A DCT Based Filtering of Biomedical Images

Image filtering techniques have numerous potential applications in biomedical imaging and image processing. The design of filters largely depends on the a-priori knowledge about the type of noise corrupting the image and image features. This makes the standard filters to be application and image specific. The most popular filters such as average, Gaussian and Wiener reduce noisy artifacts by smoothing. However, this operation normally results in smoothing of the edges as well. On the other hand, sharpening filters enhance the high frequency details making the image non-smooth. An integrated general approach to design filters based on discrete cosine transform (DCT) is proposed in this study for optimal medical image filtering. This algorithm exploits the better energy compaction property of DCT and re-arrange these coefficients in a wavelet manner to get the better energy clustering at desired spatial locations. This algorithm performs optimal smoothing of the noisy image by preserving high and low frequency features. Evaluation results show that the proposed filter is robust under various noise distributions.

Trapped fermions in a synthetic non-Abelian gauge field

On increasing the coupling strength (lambda) of a non-Abelian gauge field that induces a generalized Rashba spin-orbit interaction, the topology of the Fermi surface of a homogeneous gas of noninteracting fermions of density rho similar to k(F)(3) undergoes a change at a critical value, lambda(T) approximate to k(F) [Phys. Rev. B 84, 014512 ( 2011)]. In this paper we analyze how this phenomenon affects the size and shape of a cloud of spin-1/2 fermions trapped in a harmonic potential such as those used in cold atom experiments. We develop an adiabatic formulation, including the concomitant Pancharatnam-Berry phase effects, for the one-particle states in the presence of a trapping potential and the gauge field, obtaining approximate analytical formulas for the energy levels for some high symmetry gauge field configurations of interest. An analysis based on the local density approximation reveals that, for a given number of particles, the cloud shrinks in a characteristic fashion with increasing.. We explain the physical origins of this effect by a study of the stress tensor of the system. For an isotropic harmonic trap, the local density approximation predicts a spherical cloud even for anisotropic gauge field configurations. We show, via a calculation of the cloud shape using exact eigenstates, that for certain gauge field configurations there is a systematic and observable anisotropy in the cloud shape that increases with increasing gauge coupling lambda. The reasons for this anisotropy are explained using the analytical energy levels obtained via the adiabatic approximation. These results should be useful in the design of cold atom experiments with fermions in non-Abelian gauge fields. An important spin-off of our adiabatic formulation is that it reveals exciting possibilities for the cold-atom realization of interesting condensed matter Hamiltonians by using a non-Abelian gauge field in conjunction with another potential. In particular, we show that the use of a spherical non-Abelian gauge field with a harmonic trapping potential produces a monopole field giving rise to a spherical geometry quantum Hall-like Hamiltonian in the momentum representation.

Towards sustainability: a new, solid-state synthetic route for supported metal nanocatalysts

Noble metal ions like Pt(IV) and Pd(II) were impregnated on gamma-alumina and aerosol 300 silica surfaces. Reduction of these ions using ammonia borane in the solid state resulted in the formation of the respective metal nanoparticles embedded in BNHx polymer which is dispersed on the oxide support. Removal of the BNH polymer was accomplished by washing the samples repeatedly with methanol. In this process the polymer undergoes solvolysis to release H-2 accompanied by the formation of ammonium methoxy borate salt, which has been removed by repeated methanol washings. As a result, metal nanoparticles well dispersed on gamma-alumina and aerosol 300 silica were obtained. These samples have been characterized by a combination of techniques, including electron microscopy, powder X-ray diffraction, NMR spectroscopy and surface area analyser.

Curcumin-glucoside, A Novel Synthetic Derivative of Curcumin, Inhibits alpha-Synuclein Oligomer Formation: Relevance to Parkinson's Disease

alpha-Synuclein aggregation is centrally implicated in Parkinson's disease (PD). It involves multi-step nucleated polymerization process via the formation of dimers, soluble toxic oligomers and insoluble fibrils. In the present study, we synthesized a novel compound viz., Curcumin-glucoside (Curc-gluc), a modified form of curcumin and studied its anti-aggregating potential with alpha-synuclein. Under aggregating conditions in vitro, Curc-gluc prevents oligomer formation as well as inhibits fibril formation indicating favorable stoichiometry for inhibition. The binding efficacies of Curc-gluc to both alpha-synuclein monomeric and oligomeric forms were characterized by micro-calorimetry. It was observed that titration of Curc-gluc with alpha-synuclein monomer yielded very low heat values with low binding while, in case of oligomers, Curc-gluc showed significant binding. Addition of Curc-gluc inhibited aggregation in a dose-dependent manner and enhanced alpha-synuclein solubility, which propose that Curc-gluc solubilizes the oligomeric form by disintegrating preformed fibrils and this is a novel observation. Overall, the data suggest that Curc-gluc binds to alpha-synuclein oligomeric form and prevents further fibrillization of alpha-synuclein; this might aid the development of disease modifying agents in preventing or treating PD.

