An Improved Hybrid Model for Molecular Image Denoising

In this paper an improved hybrid method for removing noise from low SNR molecular images is introduced. The method provides an improvement over the one suggested by Jian Ling and Alan C. Bovik (IEEE Trans. Med. Imaging, 21(4), [2002]). The proposed model consists of two stages. The first stage consists of a fourth order PDE and the second stage is a relaxed median filter, which processes the output of fourth order PDE. The model enjoys the benefit of both nonlinear fourth order PDE and relaxed median filter. Apart from the method suggested by Ling and Bovik, the proposed method will not introduce any staircase effect and preserves fine details, sharp corners, curved structures and thin lines. Experiments were done on molecular images (fluorescence microscopic images) and standard test images and the results shows that the proposed model performs better even at higher levels of noise.     

Proteomic profiling of cancer of the gingivo-buccal complex: Identification of new differentially expressed markers

Tobacco-related oral cancer is the most common cancer among Indian males, gingivo-buccal complex (GBC) being the most affected subsite due to the habit of chewing tobacco. Proteins from the lysates of microdissected normal and transformed epithelium from clinically well-characterized tissue samples of the GBC were separated by two-dimensional gel electrophoresis to identify differentially expressed proteins. Eleven protein spots showed differential expression, which could withstand the stringency of statistical evaluation. The observations were confirmed with additional tissues. Nine of these differentiators were identified by MS as lactate dehydrogenase B, α-enolase, prohibitin, cathepsin D, apolipoprotein A-I, tumor protein translationally controlled-1, an SFN family protein, 14-3-3σ and tropomyosin. Cluster analysis indicated that these proteins, as a coexpressed set, could distinguish normal and transformed epithelium. Functionally, these differentiator molecules are relevant to the pathways and processes that have been previously implicated in oral carcinogenesis and could therefore be investigated further as a panel of markers for management of cancer of the GBC.     

Effective fuzzy c-means clustering algorithms for data clustering problems

Clustering is a well known technique in identifying intrinsic structures and find out useful information from large amount of data. One of the most extensively used clustering techniques is the fuzzy c-means algorithm. However, computational task becomes a problem in standard objective function of fuzzy c-means due to large amount of data, measurement uncertainty in data objects. Further, the fuzzy c-means suffer to set the optimal parameters for the clustering method. Hence the goal of this paper is to produce an alternative generalization of FCM clustering techniques in order to deal with the more complicated data; called quadratic entropy based fuzzy c-means. This paper is dealing with the effective quadratic entropy fuzzy c-means using the combination of regularization function, quadratic terms, mean distance functions, and kernel distance functions. It gives a complete framework of quadratic entropy approaching for constructing effective quadratic entropy based fuzzy clustering algorithms. This paper establishes an effective way of estimating memberships and updating centers by minimizing the proposed objective functions. In order to reduce the number iterations of proposed techniques this article proposes a new algorithm to initialize the cluster centers.In order to obtain the cluster validity and choosing the number of clusters in using proposed techniques, we use silhouette method. First time, this paper segments the synthetic control chart time series directly using our proposed methods for examining the performance of methods and it shows that the proposed clustering techniques have advantages over the existing standard FCM and very recent ClusterM-k-NN in segmenting synthetic control chart time series.     

Double bond migration of eugenol to isoeugenol over as-synthesized hydrotalcites and their modified forms

