Recent multiscale AM-FM methods in emerging applications in medical imaging

Murray, Víctor; Pattichis, Marios S.; Barriga, E. Simon; Solíz, Peter

doi:10.1186/1687-6180-2012-23

Cited by 25 publications

(21 citation statements)

References 95 publications

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“…Since cos(ϕ(x)) = cos(−ϕ(x)), there is an ambiguity in the definition of the instantaneous frequency ∇ϕ since ϕ can be replaced by −ϕ. To solve this problem, practitioners usually assume that the first component of the instantaneous frequency is positive (see [10] and references therein for more details). Under this additional assumption, our method yields an estimate of the amplitude and of the instantaneous frequency of an IMMF:…”

Section: Wavelet Analysis Of Bidimensional Spectral Linesmentioning

confidence: 99%

The monogenic synchrosqueezed wavelet transform: a tool for the decomposition/demodulation of AM–FM images

Clausel

Oberlin

Perrier

2015

Applied and Computational Harmonic Analysis

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The synchrosqueezing method aims at decomposing 1D functions as superpositions of a small number of "Intrinsic Modes", supposed to be well separated both in time and frequency. Based on the unidimensional wavelet transform and its reconstruction properties, the synchrosqueezing transform provides a powerful representation of multicomponent signals in the time-frequency plane, together with a reconstruction of each mode.In this paper, a bidimensional version of the synchrosqueezing transform is defined, by considering a well-adapted extension of the concept of analytic signal to images: the monogenic signal. The natural bidimensional counterpart of the notion of Intrinsic Mode is then the concept of "Intrinsic Monogenic Mode" that we define. Thereafter, we investigate the properties of its associated Monogenic Wavelet Decomposition. This leads to a natural bivariate extension of the Synchrosqueezed Wavelet Transform, for decomposing and processing multicomponent images. Numerical tests validate the effectiveness of the method for different examples.

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Section: Wavelet Analysis Of Bidimensional Spectral Linesmentioning

confidence: 99%

The monogenic synchrosqueezed wavelet transform: a tool for the decomposition/demodulation of AM–FM images

Clausel

Oberlin

Perrier

2015

Applied and Computational Harmonic Analysis

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“…Biomedical images are often dominated by strong nonstationary behavior that can be described using Multidimensional Amplitude Modulation-Frequency Modulation (AM-FM) models [1], [2]. The development of AM-FM models and applications have been documented in previous research reported in [3], [4], [5], [6], [7].…”

Section: Introductionmentioning

confidence: 97%

A quasi-local method for instantaneous frequency estimation with application to structural magnetic resonance images

Ulloa

Rodríguez

Liu

et al. 2014

2014 36th Annual International Conference of the IEEE Engineering in Medicine and Biology Society

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Spatially-varying signal content can be effectively modeled using amplitude modulation-frequency modulation (AM-FM) representations. The AM-FM representation allow us to extract instantaneous amplitude (IA) and instantaneous frequency (IF) components that can be used to measure non-stationary content in biomedical images and videos. This paper introduces a new method for estimating the IA and the IF based on a quasi-local method (QLM). We provide an extensive comparison of AM-FM demodulation approaches based on QLM and a quasi-eigenfunction approximation method using three different filter-banks: (i) a separable, equiripple design, (ii) a Gabor filter bank, and (iii) a directional filter bank approach based on the Contourlet transform. The results document that the use of the new QLM method with an equiripple filter bank design gave the best IF magnitude estimates for a synthetic image. The new QLM method is then applied to a multi-site schizophrenia dataset (N=307). The dataset included structure magnetic resonance images from healthy controls and patients diagnosed with schizophrenia. The IF magnitude is shown to be less sensitive to variations across sites as opposed to the standard use of SMRI images that suffered from significant dependency on the scanner configurations on different collection sites. Furthermore, the regions of interest identified through the use of the IF magnitude are in agreement with previous studies.

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“…A summary of several applications of the new multi-scale AM-FM methods can be found in the recently published survey paper [2].…”

Section: Introductionmentioning

confidence: 99%

Multiscale AM-FM image reconstructions based on elastic net regression and Gabor filterbanks

Constantinou

Pattichis

2013

2013 Asilomar Conference on Signals, Systems and Computers

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The paper proposes the use of elastic net regression for reconstructing images from AM-FM components. Current AM-FM reconstruction methods are based on Dominant Component Analysis (DCA), multi-scale DCA, and Channel Component Analysis (CCA). The paper introduce a variation on CCA that uses elastic net regression to minimize the number of channels that are used in the reconstruction. The new approach is validated using a family of Gabor filterbanks that is parameterized by an overlap index. The results show that the elastic net regression component selection algorithm performs significantly better than multiscale DCA.

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Recent multiscale AM-FM methods in emerging applications in medical imaging

Cited by 25 publications

References 95 publications

The monogenic synchrosqueezed wavelet transform: a tool for the decomposition/demodulation of AM–FM images

The monogenic synchrosqueezed wavelet transform: a tool for the decomposition/demodulation of AM–FM images

A quasi-local method for instantaneous frequency estimation with application to structural magnetic resonance images

Multiscale AM-FM image reconstructions based on elastic net regression and Gabor filterbanks

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