Thermal effects in high density polyethylene and low density polyethylene at high hydrostatic pressures

The temperature changes as a result of rapid hydrostatic pressure applications are reported for high density polyethylene (HDPE) and low density polyethylene (LDPE) in the reference temperature range from 298 to 423 K and in the pressure range from 13.8 to 200 MN m −2 . The adiabatic temperature changes were found to be a function of pressure and temperature. A curve fitting analysis showed that the empirical curve (∂/∂ P ) = ab (Δ P ) b−1 described the experimental thermoelastic coefficients obtained from the experiments. The data were analyzed by determining the predicted thermoelastic coefficients derived from the Thomson equation (∂/∂ P ) θ = α T 0 /ϱ C p . The experimental and predicted Grüneisen parameter γ T were also determined.Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/44687/1/10853_2005_Article_BF01132919.pd

A Robust Polynomial Filtering Framework for Mammographic Image Enhancement from Biomedical Sensors

This paper presents a non-linear framework employing a robust Polynomial filter for accomplishing enhancement of mammographic abnormalities outcoming from biomedical instrumentation, that is, X-rays instrumentation. The approach proposed in this work utilizes a linear combination of Type-0 and Type-II Polynomial filters as a generalized filtering solution to achieve enhancement of mammographic masses and calcifications irrespective of the nature of background tissues. A Type-0 filter provides contrast enhancement, suppressing the ill effects of background noise. On the other hand, Type-II filter performs edge enhancement leading to preservation of finer details. Contrast Improvement Index (CII) is used as a performance measure to quantify the degree of improvement in contrast of the region-of interest (ROI). In addition, estimation of signal-to-noise ratio (in terms of PSNR and ASNR) is carried out to account for the suppression in background noise levels and over-enhancements of the processed mammograms. These measures are used as a mechanism to optimally select the filter parameters and also serve as a quantifying platform to compare the performance of the proposed filter with other non-linear enhancement techniques to be used for diverse biomedical image sensors

NSCT Based Multispectral Medical Image Fusion Model

Multispectral image fusion deliberates upon fusion of the complementary information while discarding the surplus information to achieve a solitary image which encloses both spatial and spectral details. This paper presents a Non-subsampled Contourlet Transform (NSCT) based multispectral image fusion model which integrates Principal Component Analysis (PCA), Phase congruency, directive contrast and entropy. The proposed methodology involves color transformation of input multispectral image. Two different fusion rules are then applied to the high-pass and low-pass subbands: Phase congruency is applied to low frequency coefficients and a combination of directive contrast and normalized Shannon entropy is applied to high frequency coefficients. The superiority of the fusion response is depicted by the comparisons made with the other state-of-the-art fusion approaches (in terms of various fusion metrics)

Improved Non-Linear Polynomial Filters for Contrast Enhancement of Breast Tumors

Non-Linear Polynomial Filters (NPF) consists of a framework of weighted coefficients of low-pass and high pass filters. This paper explores the applicability of NPF for the contrast enhancement of breast tumors in mammograms. NPF algorithm in the present work has been improved to provide controlled background suppression during the mammogram enhancement. This is because, in the process to control overshoots and visualization of tumor margins; the uncontrolled background suppression may lead to loss of finer details in the vicinity of the lesion region. Simulation results have shown that the response of the proposed NPF has been reasonably good on mammograms containing tumors embedded in different types of background tissues

A Polynomial filtering model for enhancement of mammogram lesions

This paper presents a preliminary analysis of a class of non-linear filters for enhancement of mammogram lesions. A non-linear filtering approach employing polynomial model of non-linearity is designed by second order truncation of Volterra series expansion. The proposed filter response is a linear combination of Type-0 and Type-II Volterra filters. Type-0 filter provides contrast enhancement, suppressing the ill-effects of background noise. On the other hand, Type-II filter employs edge enhancement. The objective analysis of the proposed filter is carried out by estimating values of quality parameters like CEM and PSNR on mammograms from MIAS and DDSM databases

Improvement of masses detection in digital mammograms employing non-linear filtering

Computer aided detection of mammographic masses can be improved to a greater extent employing non-linear filters for image enhancement. The present work proposes a truncated Volterra filter combination to provide contrast enhancement as well as texture based processing of masses in digital mammograms. Noteworthy improvement in visualization of masses has been observed in the simulation results carried out on cases from DDSM database. The improved performance of the proposed filtering approach is well supported with calculated values of objective evaluation parameters