Double bond migration of eugenol to isoeugenol was carried out over as-synthesized hydrotalcites and their modified forms. The catalysts of general formula M(II)M(III)-xHT with carbonate as interlayer anion were synthesized by a co-precipitation method where M(II)=Mg, Ni, Co, Zn, Cu and M(III)=Al, Cr, Fe, La, V with varying M(II)/M(III) atomic compositions (here represented as ‘x’). The synthesized catalysts were tested for the reaction. Among various binary hydrotalcites investigated, Mg and Ni offered maximum activity, wherein MgAl-4HT showed nearly 73% conversion and NiAl-4HT showed 75% conversion with 15:85 cis:trans ratio at 200 °C with a substrate:catalyst mass ratio of 2:1. The other binary systems showed poor activity (less than 5%) under similar reaction conditions. The preservation of HT-like lattice is presumed to be crucial for this reaction, as evidenced from “in situ” powder X-ray diffraction (PXRD) and thermogravimetric (TG) analysis measurements. Variation in the trivalent metal ions indicated a maximum activity for Al, followed by Fe and Cr, in accordance with the crystallinity. A co-operative phenomenon was noted when both Mg and Ni were present together in a ternary MgNiAl-HT, however the activity varied with Mg/Ni atomic composition. Solvent variation studies indicated that more polar solvents favored the reaction. Significant promotional influence in the activity was noted with alkali and ruthenium impregnation on MgAl-4HT, wherein maximum activity was showed by catalysts modified with Cs (among the alkali metal ions studied) and higher content of ruthenium. Comparison of the activity with conventional bases such as KOH and KOBu^t revealed a superior performance of HT-based catalysts, although conventional bases had been used under stoichiometrically excess conditions (around 9% conversion for KOH with 1:10 and 5% conversion for KOBu^t with 1:3 substrate:catalyst mole ratio). The good performances of these catalysts encouraged further studies. A reaction mechanism involving the hydroxyl group of HT-like lattice is proposed for this isomerization reaction.     

Synthesis and characterization of methacrylate phospho-silicate hybrid for thin film applications

Phosphorus containing methacrylate hybrids were synthesized from 2-(methacryloyloxy)ethyl phosphate (EGMP) and 3-[(methacryloyloxy)propyl] trimethoxysilane (MEMO) via dual-cure process involving sol-gel reaction and addition polymerization. The kinetics of the reactions was established using spectroscopic techniques. Photoacoustic Fourier transform infrared spectroscopy (PA-FTIR) and X-ray photoelectron spectroscopy (XPS) confirm the formation of Si-O-Si, P-O-P and Si-O-P linkages and simultaneous polymerization of methacrylate groups leading to a dense networked structure. The presence of silicate/phospho-silicate network in the hybrid enhances its thermal stability. Nanoindentation measurements on thin films show enhanced hardness and modulus with increasing silicate network. Topographic and conductivity images obtained using micro-thermal analysis (μTA) reveal a dense, homogenous and defect-free thin film formed on metallic substrate with a T_g of 93 °C.     

A robust model for finding optimal evolutionary trees

Constructing evolutionary trees for species sets is a fundamental problem in computational biology. One of the standard models assumes the ability to compute distances between every pair of species, and seeks to find an edge-weighted treeT in which the distanced _ij ^T in the tree between the leaves ofT corresponding to the speciesi andj exactly equals the observed distance,d _ij . When such a tree exists, this is expressed in the biological literature by saying that the distance function or matrix isadditive, and trees can be constructed from additive distance matrices in0(n ²) time. Real distance data is hardly ever additive, and we therefore need ways of modeling the problem of finding the best-fit tree as an optimization problem.In this paper we present several natural and realistic ways of modeling the inaccuracies in the distance data. In one model we assume that we have upper and lower bounds for the distances between pairs of species and try to find an additive distance matrix between these bounds. In a second model we are given a partial matrix and asked to find if we can fill in the unspecified entries in order to make the entire matrix additive. For both of these models we also consider a more restrictive problem of finding a matrix that fits a tree which is not only additive but alsoultrametric. Ultrametric matrices correspond to trees which can be rooted so that the distance from the root to any leaf is the same. Ultrametric matrices are desirable in biology since the edge weights then indicate evolutionary time. We give polynomial-time algorithms for some of the problems while showing others to be NP-complete. We also consider various ways of fitting a given distance matrix (or a pair of upper- and lower-bound matrices) to a tree in order to minimize various criteria of error in the fit. For most criteria this optimization problem turns out to be NP-hard, while we do get polynomial-time algorithms for some.Supported by DIMACS under NSF Contract STC-88-09648.Supported by NSF Grant CCR-9108969.This work was begun while this author was visiting DIMACS in July and August 1992, and was supported in part by the U.S. Department of Energy under Contract DE-AC04-76DP00789.     

