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首页> 外文会议>Computer-Aided Diagnosis pt.2; Progress in Biomedical Optics and Imaging; vol.8,no.33; Proceedings of SPIE-The International Society for Optical Engineering; vol.6514 pt.2 >Texture-Based Computed-Aided Diagnosis System for Lung Fibrosis
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Texture-Based Computed-Aided Diagnosis System for Lung Fibrosis

机译：基于纹理的肺纤维化计算机辅助诊断系统

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摘要

Computer-aided detection of lung fibrosis remains a difficult task due to the small vascular structures, scars, and fibrotic tissues that need to be identified and differentiated. In this paper, we present a texture-based computer-aided diagnosis (CAD) system that automatically detects lung fibrosis. Our system uses high-resolution computed tomography (HRCT), advanced texture analysis, and support vector machine (SVM) committees to automatically and accurately detect lung fibrosis. Our CAD system follows a five-stage pipeline that is comprised of: segmentation, texture analysis, training, classification, and display. Since the accuracy of the proposed texture-based CAD system depends on how precise we can distinguish texture dissimilarities between normal and abnormal lungs, in this paper we have given special attention to the texture block selection process. We present the effects that texture block size, data reduction techniques, and image smoothing filters have within the overall classification results. Furthermore, a histogram-based technique to refine the classification results inside texture blocks is presented. The proposed texture-based CAD system to detect lung fibrosis has been trained with several normal and abnormal HRCT studies and has been tested with the original training dataset as well as new HRCT studies. On average, when using the suggested/default texture size and an optimized SVM committee system, a 90% accuracy has been observed with the proposed texture-based CAD system to detect lung fibrosis.
机译：由于需要识别和区分小的血管结构，疤痕和纤维化组织，因此计算机辅助检测肺纤维化仍然是一项艰巨的任务。在本文中，我们提出了一种基于纹理的计算机辅助诊断（CAD）系统，该系统可自动检测肺纤维化。我们的系统使用高分辨率计算机断层扫描（HRCT），高级纹理分析和支持向量机（SVM）委员会来自动，准确地检测肺纤维化。我们的CAD系统遵循五个阶段的流程，该流程包括：分割，纹理分析，训练，分类和显示。由于所提出的基于纹理的CAD系统的准确性取决于我们可以区分正常肺和异常肺之间纹理差异的精确程度，因此在本文中，我们特别关注了纹理块的选择过程。我们介绍了纹理块大小，数据缩减技术和图像平滑滤波器在整体分类结果中所具有的效果。此外，提出了一种基于直方图的技术来细化纹理块内部的分类结果。拟议的基于纹理的CAD系统检测肺纤维化已通过数项正常和异常HRCT研究进行了培训，并已通过原始训练数据集和新的HRCT研究进行了测试。平均而言，当使用建议的/默认纹理大小和优化的SVM委员会系统时，使用基于纹理的CAD系统检测肺纤维化的准确性已达到90％。

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