A survey of image registration techniques
ACM Computing Surveys (CSUR)
Machine Learning
Data Mining in Time Series Database
Data Mining in Time Series Database
Cervical Cancer Detection Using Colposcopic Images: a Temporal Approach
ENC '05 Proceedings of the Sixth Mexican International Conference on Computer Science
Automatic colposcopy video tissue classification using higher order entropy-based image registration
Computers in Biology and Medicine
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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%.