Root Mean Square filter for noisy images based on hyper graph model

In this paper, we propose a noise removal algorithm for digital images. This algorithm is based on hypergraph model of image, which enables us to distinguish noisy pixels in the image from the noise-free ones. Hence, our algorithm obviates the need for denoising all the pixels, thereby preserving as much image details as possible. The identified noisy pixels are denoised through Root Mean Square (RMS) approximation. The performance of our algorithm, based on peak-signal-to-noise-ratio (PSNR) and mean-absolute-error (MAE), was studied on various benchmark images, and found to be superior to that of other traditional filters and other hypergraph based denoising algorithms.     

Novel segmentation algorithm in segmenting medical images

The aim of this paper is to develop an effective fuzzy c-means (FCM) technique for segmentation of Magnetic Resonance Images (MRI) which is seriously affected by intensity inhomogeneities that are created by radio-frequency coils. The weighted bias field information is employed in this work to deal the intensity inhomogeneities during the segmentation of MRI. In order to segment the general shaped MRI dataset which is corrupted by intensity inhomogeneities and other artifacts, the effective objective function of fuzzy c-means is constructed by replacing the Euclidean distance with kernel-induced distance. In this paper, the initial cluster centers are assigned using the proposed center initialization algorithm for executing the effective FCM iteratively. To assess the performance of proposed method in comparison with other existed methods, experiments are performed on synthetic image, real breast and brain MRIs. The clustering results are validated using Silhouette accuracy index. The experimental results demonstrate that our proposed method is a promising technique for effective segmentation of medical images.     

The direct discontinuous Galerkin (DDG) viscous flux scheme for the high order spectral volume method

The direct discontinuous Galerkin (DDG) method was developed by Liu and Yan to discretize the diffusion flux. It was implemented for the discontinuous Galerkin (DG) formulation. In this paper, we perform four tasks: (i) implement the direct discontinuous Galerkin (DDG) scheme for the spectral volume method (SV) method, (ii) design and implement two variants of DDG (called DDG2 and DDG3) for the SV method, (iii) perform a Fourier type analysis on both methods when solving the 1D diffusion equation and combine the above with a non-linear global optimizer, to obtain modified constants that give significantly smaller errors (in 1D), (iv) use the above coefficients as starting points in 2D. The dissipation properties of the above schemes were then compared with existing flux formulations (local discontinuous Galerkin, Penalty and BR2). The DDG, DDG2 and DDG3 formulations were found to be much more accurate than the above three existing flux formulations. The accuracy of the DDG scheme is heavily dependent on the penalizing coefficient for the odd ordered schemes. Hence a loss of accuracy was observed even for mildly non-uniform grids for odd ordered schemes. On the other hand, the DDG2 and DDG3 schemes were mildly dependent on the penalizing coefficient for both odd and even orders and retain their accuracy even on highly irregular grids. Temporal analysis was also performed and this yielded some interesting results. The DDG and its variants were implemented in 2D (on triangular meshes) for Navier–Stokes equations. Even the non-optimized versions of the DDG displayed lower errors than the existing schemes (in 2D). In general, the DDG and its variants show promising properties and it indicates that these approaches have a great potential for higher dimension flow problems.     

Effective fuzzy clustering techniques for segmentation of breast MRI

The goal of this work is to segment the breast into different regions, each corresponding to a different tissue, and to identify tissue regions judged abnormal, based on the signal enhancement-time information. There are a number of problems that render this task complex. Breast MRI segmentation based on the differential enhancement of image intensities can assist the clinician to detect suspicious regions. In this paper, we propose an effective segmentation method for breast contrast-enhanced MRI (ce-MRI). The segmentation method is developed based on standard fuzzy clustering techniques proposed by Bezedek. By minimizing the proposed effective objective function, this paper obtains an effective way of predicting membership grades for objects and new method to update centers. Experiments will be done with a synthetic image to show how effectively the new proposed effective fuzzy c-means (FCM) works in obtaining clusters. To show the performance of proposed FCM, this work compares the results with results of standard FCM algorithm on same synthetic image. Then the proposed method was applied to segment the clinical ce-MR images with the help of computer programing language and results have been shown visually.