The early stages of malignant diseases, such as cancer, are characterized by cellular and microstructural
changes which define both the diagnosis and the prognosis of the disease. Unfortunately, at the current
resolution of Optical Coherence Tomography (OCT), such changes associated with early cancer are not
clearly discernible. However, spectral analysis of OCT images has recently shown that additional information
can be extracted from those signals, resulting in improved contrast which is directly related to scatterer size
changes. Amplitude Modulation - Frequency Modulation (AM-FM) analysis is a fast and accurate technique
which can also be applied to the OCT images for estimation of spectral information. It is based on the
analytic signal of the real data, obtained using a Hilbert Transform, and provides the instantaneous amplitude,
phase, and frequency of an OCT signal. The performance of this method is superior to both FFT-based and
parametric (e.g. autoregressive) spectral analysis providing better accuracy and faster convergence when
estimating scatterer features. Since disease tissues exhibit variations in scatterer size and thus also exhibit
marked differences in spectral and phase characteristics, such advanced analysis techniques can provide more
insight into the subtle changes observed in OCT images of malignancy. Therefore, they can make available a
tool which could prove extremely valuable for the investigation of disease features which now remain below
the resolution of OCT and improved the technology's diagnostic capabilities.
The early stages of malignant diseases, such as cancer, are characterized by cellular and microstructural changes
which define both the diagnosis and the prognosis of the disease. Unfortunately, at the current resolution of Optical
Coherence Tomography (OCT), such changes associated with early cancer are not clearly discernible. However,
spectral analysis of OCT images has recently shown that additional information can be extracted from those
signals, resulting in improved contrast which is directly related to scatterer size changes. Amplitude Modulation - Frequency Modulation (AM-FM) analysis is a fast and accurate technique which can also be applied to the OCT
images for estimation of spectral information. It is based on the analytic signal of the real data, obtained using a
Hilbert Transform, and provides the instantaneous amplitude, phase, and frequency of an OCT signal. The
performance of this method is superior to both FFT-based and parametric (e.g. autoregressive) spectral analysis
providing better accuracy and faster convergence when estimating scatterer features. Since disease tissues exhibit
variations in scatterer size and thus also exhibit marked differences in spectral and phase characteristics, such
advanced analysis techniques can provide more insight into the subtle changes observed in OCT images of
malignancy. Therefore, they can make available a tool which could prove extremely valuable for the investigation
of disease features which now remain below the resolution of OCT and improved the technology's diagnostic
capabilities.
A novel technique for analyzing Optical Coherence Tomography (OCT) signals is
presented. Spectral analysis of the interferometric OCT signal reveals scatterer size-depended
changes which can offer diagnostic information and be used to segment OCT images.
The early stages of malignancy, in most tissues, are characterized by unique cellular changes. Currently, these early changes are detectable only by confocal or multi-photon microscopy. Unfortunately, neither of the two imaging techniques can penetrate deep enough into the tissue to investigate the borders of thick lesions. A technique which would allow extraction of information regarding scatterer size from Optical Coherence Tomography (OCT) signals could prove a very powerful diagnostic tool and produce significant diagnostic insight. Such a procedure is proposed here. It is shown to be very effective in differentiating spectral differences which depend on scatterer size. The analysis of the OCT signal is based on spectral estimation techniques and statistical analysis. First, using autoregressive spectral estimation, it was deduced that tissues with different size scatterers exhibit marked differences in spectral content. Further, advanced analysis techniques, such as Principal Component Analysis (PCA) and Multivariate Analysis of Variance (MANOVA), provided more insight into the spectral changes. These techniques where tested on solutions of known scatterers and multilayered samples. The initial results are very encouraging and indicate that the spectral content of OCT signals can be used to extract scatterer size information. This technique can result in an extremely valuable tool for the investigation of disease tissue features which now remain below the resolution of OCT.
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