Joined department in 1981

Professor Lucier has worked for over fifteen years on wavelet and multiresolution methods for image processing and other applications. He has a particular interest in applications in medical imaging (image compression for telemedicine, tomographic and MRI reconstruction, etc.).

The selected publications below indicate some of these interests. The first paper relates variational problems to wavelet shrinkage, as introduced by David Donoho and Iain Johnstone. The second paper introduces several results about wavelet methods for medical tomography, especially for Positron Emission Tomography (PET) imaging. Basically, it again puts into a variational framework the wavelet-vaguelette method of Donoho; it shows that wide classes of computationally efficient wavelet transforms can be used for tomography; and it gives examples that show that wavelet techniques are much more effective than the filtered back-projection method for PET imaging.

Finally, the third paper is a study of radiologist performance in interpreting wavelet-compressed mammographic images. The compression method was designed specifically to keep image features that are needed to interpret mammograms. The results showed that after compressing the images over 50-to-1 (on average; compression rates ranged from 14-to-1 for mammograms with dense structures to over 2000-to-1 for mammograms with few optical structures) radiologists interpreted the compressed mammograms more accurately than they interpreted the originals. Such effective data compression may allow mammographic screenings to be performed by technicians in areas traditionally underserved by the medical community, with the data sent over regular phone lines to radiologists in medical centers for interpretation.