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.
| Original language | English |
|---|---|
| Pages (from-to) | 1833-1844 |
| Number of pages | 12 |
| Journal | Proceedings of SPIE - The International Society for Optical Engineering |
| Volume | 5370 III |
| DOIs | |
| State | Published - 2004 |
| Event | Progress in Biomedical Optics and Imaging - Medical Imaging 2004: Imaging Processing - San Diego, CA, United States Duration: Feb 16 2004 → Feb 19 2004 |
Keywords
- Cervical cancer
- Cervigram
- Compression
- Multi-spectral analyzer
- Registration
- Statistical segmentation