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Detection of juxta-pleural lung nodules in computed tomography images

机译:检测计算机断层扫描图像中的Juxta-胸膜肺结节

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摘要

A method for the detection of juxta-pleural lung nodules with radius ≤ 5mm in chest computed tomography images is proposed. The lung volume is segmented using region-growing and refined with morphological operations and active contours to include juxta-pleural nodules. Nodule candidates are searched slice-wise inside the lung volume segmentation. Solid nodules are detected by selecting an appropriate threshold inside a representative sliding window. Sub-solid and non-solid nodules are enhanced with a multiscale Laplacian-of-Gaussian filtering prior to their detection. Obvious non-nodule candidates, namely small blood vessels, are discarded using fixed rules. Then, a support vector machine with radial basis function is trained with the remaining candidates to further reduce the number of false positives (FPs). The final system sensitivity is 57.4% with 4 FPs/scan.The performance is similar or better than state-of-the-art methods, especially when considering the high number and small radius of the studied juxta-pleural nodules.
机译:提出了一种检测胸部计算机断层摄影图像中半径≤5mm的Juxta-β肺结节的方法。使用区域生长并用形态学作用和活性轮廓进行细化来分段,以包括Juxta-胸膜结节。结节候选人在肺部分段内搜索切片。通过在代表性滑动窗口内选择适当的阈值来检测固体结节。在其检测之前,通过多尺度Laplacian-of oussian滤波增强了亚固体和非固结节。明显的非结节候选者,即小血管,使用固定规则丢弃。然后,具有径向基函数的支持向量机接受剩余的候选培训,以进一步减少误报(FPS)的数量。最终系统灵敏度为57.4%,4 FPS /扫描。性能相似或优于最先进的方法,特别是在考虑研究的JUXTA-胸膜结节的高数量和小半径时。

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