Federal University of Maranhão - UFMA, Applied Computing Group - NCA, Av. dos Portugueses, SN, Campus do Bacanga, Bacanga, São Luís, MA, 65085-580, Brazil.
Federal University of Piauí - UFPI, Rua Cícero Duarte, SN, Campus de Picos, Junco, Picos, PI, 64600-000, Brazil.
Med Biol Eng Comput. 2018 Nov;56(11):2125-2136. doi: 10.1007/s11517-018-1841-0. Epub 2018 May 23.
Lung cancer presents the highest cause of death among patients around the world, in addition of being one of the smallest survival rates after diagnosis. Therefore, this study proposes a methodology for diagnosis of lung nodules in benign and malignant tumors based on image processing and pattern recognition techniques. Mean phylogenetic distance (MPD) and taxonomic diversity index (Δ) were used as texture descriptors. Finally, the genetic algorithm in conjunction with the support vector machine were applied to select the best training model. The proposed methodology was tested on computed tomography (CT) images from the Lung Image Database Consortium and Image Database Resource Initiative (LIDC-IDRI), with the best sensitivity of 93.42%, specificity of 91.21%, accuracy of 91.81%, and area under the ROC curve of 0.94. The results demonstrate the promising performance of texture extraction techniques using mean phylogenetic distance and taxonomic diversity index combined with phylogenetic trees. Graphical Abstract Stages of the proposed methodology.
肺癌是全球患者死亡的主要原因之一，也是诊断后存活率最低的癌症之一。因此，本研究提出了一种基于图像处理和模式识别技术的肺结节良恶性肿瘤诊断方法。采用平均系统发育距离（MPD）和分类多样性指数（Δ）作为纹理描述符。最后，应用遗传算法和支持向量机选择最佳训练模型。该方法在肺影像数据库联盟和影像数据库资源倡议（LIDC-IDRI）的 CT 图像上进行了测试，最佳灵敏度为 93.42%，特异性为 91.21%，准确率为 91.81%，ROC 曲线下面积为 0.94。结果表明，基于平均系统发育距离和分类多样性指数的纹理提取技术与系统发育树相结合具有良好的性能。
