The aim of this work is to draw the attention of the biophotonics community to a stochastic decomposition method (SDM) to potentially model 2-D scans of light scattering data from epithelium mucosa tissue. The emphasis in this work is on the proposed model and its theoretical pinning and foundation. Unlike previous works that analyze scattering signal at one spot as a function of wavelength or angle, our method statistically analyzes 2-D scans of light scattering data over an area. This allows for the extraction of texture parameters that correlate with changes in tissue morphology, and physical characteristics such as changes in absorption and scattering characteristics secondary to disease, information that could not be revealed otherwise. The method is tested on simulations, phantom data, and on a limited preliminary in-vitro animal experiment to track mucosal tissue inflammation over time, using the area Az under receiver operating characteristics (ROC) curve as a performance measure. Combination of all the features results in an Az value up to 1 for the simulated data, and Az > 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.
In this paper, the structural parameters of dysplasia formation in the epithelial tissue are estimated using a stochastic decomposition algorithm (SDM) by means of scattered light. We extract texture parameters obtained from the decomposition that capture the signature of dysplasia formation. These parameters include the number and mean energy of coherent scatterers; deviation from Rayleigh scattering; average energy of diffuse scatterers; and normalized correlation coefficient. The tests are performed on simulations, and tissue-mimicking phantom data. The simulations are based on the light scattered from the cells with varying parameters such as, index of refraction, number of cells, and size of cells. The obtained results demonstrate the proof-of-concept in being able to differentiate between tissue structures that give rise to changes in cell morphology as well as other physical properties such as change in index of refraction. Fusing all the estimated parameter set together results in the differentiation performance (Az value) up to 1(perfect detection) for simulated data, and Az>0.927 for the phantom data.
Visual inspection of ultrasound is diagnostically limited for characterizing breast tissue, in particular when it comes to visually detecting hyperplasia that forms in the ducts at its early formation (at submillimeter resolution) stages. It can, of course, be seen using biopsies. But this will not be done unless the areas have been flagged using noninvasive modalities. The aim of this paper is to draw to the attention of the medical community (albeit through simulations) that the continuous wavelet transform decomposition (CWTD) that was proven in vivo for tissue characterization before has the potential to flag out simulated hyperplasia data at submillimeter resolutions. And it might be an excellent candidate for detecting in vivo hyperplastic changes in the breast. To the best of our knowledge, this is the first attempt at studying the potential of detecting cell growth in breast ducts using ultrasound. The stochastic decomposition model (the CWTD) of the RF echo with its coherent and diffuse components, yields image parameters that correlate closely with the structural parameters of the (simulated) hyperplastic stages of the breast tissue. The discrimination power of the various parameters is studied under a host of conditions, such as varying resolution, depth, and coherent to diffuse energy ratio (CDR) values using a point-scatterer model simulator that mimics epithelium hyperplastic growth in the breast ducts. These are shown to be useful for detecting the various types of simulated hyperplastic data. Careful analysis shows that three parameters, in particular the number of coherent scatterers, the Rayleigh scattering degree, and the energy of the diffuse scatterers, are most sensitive to variations in the hyperplastic simulated data. And they show very high ability to discriminate between various stages of simulated hyperplasia, even in cases of low resolution and low CDR values. Using the area under the receiver operating characteristics (ROC) curve (A(z)) as the performance metric, values of A(z) > 0.942 are obtained when discriminating between stages for resolution
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.
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.
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