Tissue characterization using the continuous wavelet transform. I. Decomposition method

Γεωργίου, Γεωργία; Cohen, Fernand S.

doi:10.1109/58.911718

Cited by 63 publications

(47 citation statements)

References 25 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The rejection of the Rayleigh scattering hypothesis indicates the existence of a coherent component. If a coherent component exists, the signal is decomposed into its coherent c(n) and diffuse d(n) components using the wavelet decomposition described in [5]. This decomposition is consistent with the general decomposition of regular stochastic fields into predictable (the specular field) component and unpredictable (the diffused field) component, known in the literature of stochastic processes as the WOLD decomposition [4].…”

Section: Stochastic Decomposition Methods (Sdm) and The Extractedmentioning

confidence: 96%

Stochastic Decomposition Method for Detection of Epithelium Dysplasia and Inflammation using White Light Spectroscopy Imaging

Taslidere

Cohen

2006

2006 International Conference of the IEEE Engineering in Medicine and Biology Society

View full text Add to dashboard Cite

In this paper, we present a stochastic decomposition method (SDM) that allows the detection of dysplasia in epithelial tissue using white-light spectroscopy imaging. The main goal is to extract the data from the decomposition which will lead to the construction of a feature parameter space corresponding to changes in the tissue morphology related to formation of dysplasia and inflammation. These parameters include the number and mean energy of coherent scatterers; deviation from Rayleigh scattering; residual error variance of the diffuse component; and normalized correlation coefficient. The tests are performed on tissue-mimicking phantom data and tissue data collected from mouse colon in vitro. The obtained results demonstrate effectiveness of the method in differentiating between tissue structures with different cell morphologies. The results are shown by fusing all the estimated parameter set together and also using each parameter separately. Combination of all the features results in an Az value higher than 0.927 for the phantom data. For the tissue data, the best performances for differentiation between pairs of various levels of inflammation are 0.859, 0.983, and 0.999.

show abstract

Section: Stochastic Decomposition Methods (Sdm) and The Extractedmentioning

confidence: 96%

Stochastic Decomposition Method for Detection of Epithelium Dysplasia and Inflammation using White Light Spectroscopy Imaging

Taslidere

Cohen

2006

2006 International Conference of the IEEE Engineering in Medicine and Biology Society

View full text Add to dashboard Cite

show abstract

“…3. What determines p is the memory of the process, namely, how many previous values of the process enter in the determination of the conditional mean value of the process at time n. The value of 5 for p is determined using our previous work in [23,43,44]. The AR process is parameterized by the set [b 1 (k), b 2 (k),…, b p (k), r 2 (k) = (b(k), r 2 (k)].…”

Section: The Diffuse Modelmentioning

confidence: 99%

Can we see epithelium tissue structure below the surface using an optical probe?

Cohen

Taslidere

Murthy

2010

Med Biol Eng Comput

View full text Add to dashboard Cite

This paper answers the question of whether it is possible to detect changes below the surface in epithelium layered structures using a Stochastic Decomposition Method (SDM) that models the scattered light reflected from the layered structure over an area (2-D scan) illuminated by an optical sensor (fibre) emitting light at either one wavelength or with white light. Our technique correlates the differential changes in the reflected tissue texture with the morphological and physical changes that occur in the tissue occurring inside the structure. This work has great potential for detecting changes in mucosal structures and may lead to enhanced endoscopy when the disease is developing to the outside of the mucosal structure and hence becoming hidden during colonoscopy or endoscopic examination. Tests are performed on layered tissue phantoms, and the results obtained show great effectiveness of the model and method in picking up changes in the morphology of the layered tissue phantoms occurring below the surface. We also establish the robustness of the model to changes in viewing depth by testing it on phantoms viewed at different depths. We show that the model is robust to within a 4-mm-deep viewing range.

show abstract

“…The Hanning taper with gaps (HnG), a Hanning taper multiplied element-wise with the gap pattern, was also examined. The sinusoidal taper, generated according to (10) for n = 1, 2, …, N, was also examined. The sinusoidal taper with gaps (SNG) was generated using element-wise multiplication of the taper and the gap pattern.…”

Section: Spectrum Estimation: Tapers With Gapsmentioning

confidence: 99%

“…In addition, Monte Carlo simulations have been used previously to tune θ. 10 The decision rule in Equation (9) allows for the creation of a binary mask that records whether each RF sample belongs to the diffuse or coherent component. After generating this mask, a windowing technique can be used to estimate the spectrum using only the RF samples that are included in the diffuse component.…”

Section: Signal Decompositionmentioning

confidence: 99%

“…These structures create nonstationarities in the backscattered signal that must be taken into account when parameterizing BSCs using diffuse scattering models. Previously, models have been proposed for the interrogated medium that include two 9,10 or three 11,12 scatterer components. The two-component models consist of diffuse (nonresolvable) scatterers and scatterers that produce resolvable echoes and have quasiperiodic spatial locations.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Backscatter coefficient estimation using tapers with gaps

Luchies

Oelze

2014

The Journal of the Acoustical Society of America

View full text Add to dashboard Cite

When using the backscatter coefficient (BSC) to estimate quantitative ultrasound parameters such as the effective scatterer diameter (ESD) and the effective acoustic concentration (EAC), it is necessary to assume that the interrogated medium contains diffuse scatterers. Structures that invalidate this assumption can affect the estimated BSC parameters in terms of increased bias and variance and decrease performance when classifying disease. In this work, a method was developed to mitigate the effects of echoes from structures that invalidate the assumption of diffuse scattering, while preserving as much signal as possible for obtaining diffuse scatterer property estimates. Backscattered signal sections that contained nondiffuse signals were identified and a windowing technique was used to provide BSC estimates for diffuse echoes only. Experiments from physical phantoms were used to evaluate the effectiveness of the proposed BSC estimation methods. Tradeoffs associated with effective mitigation of specular scatterers and bias and variance introduced into the estimates were quantified. Analysis of the results suggested that discrete prolate spheroidal (PR) tapers with gaps provided the best performance for minimizing BSC error. Specifically, the mean square error for BSC between measured and theoretical had an average value of approximately 1.0 and 0.2 when using a Hanning taper and PR taper respectively, with six gaps. The BSC error due to amplitude bias was smallest for PR (Nω = 1) tapers. The BSC error due to shape bias was smallest for PR (Nω = 4) tapers. These results suggest using different taper types for estimating ESD versus EAC.

show abstract

Tissue characterization using the continuous wavelet transform. I. Decomposition method

Cited by 63 publications

References 25 publications

Stochastic Decomposition Method for Detection of Epithelium Dysplasia and Inflammation using White Light Spectroscopy Imaging

Stochastic Decomposition Method for Detection of Epithelium Dysplasia and Inflammation using White Light Spectroscopy Imaging

Can we see epithelium tissue structure below the surface using an optical probe?

Backscatter coefficient estimation using tapers with gaps

Contact Info

Product

Resources

About