CAJ | 학술논문

本文是以DICOM标准的肺部序列影像为研究对象，将CT图像序列分割提取获得肺实质模块，再获取种子区域进行优化分割，最后通过ROI检测提取肺部特征信息并进行分类，从而达到肺结节ROI自动检测的目的。实验结果表明本文算法对微小结节特别是3m m以下的结节敏感性不高，而直径大于5m m的结节检出较为准确。实验中出现假阳性结节的个数较多，说明所选特征向量与判别分类标准比较严格，分类器的一些参数需要进一步优化，以达到更高的检出率及更低的漏检率。
본문시이DICOM표준적폐부서렬영상위연구대상，장CT도상서렬분할제취획득폐실질모괴，재획취충자구역진행우화분할，최후통과ROI검측제취폐부특정신식병진행분류，종이체도폐결절ROI자동검측적목적。실험결과표명본문산법대미소결절특별시3m m이하적결절민감성불고，이직경대우5m m적결절검출교위준학。실험중출현가양성결절적개수교다，설명소선특정향량여판별분류표준비교엄격，분류기적일사삼수수요진일보우화，이체도경고적검출솔급경저적루검솔。
This paper is based on lung image sequence of DICOM standard as the research object, segmenting a sequence of CT image for lung parenchyma, then getting the seed region were again optimized segmentation, and finaly being detected by ROI extraction and classification of lung feature information, so achieve the automatic detection of ROI lung nodules. Experimental results show that for the proposed algorithm nodules sensitivity is not high, especially less than 3mm tiny nodules, and the detection of the diameter greater than 5mm nodules is more accurate, the number of false-positive nodules in the experiment are larger, indicating the selected feature vectors and discriminate classification criteria is stricter, and the classifier needs to optimize some parameters in order to achieve a higher detection rate and lower missed rate.