Aceto-white temporal pattern classification using k-NN to identify precancerous cervical lesion in colposcopic images
Aceto-white temporal pattern classification using k-NN to identify precancerous cervical lesion in colposcopic images
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DOI：
10.1016/j.compbiomed.2009.06.006
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发表时间：
2009-09-01
影响因子：
7.7
通讯作者：
Hernandez-Jimenez, Rodolfo
中科院分区：
文献类型：
--
作者：
Acosta-Mesa, Hector-Gabriel;Cruz-Ramirez, Nicandro;Hernandez-Jimenez, Rodolfo
在巴氏涂片检查之后，阴道镜检查是诊断宫颈癌最常用的技术，因为它具有更高的敏感性和特异性。改进阴道镜检查的最有前途的方法之一是使用数字图像中颜色变化固有的醋酸白色时间模式。然而，对于如何使用它们来分割阴道镜图像还没有完全的理解。在这项工作中，我们使用分类算法k-NN在整个长度的醋酸白色的时间模式自动区分正常和异常的宫颈组织，达到71%的灵敏度和59%的特异性。(C)2009年由Elsevier Ltd.出版
After Pap smear test, colposcopy is the most used technique to diagnose cervical cancer due to its higher sensitivity and specificity. One of the most promising approaches to improve the colposcopic test is the use of the aceto-white temporal patterns intrinsic to the color changes in digital images. However, there is not a complete understanding of how to use them to segment colposcopic images. In this work, we used the classification algorithm k-NN over the entire length of the aceto-white temporal pattern to automatically discriminate between normal and abnormal cervical tissue, reaching a sensitivity of 71% and specificity of 59%. (C) 2009 Published by Elsevier Ltd.