Cervical Cancer Detection Using Colposcopic Images: a Temporal Approach

Authors:
Hector G. Acosta-Mesa;Homero V. Rios-Figueroa;Nicandro Cruz-Ramirez;Antonio Marin-Hernandez;Zitova Barbara;Rodolfo Hernandez-Jimenez;Bertha E. Cocotle-Ronzon;Efrain Hernandez-Galicia
Affiliations:
University of Veracruz, Sebastian Camacho, Mexico;University of Veracruz, Sebastian Camacho, Mexico;University of Veracruz, Sebastian Camacho, Mexico;University of Veracruz, Sebastian Camacho, Mexico;University of Veracruz, Sebastian Camacho, Mexico;Obstetrician and Gynaecologist, Mexico;Anatomical Patology , México;University of Veracruz, Sebastian Camacho, Mexico
Venue:
ENC '05 Proceedings of the Sixth Mexican International Conference on Computer Science
Year:
2005

Citing 4
Cited 6

Registration of Translated and Rotated Images Using Finite Fourier Transforms

IEEE Transactions on Pattern Analysis and Machine Intelligence
A survey of image registration techniques

ACM Computing Surveys (CSUR)
Bayesian Network Classifiers

Machine Learning - Special issue on learning with probabilistic representations
Pattern Classification (2nd Edition)

Pattern Classification (2nd Edition)

Modeling Aceto-White Temporal Patterns to Segment Colposcopic Images

IbPRIA '07 Proceedings of the 3rd Iberian conference on Pattern Recognition and Image Analysis, Part II
Aceto-white temporal pattern classification using k-NN to identify precancerous cervical lesion in colposcopic images

Computers in Biology and Medicine
Bayesian model combination and its application to cervical cancer detection

IBERAMIA-SBIA'06 Proceedings of the 2nd international joint conference, and Proceedings of the 10th Ibero-American Conference on AI 18th Brazilian conference on Advances in Artificial Intelligence
Automatic colposcopy video tissue classification using higher order entropy-based image registration

Computers in Biology and Medicine
Image analysis of histological features in molar pregnancies

Expert Systems with Applications: An International Journal
Automatic cervical cell segmentation and classification in Pap smears

Computer Methods and Programs in Biomedicine

Quantified Score

Hi-index	0.00

Visualization

Abstract

In the present work we propose a methodology analysis of the colposcopic images to help the expert to make a more robust diagnosis of precursor lesions of cervical cancer. Although some others approaches have been used to assess cervical lesion, a complete methodology to evaluate temporal changes of tissue color is still missing. The different processes involved in the analysis are described. The image registration was implemented using the phase correlation method followed by a locally applied algorithm based on the normalized cross-correlation. During the parameterization process, each time series obtained from the image sequences was represented as a parabola in a parameter space. A supervised Bayesian learning approach is proposed to classify the features in the parameter space according to the classification made by the colposcopist. Then those labels are used as a criterion to categorize the tissue and perform the image segmentation. Some preliminary results are shown using unsupervised learning with real data.