A wavelet-based multi-spectral codec for efficient detection of cervical neoplasia from encoded cervical images

The significance and need for expert interpretation of cervigrams in the study of human papillomavirus (HPV) are currently being investigated. Results of these preliminary studies suggest development and integration of new optical probes for detection and discrimination of cervical neoplasia using automated image analysis tools to reduce subjective variability and to provide remote areas with effective screening tools for early detection of cervical cancer. However, long-term studies using already available cervical images are needed to validate the potential of automated classification and recognition algorithms in discriminating cervical neoplasia and normal tissue. For the effective dissemination of cervical image data over the Web from a central repository to various study groups, it is essential that the image file size be reduced by advanced color data compression techniques while preserving crucial features of color and spatial details. We present the preliminary results of the effectiveness of a novel, wavelet-based, multi-spectral codec in retaining diagnostic features in encoded cervical images.