Affiliations: Department of Biomedical Engineering, Columbia
University, New York NY, USA | Department of Electrical Engineering, Technion –
Israel Institute of Technology, Haifa, Israel
Note: [] Corresponding author. Department of Biomedical Engineering,
Columbia University, 351 Engineering Terrace, MC 8904, New York, NY 10027, USA.
E-mail: ps629@columbia.edu
Abstract: Identifying physiological and anatomical signatures of disease in
signals and images is one of the fundamental challenges in biomedical
engineering. The challenge is most apparent given that such signatures must be
identified in spite of tremendous inter and intra-subject variability and
noise. Crucial for uncovering these signatures has been the development of
methods that exploit general statistical properties of natural signals. The
signal processing and applied mathematics communities have developed, in recent
years, signal representations which take advantage of Gabor-type and
wavelet-type functions that localize signal energy in a joint time-frequency
and/or space-frequency domain. These techniques can be expressed as
multi-resolution transformations, of which perhaps the best known is the
wavelet transform. In this paper we review wavelets, and other related
multi-resolution transforms, within the context of identifying signatures for
disease. These transforms construct a general representation of signals which
can be used in detection, diagnosis and treatment monitoring. We present
several examples where these transforms are applied to biomedical signal and
imaging processing. These include computer-aided diagnosis in mammography,
real-time mosaicking of ophthalmic slit-lamp imagery, characterization of heart
disease via ultrasound, predicting epileptic seizures and signature analysis of
the electroencephalogram, and reconstruction of positron emission tomography
data.
Journal: Disease Markers, vol. 18, no. 5-6, pp. 339-363, 2002
