Share Email Print

Proceedings Paper

A multispectral digital Cervigram analyzer in the wavelet domain for early detection of cervical cancer
Author(s): Shuyu Yang; Jiangling Guo; Philip S. King; Y. Sriraja; Sunanda Mitra; Brian Nutter; Daron Ferris; Mark Schiffman; Jose Jeronimo; Rodney Long
Format Member Price Non-Member Price
PDF $14.40 $18.00
cover GOOD NEWS! Your organization subscribes to the SPIE Digital Library. You may be able to download this paper for free. Check Access

Paper 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.

Paper Details

Date Published: 12 May 2004
PDF: 12 pages
Proc. SPIE 5370, Medical Imaging 2004: Image Processing, (12 May 2004); doi: 10.1117/12.537622
Show Author Affiliations
Shuyu Yang, Texas Tech Univ. (United States)
Jiangling Guo, Texas Tech Univ. (United States)
Philip S. King, Texas Tech Univ. (United States)
Y. Sriraja, Texas Tech Univ. (United States)
Sunanda Mitra, Texas Tech Univ. (United States)
Brian Nutter, Texas Tech Univ. (United States)
Daron Ferris, Medical College of Georgia (United States)
Mark Schiffman, National Cancer Institute (United States)
Jose Jeronimo, National Cancer Institutes (United States)
Rodney Long, National Library of Medicine (United States)

Published in SPIE Proceedings Vol. 5370:
Medical Imaging 2004: Image Processing
J. Michael Fitzpatrick; Milan Sonka, Editor(s)

© SPIE. Terms of Use
Back to Top