Correlation coefficient based supervised locally linear embedding for pulmonary nodule recognition.
Correlation coefficient based supervised locally linear embedding for pulmonary nodule recognition.
复制标题
基于相关系数监督的局部线性嵌入的肺结节识别
DOI：
10.1016/j.cmpb.2016.08.009
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发表时间：
2016-11
影响因子：
6.1
通讯作者：
Yu H
中科院分区：
文献类型：
--
作者：
Wu P;Xia K;Yu H
为了抑制肺部CT图像高维特征空间对肺结节计算机辅助检测(CAD)系统分类性能的负面影响，提出了一种降维技术。基于相关系数的概念，提出了一种改进的有监督局部线性嵌入算法。在SLLE算法中引入Spearman等级相关系数来调整距离度量，以确保能够识别出更合适的邻近点，从而增强嵌入数据的区分能力。本文提出的基于Spearman等级相关系数的SLLE算法(SC2SLLE)在我们的试点CAD系统中得到了实现，并在我们的试点CAD系统中得到了验证，该算法使用的临床数据集来自公共可用的肺图像数据库联盟和图像数据库资源倡议(LICD-IDRI)。特别是，设计并实现了一个具有代表性的孤立性肺结节检测CAD系统。经过一系列的医学图像处理步骤，提取出结节和140个非结节，计算出34个具有代表性的特征。采用SC2SLLE、SLLE和LLE算法进行降维。还使用了几个定量测量来评估和比较性能。使用5次交叉验证方法，该算法平均获得87.65%的准确率、79.23%的敏感度、91.43%的特异度和8.57%的假阳性率。实验结果表明，该算法比传统的局部线性嵌入和SLLE结合支持向量机分类器具有更好的分类性能。基于我们数据集中有限数量的结核的初步结果，这项研究证明了使用所建议的SC2SLLE来提高用于结核检测的CAD系统的性能的巨大潜力。
Dimensionality reduction techniques are developed to suppress the negative effects of high dimensional feature space of lung CT images on classification performance in computer aided detection (CAD) systems for pulmonary nodule detection. An improved supervised locally linear embedding (SLLE) algorithm is proposed based on the concept of correlation coefficient. The Spearman’s rank correlation coefficient is introduced to adjust the distance metric in the SLLE algorithm to ensure that more suitable neighborhood points could be identified, and thus to enhance the discriminating power of embedded data. The proposed Spearman’s rank correlation coefficient based SLLE (SC2SLLE) is implemented and validated in our pilot CAD system using a clinical dataset collected from the publicly available lung image database consortium and image database resource initiative (LICD-IDRI). Particularly, a representative CAD system for solitary pulmonary nodule detection is designed and implemented. After a sequential medical image processing steps, 64 nodules and 140 non-nodules are extracted, and 34 representative features are calculated. The SC2SLLE, as well as SLLE and LLE algorithm are applied to reduce the dimensionality. Several quantitative measurements are also used to evaluate and compare the performance. Using a 5-fold cross-validation methodology, the proposed algorithm achieves 87.65% accuracy, 79.23% sensitivity, 91.43% specificity, and 8.57% false positive rate, on average. Experimental results indicate that the proposed algorithm outperforms the original locally linear embedding and SLLE coupled with the support vector machine (SVM) classifier. Based on the preliminary results from a limited number of nodules in our dataset, this study demonstrates the great potential to improve the performance of a CAD system for nodule detection using the proposed SC2SLLE.
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