Document Detail

Aceto-white temporal pattern classification using k-NN to identify precancerous cervical lesion in colposcopic images.
MedLine Citation:
PMID:  19608162     Owner:  NLM     Status:  MEDLINE    
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%.
Héctor-Gabriel Acosta-Mesa; Nicandro Cruz-Ramírez; Rodolfo Hernández-Jiménez
Publication Detail:
Type:  Journal Article; Research Support, Non-U.S. Gov't     Date:  2009-07-15
Journal Detail:
Title:  Computers in biology and medicine     Volume:  39     ISSN:  1879-0534     ISO Abbreviation:  Comput. Biol. Med.     Publication Date:  2009 Sep 
Date Detail:
Created Date:  2009-08-07     Completed Date:  2009-11-04     Revised Date:  -    
Medline Journal Info:
Nlm Unique ID:  1250250     Medline TA:  Comput Biol Med     Country:  United States    
Other Details:
Languages:  eng     Pagination:  778-84     Citation Subset:  IM    
School of Physics and Artificial Intelligence, Department of Artificial Intelligence, Universidad Veracruzana, Veracruz, Mexico.
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MeSH Terms
Artificial Intelligence
Colposcopy / statistics & numerical data*
Computer Simulation
Diagnosis, Computer-Assisted*
Image Interpretation, Computer-Assisted
Pilot Projects
Precancerous Conditions / classification,  diagnosis*
Uterine Cervical Neoplasms / classification,  diagnosis*
Young Adult

From MEDLINE®/PubMed®, a database of the U.S. National Library of Medicine

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