Nodule detection on chest helical CT scans by using a genetic algorithm
Nodule detection on chest helical CT scans by using a genetic algorithm
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使用遗传算法对胸部螺旋 CT 扫描进行结节检测
DOI：
10.1109/iis.1997.645183
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发表时间：
1997
期刊：
影响因子：
--
通讯作者：
Takeo Ishigaki
中科院分区：
文献类型：
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作者：
Yongbum Lee;T. Hara;Hiroshi Fujita;Shigeki Itoh;Takeo Ishigaki
本研究的目的是应用遗传算法（GA）的模板匹配方法来检测肺部结节的胸部螺旋X射线CT（计算机断层扫描）图像。我们结合遗传算法和模板匹配来搜索结节的位置和计算个人的适应尺度的遗传算法，分别。我们使用四个模拟结节高斯分布，其大小不同，作为参考模式的遗传模板匹配。遗传算法从四幅图像中选择一幅合适的参考图像，搜索合适的位置进行模板匹配。我们使用互相关作为模板匹配的相似度和遗传算法中个体的适应尺度。从45个没有接触肺壁的结节中可以检测到23个，而不考虑它们的大小。通过使用沿沿着肺壁的常规模板匹配，也可以检测接触肺壁的所有结节。总检出率约为67%。每个切片的假阳性数量超过10。为了提高检测性能和减少误报率，我们现在正在考虑遗传算法的算子及其参数。
The purpose of the study is to apply a genetic algorithm (GA) template matching method to detect lung nodules in chest helical X ray CT (computed tomography) images. We combined GA and template matching to search the positions of nodules and to calculate adaptation scales of individuals on GA, respectively. We used four simulated nodules created by Gaussian distribution, whose sizes were different to each other, as reference patterns in the GA template matching. The GA selected an adequate reference image from four images and searched adequate positions to template matching. We used cross correlation as similarity of template matching and as adaptation scales of individuals on GA. It was possible to detect 23 nodules from 45 that did not touch the lung walls, without consideration of their sizes. It was also possible to detect all nodules that touched the lung walls by using conventional template matching along lung walls. The total detection rate was approximately 67%. The number of false positives per slice was over 10. To improve the detection performance and to decrease the number of false positives, we are now working on considering operators and their parameters of GA.