulated, air bronchograms, ground glass opacity (GGO) and density. This study identifies GGO lesion characteristics using CT image datasets obtained from Sardjito Public Hospital, Indonesia. The initial stage conducted is a cropping process performed by a radiologist so that the research's focus is merely on the lesion. The next process is the feature extraction by using grey level co-occurrence matrices (GLCM) with four features, namely energy, contrast, correlation and homogeneity. The classification stage is carried out after the extraction stage which is followed by features selection. Having selected two most dominant features from total of 16 features, the proposed method achieves accuracy of 88.8%, sensitivity of 87.5% and specificity of 90%.
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