基于多特征融合跟踪的微小肺结节识别算法

Detection of Lung Mini-nodules Using Multi-feature Tracking

如何在海量的肺部高分辨率CT(HRCT)序列图片中准确识别微小结节(直径为5～10 mm)一直是肺结节计算机辅助检测(CAD)系统的研究重点和难点。本文提出了一种新的微小肺结节识别算法——多特征融合跟踪算法。该算法在处理一个HRCT序列图片时,首先结合大津法和形态学方法获取每一张CT图的肺实质,再通过基于灰度阈值和改进的模板匹配算法提取感兴趣区域(ROI),接着计算ROI的多个有效特征,然后在整个HRCT序列图片中进行ROI的多特征跟踪和融合,最后根据分类规则识别并标出候选肺结节。实验证明,该算法能准确地检测出微小肺结节,且假阳率较低。

How to accurately identify mini-nodules in a large amount of high resolution computed tomography（HRCT） images is always a significant and difficult issue in lung nodule computer-aided detection（CAD）.This paper describes a new mini-nodules detection method which is based on a multi-feature tracking algorithm.Our detection method began after running the Da-Jing algorithm and morphological operation to extract the lung region of every HRCT image in a sequence.Once the lung had been extracted,a hybrid algorithm,combining gray threshold and improved template matching,was used to obtain the regions of interest（ROI）.Next,several characteristics of each ROI were calculated to identify the final results by using multi-feature tracking throughout the whole HRCT image sequence.The results showed that the proposed method would be of high accuracy with a low occurrence of false positives.