基于神经模糊分类器的CT图像肺结节检测

A. Tariq, M. U. Akram, Muhammad Younus Javed
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引用次数: 80

摘要

基于计算机辅助诊断(CAD)的肺癌自动检测是临床应用的一个重要领域。由于人工检测非常耗时和昂贵,因此计算机化系统可以帮助实现这一目的。本文提出了一种计算机化的CT扫描图像肺结节检测系统。该自动化系统包括两个阶段:肺的分割和增强、特征提取和分类。分割过程将导致肺组织与图像的其余部分分离,并且仅将被检查的肺组织作为检测肺部分恶性结节的候选区域。计算可能异常区域的特征向量,并使用神经模糊分类器对可能异常区域进行分类。该系统是一个不需要人工干预的全自动系统,实验结果表明了系统的有效性。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Lung nodule detection in CT images using neuro fuzzy classifier
Automated lung cancer detection using computer aided diagnosis (CAD) is an important area in clinical applications. As the manual nodule detection is very time consuming and costly so computerized systems can be helpful for this purpose. In this paper, we propose a computerized system for lung nodule detection in CT scan images. The automated system consists of two stages i.e. lung segmentation and enhancement, feature extraction and classification. The segmentation process will result in separating lung tissue from rest of the image, and only the lung tissues under examination are considered as candidate regions for detecting malignant nodules in lung portion. A feature vector for possible abnormal regions is calculated and regions are classified using neuro fuzzy classifier. It is a fully automatic system that does not require any manual intervention and experimental results show the validity of our system.
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