Tissue characterization and detection of dysplasia using scattered light

Paper presented at the 2006 3rd IEEE International Symposium on Biomedical Imaging: From Nano to Macro, Arlington, VA.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

Detecting the stages of hyperplasia formation in the breast ducts using ultrasound B-scan images

Presented at the 2006 3rd IEEE International Symposium on Biomedical Imaging: From Nano to Macro, Arlington, VA, DOI: http://dx.doi.org/10.1109/ISBI.2006.1625064A stochastic decomposition algorithm of the RF Echo into its coherent and diffuse components is used towards estimating the structural parameters of the hyperplastic stages of the breast tissue leading to early breast cancer detection. The discrimination power of the various parameters is studied under a host of conditions such as varying resolution and SNR values using a point scatterer model simulator that mimics epithelium hyperplastic growth in the breast ducts. It is shown that three parameters, in particular, the number of coherent scatterers, the Rayleigh scattering degree and the energy of the diffuse scatterers, prove to show very high ability to discriminate between various stages of hyperplasia even in cases of low resolution and SNR values. Values of Az>0.942 were obtained for resolution less than or equal to 0.4mm even in low SNR values, then it drops below the 0.9 range as the resolution exceeds the 0.4mm range

A História da Alimentação: balizas historiográficas

Os M. pretenderam traçar um quadro da História da Alimentação, não como um novo ramo epistemológico da disciplina, mas como um campo em desenvolvimento de práticas e atividades especializadas, incluindo pesquisa, formação, publicações, associações, encontros acadêmicos, etc. Um breve relato das condições em que tal campo se assentou faz-se preceder de um panorama dos estudos de alimentação e temas correia tos, em geral, segundo cinco abardagens Ia biológica, a econômica, a social, a cultural e a filosófica!, assim como da identificação das contribuições mais relevantes da Antropologia, Arqueologia, Sociologia e Geografia. A fim de comentar a multiforme e volumosa bibliografia histórica, foi ela organizada segundo critérios morfológicos. A seguir, alguns tópicos importantes mereceram tratamento à parte: a fome, o alimento e o domínio religioso, as descobertas européias e a difusão mundial de alimentos, gosto e gastronomia. O artigo se encerra com um rápido balanço crítico da historiografia brasileira sobre o tema

Stochastic decomposition method for detection of epithelium dysplasia and inflammation using white light spectroscopy imaging

Proceedings of the 28th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS'06, pp. 1956-1959.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

Fingerprint Alignment Based on Local Feature Combined with Affine Geometric Invariant

In this paper we introduce a novel method of fingerprint alignment that uses the intrinsic geometric properties of minutiae-based triangles combined with the geometric invariant. The minutiae points are extracted from the fingerprint image and a Delaunay (DL) triangulation is constructed from these minutiae points resulting in a series of triangles. Corresponding minutiae points are established using local affine invariants constructed from the local minutia-based triangles. Triangles that are distorted by noise or have no counter part on the query are discarded. We rely only on “strong” matches that are reliable and present, for example, where the error metric between the local absolute invariants is below a set threshold. The correspondences of such matches are then used to estimate transformation parameters. The performance of our method is represented by computing the distance map error between a template and a query fingerprint after undoing the transformation, computed from the ridge structures of the two fingerprints. In conclusion, the proposed method can be used to find the corresponding minutiae and align any fingerprints considered into affine transformation, in the presence of noise including the partial occlusion

Classification of the stages of hyperplasia in breast ducts by analyzing different depths and segmentation of ultrasound breast scans into ductal areas

Proceedings of the 28th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS'06, pp. 2396-2399.In this paper, we study in depth the potential of detection of epithelium hyperplastic growth in the breast ducts leading to early breast cancer detection. Towards that end, we use a stochastic decomposition algorithm of the RF echo into its coherent and diffuse components that yields image parameters related to the structural parameters of the hyperplastic stages of the breast tissue. Previously, we proved that the two parameters, in particular the number of coherent scatterers and the Rayleigh scattering degree show very high ability to discriminate between various stages of hyperplasia even in cases of low resolution and low SNR values. In this paper, the discrimination power of the other parameters is studied further considering different depths using a point scatterer model simulator that mimics epithelium hyperplastic growth in the breast ducts. Significant improvement is obtained in the performance with the newly adopted method considering depth. Values of Az up to 0.974 are obtained when discriminating between pairs of stages using the parameter residual error variance. In addition, this paper presents a fast nonparametric segmentation procedure to locate the ducts illustrated using phantom data. The performance of the segmentation procedure is obtained as Az>0.948 for various regions of breast scans

Tracking of Unknown Nonstationary Chirp Signals Using Unsupervised Clustering in the Wigner Distribution Space

This paper is concerned with the problems of 1) detecting the presence of one or more FM chirp signals embedded in noise, and 2) tracking or estimating the unknown, time-varying instantaneous frequency of each chirp component. No prior knowledge is assumed about the number of chirp signals present, the parameters of each chirp, or how the parameters change with time. A detection/estimation algorithm is proposed that uses the Wigner distribution transform to find the best piecewise cubic approximation to each chirp’s phase function. The first step of the WD based algorithm consists of properly thresholding the WD of the received signal to produce contours in the time-frequency plane that approximate the instantaneous frequency of each chirp component. These contours can then be approximated as generalized lines in the (to, t, t2) space. The number of chirp signals (or equivalently, generalized lines) present is determined using maximum likelihood segmentation. Minimum mean square estimation techniques are used to estimate the unknown phase parameters of each chirp component. We demonstrate that for the cases of i) nonoverlapping linear or nonlinear FM chirp signals embedded in noise or ii) overlapping linear FM chirp signals embedded in noise, the approach is very robust, highly reliable, and can operate efficiently in low signal-to-noise environments where it is hard for even trained operators to detect the presence of chirps while looking at the WD plots of the overall signal. For multicomponent signals, the proposed technique is able to suppress noise as well as the troublesome cross WD components that arise due to the bilinear nature of the WD. © 1993 IEE

TRACKING OF UNKNOWN NON-STATIONARY CHIRP SIGNALS USING UNSUPERVISED CLUSTERING IN THE WIGNER DISTRIBUTION SPACE.

The authors are concerned with the problems of detecting the presence and tracking the unknown, time-varying instantaneous frequencies of nonoverlapping linear or nonlinear FM chirp signals embedded in noise and overlapping linear FM chrip signals embedded in noise. No prior knowledge is assumed about the signal parameters, or when the signal changes its parameters in time, or the number of signals present. For both the overlapping and nonoverlapping cases, the authors analyze the Wigner distribution (WD) of the received signal s(t). The WD of many FM chirp signals is highly concentrated above a 2-D curve in the time-frequency plane that corresponds with the signal\u27s instantaneous frequency. The contours that are produced by properly thresholding the WD are hence generalized lines in the ( omega ,t,t**2) space. Hence, the tracking problem for both cases reduces to the simpler problem of tracking generalized lines, and is done using unsupervised weighted maximum-likelihood clustering, and minimum-mean-square estimation