A multispectral digital Cervigram analyzer in the wavelet domain for early detection of cervical cancer

Shuyu Yang; Jiangling Guo; Philip S. King; Y. Sriraja; Sunanda Mitra; Brian Nutter; Daron Ferris M.D.; Mark Schiffman M.D.; Jose Jeronimo; Rodney Long

doi:10.1117/12.537622

12 May 2004 A multispectral digital Cervigram analyzer in the wavelet domain for early detection of cervical cancer

Shuyu Yang, Jiangling Guo, Philip S. King, Y. Sriraja, Sunanda Mitra, Brian Nutter, Daron Ferris M.D., Mark Schiffman M.D., Jose Jeronimo, Rodney Long

Author Affiliations +

Proceedings Volume 5370, Medical Imaging 2004: Image Processing; (2004) https://doi.org/10.1117/12.537622
Event: Medical Imaging 2004, 2004, San Diego, California, United States

Abstract

The significance and need for expert interpretation of cervigrams (images of the cervix) in the study of the uterine cervix changes and pre-neoplasic lesions preceding cervical cancer are being investigated. The National Cancer Institute has collected a unique dataset taken from patients with normal cervixes and at various stages of cervical pre-cancer and cancer. This dataset allows us the opportunity for studying the uterine cervix changes for validating the potential of automated classification and recognition algorithms in discriminating cervical neoplasia and normal tissue. Pilot studies have been designed (1) to evaluate the effect of image transformation and optimal color mapping on the accepted levels of compression needed for effective dissemination of cervical image data over a network and (2) for automated detection of lesions from feature extraction, registration, and segmentation of lesions in cervix image sequences. In this paper, we present the results of the effectiveness of a novel, wavelet based, multi-spectral analyzer in retaining diagnostic features in encoded cervical images, thus allowing investigation on the potential of automated detection of lesions in cervix image sequences using automated registration, color transformation and bit-rate control, and a statistical segmentation approach.

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Shuyu Yang, Jiangling Guo, Philip S. King, Y. Sriraja, Sunanda Mitra, Brian Nutter, Daron Ferris M.D., Mark Schiffman M.D., Jose Jeronimo, and Rodney Long "A multispectral digital Cervigram analyzer in the wavelet domain for early detection of cervical cancer", Proc. SPIE 5370, Medical Imaging 2004: Image Processing, (12 May 2004); https://doi.org/10.1117/12.537622

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