Abstract
Colposcopy test is the second most used technique to diagnose cervical cancer disease. Some researchers have proposed to use temporal changes intrinsic to the colposcopic image sequences to automatically characterize cervical lesion. Under this approach, every single pixel on the image is represented as a Time Series of length equal to the sampling frequency times acquisition points. Although this approach seems to show promising results, the data analysis procedures have to deal with huge data set that rapidly increase with the number of cases (patients) considered in the analysis. In the present work, we perform principal component analysis (PCA) to reduce the dimensionality of the data in order to facilitate similarity measures for classification and clustering. The importance of this work is that we propose a model to parameterize the dynamics of the system using an efficient representation getting a 1.11% data compression ratio and similarity on clustering of 0.78. The feasibility of the proposed model is shown testing the similarity of the clusters generated using the k-means algorithm over the raw data and the compressed representation of real data.
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References
Burghardt, E.H.P., Girardi, F.: Primary care Colposcopy. Thieme, Stuttgart (2004)
Ronne, M.: Chromosome preparation and high resolution banding techniques. Dairy of Science, pp. 363–1377 (1989)
Craine, B.L., Cynthia, E.R.C., O’Toole, J., Ji, Q.: Digital Imaging Colposcopy: Corrected Area Measurements Using Shape-from-Shading. IEEE Transactions on Medical Imaging 17(6), 1003–1010 (1998)
Vlastos, A.-T., Richards-Kortum, R., Zuluaga, A., Follen, M.: New approaches to cervivcal cancer screening. Contemporary Ob/Gyn, pp. 87–103 (2002)
Claude, I., Huault, S., Boulanger, C.: Integred Color and Texture Tools For Colposcopic Image Segmentation. IEEE Transactions on Medical Imaging (2001)
Ji, Q., Engel, J., Craine, E.: Texture Analysis for Classification of Cervix Lesions. IEEE Transactions on Medical Imaging 19(11), 1144–1149 (2000)
Balas, C.: Novel Optical Imaging Method for the Early Detection, Quantitative Grading, and Mapping of Cancerous and Precancerous Lesions of Cervix. IEEE Transactions in Biomedical Engineering 48(1), 96–104 (2001)
Stefanaki, I., et al.: In Vivo Detection of Human Papilloma Virus-Induced Lesions of Ano-genital Area after Application of Acetic Acid: a Novel and Accurate Approach to a Trivial Method. Journal of Photochemistry and Photobiology 65, 115–121 (2001)
Pogue, B., et al.: Analysis of Acetic Acid-induced Whitening of High-grade Squamous Intra-epitelial Lesions. Journal of Biomedical Optics 6(4), 397–403 (2001)
Anderson, M., Jordan, J., Morse, A., Sharp, F.: A Text and Atlas of Integrated Colposcopy. Mosby, St. Louis (1993)
Brown, L.G.: A survey of Image Registration Techniques, pp. 1–60 (1992)
Zitova Barbara, F.J.: Image Registration Methods: a Survey. Image and vision computing (2004)
Hill, D.L.: Medical Image Registration. Physics in Medicine and Biology 1(46), R1–R45 (2001)
Last, M., Kandel, A., Bunke, H.: Data Mining in Time Series Databases. World Scientific, Singapore (2004)
Keogh, E., et al.: Dimensionality Reduction for Fast Sililarity Search in Large Time Series Databases. Knowledge and Information System 3(3), 263–286 (2000)
Keogh, E., Ratanamahatana, C.: Exact Indexing of Dynamic Time Warping. Knowledge and Information Systems. In: Proceedings of the 28th International Conference on Very Large Data Bases, pp. 406–417 (2004)
Tabachnick, B., et al.: Using Multivarieate Statistics, 4th edn. Allyn and Bacon, Boston (2000)
Keogh, E., Ratanamahatana, C.: Making Time-series Classification More Accurate Using Learned Constraints. In: Proceedings of hte SIAM International Conference on Data Mining, pp. 11–22 (2004)
Ratanamahatana, C., et al.: A Novel Bit Level Time Series Representation with Implications for Similarity Search and Clustering. Data Mining and Knowledge Discovery, accepted for publication (2005)
Gonzalez, C., Woods, R., Eddins, S.: Digital image processing. Prentice-Hall, Englewood Cliffs (2004)
Keogh, E., Pazzani, M.: Dynamic Time Warping with Higher Order Features. In: First SIAM International Conference on Data Mining (2001)
Larsen, B., Aone, C.: Fast and effective text mining using linear-time document clustering. In: Proceedings of the fifth ACM SIGKDD international conference on Knowledge discovery and data mining, pp. 16–22 (2000)
Acosta-Mesa, H.G., Zitova, B., Rios-Figueroa, H.V., Cruz-Ramirez, N., Marin-Hernandez, A., Hernandez-Jimenez, R., Cocotle-Ronzon, B.E., Hernandez-Galicia, E.: Cervical Cancer Detection Using colposcopic Images: a Temporal Approach. In: Proceedings of the Sixth International Conference on Computer Science, pp. 158–164. IEEE Computer Society Press, Washington DC (2005)
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Acosta-Mesa, HG., Cruz-Ramírez, N., Hernández-Jiménez, R., García-López, DA. (2007). Modeling Aceto-White Temporal Patterns to Segment Colposcopic Images. In: Martí, J., Benedí, J.M., Mendonça, A.M., Serrat, J. (eds) Pattern Recognition and Image Analysis. IbPRIA 2007. Lecture Notes in Computer Science, vol 4478. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-72849-8_69
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DOI: https://doi.org/10.1007/978-3-540-72849-8_69
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-72848-1
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