Hierarchical Clustering Algorithm for Land Cover Mapping Using Satellite Images

This paper presents hierarchical clustering algorithms for land cover mapping problem using multi-spectral satellite images. In unsupervised techniques, the automatic generation of number of clusters and its centers for a huge database is not exploited to their full potential. Hence, a hierarchical clustering algorithm that uses splitting and merging techniques is proposed. Initially, the splitting method is used to search for the best possible number of clusters and its centers using Mean Shift Clustering (MSC), Niche Particle Swarm Optimization (NPSO) and Glowworm Swarm Optimization (GSO). Using these clusters and its centers, the merging method is used to group the data points based on a parametric method (k-means algorithm). A performance comparison of the proposed hierarchical clustering algorithms (MSC, NPSO and GSO) is presented using two typical multi-spectral satellite images - Landsat 7 thematic mapper and QuickBird. From the results obtained, we conclude that the proposed GSO based hierarchical clustering algorithm is more accurate and robust.

Synthetic studies toward geranylated PPAP natural products oblongifolin A, oblongifolin D, and enervosanone

A concise approach of general utility toward mono- and di-geranylated PPAP frameworks employing `reconstructive aldol cyclization' as the key step is delineated. (C) 2012 Elsevier Ltd. All rights reserved.

Model synthetic studies toward jiadifenin and majucin type seco-prezizaane natural products via a stereo- and enantioselective approach

A concise stereo- and enantioselective approach to seco-prezizaane sesquiterpenoids, leading to the acquisition of two bicyclic fragments, is delineated. (c) 2012 Elsevier Ltd. All rights reserved.

Interacting fermions in synthetic non-Abelian gauge fields

Generation and study of synthetic gauge fields has enhanced the possibility of using cold atom systems as quantum emulators of condensed matter Hamiltonians. In this article we describe the physics of interacting spin -1/2 fermions in synthetic non-Abelian gauge fields which induce a Rashba spin-orbit interaction on the motion of the fermions. We show that the fermion system can evolve to a Bose-Einstein condensate of a novel boson which we call rashbon. The rashbon-rashbon interaction is shown to be independent of the interaction between the constituent fermions. We also show that spin-orbit coupling can help enhancing superfluid transition temperature of weak superfluids to the order of Fermi temperature. A non-Abelian gauge field, when used in conjunction with another potential, can generate interesting Hamiltonians such as that of a magnetic monopole.

Metal-Ion Metathesis in Metal-Organic Frameworks: A Synthetic Route to New Metal-Organic Frameworks

A porous metalorganic framework, Mn(H3O)(Mn4Cl)(3)(hmtt)(8)] (POST-65), was prepared by the reaction of 5,5',10,10',15,15'-hexamethyltruxene-2,7,12-tricarboxylic acid (H(3)hmtt) with MnCl2 under solvothermal conditions. POST-65(Mn) was subjected to post-synthetic modification with Fe, Co, Ni, and Cu according to an ion-exchange method that resulted in the formation of three isomorphous frameworks, POST-65(Co/Ni/Cu), as well as a new framework, POST-65(Fe). The ion-exchanged samples could not be prepared by regular solvothermal reactions. The complete exchange of the metal ions and retention of the framework structure were verified by inductively coupled plasmaatomic emission spectrometry (ICP-AES), powder X-ray diffraction (PXRD), and BrunauerEmmettTeller (BET) surface-area analysis. Single-crystal X-ray diffractions studies revealed a single-crystal-to-single-crystal (SCSC)-transformation nature of the ion-exchange process. Hydrogen-sorption and magnetization measurements showed metal-specific properties of POST-65.

Digestive ripening: a synthetic method par excellence for core-shell, alloy, and composite nanostructured materials

The solvated metal atom dispersion (SMAD) method has been used for the synthesis of colloids of metal nanoparticles. It is a top-down approach involving condensation of metal atoms in low temperature solvent matrices in a SMAD reactor maintained at 77 K. Warming of the matrix results in a slurry of metal atoms that interact with one another to form particles that grow in size. The organic solvent solvates the particles and acts as a weak capping agent to halt/slow down the growth process to a certain extent. This as-prepared colloid consists of metal nanoparticles that are quite polydisperse. In a process termed as digestive ripening, addition of a capping agent to the as-prepared colloid which is polydisperse renders it highly monodisperse either under ambient or thermal conditions. In this, as yet not well-understood process, smaller particles grow and the larger ones diminish in size until the system attains uniformity in size and a dynamic equilibrium is established. Using the SMAD method in combination with digestive ripening process, highly monodisperse metal, core-shell, alloy, and composite nanoparticles have been synthesized. This article is a review of our contributions together with some literature reports on this methodology to realize various nanostructured materials.

A Huber-loss-driven clustering technique and its application to robust cell detection in confocal microscopy images

We address the problem of detecting cells in biological images. The problem is important in many automated image analysis applications. We identify the problem as one of clustering and formulate it within the framework of robust estimation using loss functions. We show how suitable loss functions may be chosen based on a priori knowledge of the noise distribution. Specifically, in the context of biological images, since the measurement noise is not Gaussian, quadratic loss functions yield suboptimal results. We show that by incorporating the Huber loss function, cells can be detected robustly and accurately. To initialize the algorithm, we also propose a seed selection approach. Simulation results show that Huber loss exhibits better performance compared with some standard loss functions. We also provide experimental results on confocal images of yeast cells. The proposed technique exhibits good detection performance even when the signal-to-noise ratio is low.

Multi-script and multi-oriented text localization from scene images

This paper describes a new method of color text localization from generic scene images containing text of different scripts and with arbitrary orientations. A representative set of colors is first identified using the edge information to initiate an unsupervised clustering algorithm. Text components are identified from each color layer using a combination of a support vector machine and a neural network classifier trained on a set of low-level features derived from the geometric, boundary, stroke and gradient information. Experiments on camera-captured images that contain variable fonts, size, color, irregular layout, non-uniform illumination and multiple scripts illustrate the robustness of the method. The proposed method yields precision and recall of 0.8 and 0.86 respectively on a database of 100 images. The method is also compared with others in the literature using the ICDAR 2003 robust reading competition dataset.

Automatic road extraction using high resolution satellite images based on Level Set and Mean Shift methods

Analysis of high resolution satellite images has been an important research topic for urban analysis. One of the important features of urban areas in urban analysis is the automatic road network extraction. Two approaches for road extraction based on Level Set and Mean Shift methods are proposed. From an original image it is difficult and computationally expensive to extract roads due to presences of other road-like features with straight edges. The image is preprocessed to improve the tolerance by reducing the noise (the buildings, parking lots, vegetation regions and other open spaces) and roads are first extracted as elongated regions, nonlinear noise segments are removed using a median filter (based on the fact that road networks constitute large number of small linear structures). Then road extraction is performed using Level Set and Mean Shift method. Finally the accuracy for the road extracted images is evaluated based on quality measures. The 1m resolution IKONOS data has been used for the experiment.

Molecular Design of Synthetic Benzimidazoles for the Switchover of the Duplex to G-quadruplex DNA Recognition

Benzimidazole derivatives are well known for their antibacterial, antiviral, anticonvulsant, antihistaminic, anthelmintic and antidepressant activities. Benzimidazole's unique base-selective DNA recognition property has been studied widely. However, most of the early benzimidazole systems have been targeted towards the binding of duplex DNA. Here we have shown the evolution and progress of the design and synthesis of new benzimidazole systems towards selective recognition of the double-stranded DNA first. Then in order to achieve selective recognition of the G-quadruplex DNA and utilize their potential as future anti-cancer drug candidates, we have demonstrated their selective cytotoxicity towards the cancer cells and potent telomerase inhibition ability.

Power-law transformation for enhanced recognition of born-digital word images

In this paper, we discuss the issues related to word recognition in born-digital word images. We introduce a novel method of power-law transformation on the word image for binarization. We show the improvement in image binarization and the consequent increase in the recognition performance of OCR engine on the word image. The optimal value of gamma for a word image is automatically chosen by our algorithm with fixed stroke width threshold. We have exhaustively experimented our algorithm by varying the gamma and stroke width threshold value. By varying the gamma value, we found that our algorithm performed better than the results reported in the literature. On the ICDAR Robust Reading Systems Challenge-1: Word Recognition Task on born digital dataset, as compared to the recognition rate of 61.5% achieved by TH-OCR after suitable pre-processing by Yang et. al. and 63.4% by ABBYY Fine Reader (used as baseline by the competition organizers without any preprocessing), we achieved 82.9% using Omnipage OCR applied on the images after being processed by our algorithm.