Contrast enhancement of MRI images using morphological transforms and PSO

Medical imaging plays a crucial role in correct extraction of the significant information for monitoring the patient’s health and providing the quality treatment. A deluge of medical images requires initial interpretation for the presence of any abnormality, however, the correct diagnosis requires the images to be of good quality. To cope with the problem of poor contrast in medical images, this paper presents a method based on morphological transforms to improve the quality of the images. The proposed method incorporates Particle Swarm Optimization to find an optimum value of a parameter which controls the enhancement of the resulting image. The proposed algorithm is executed on a set of MRI images for testing its efficacy. The experimental results are compared in terms of both qualitative and quantitative parameters. The mean opinion score is obtained with the help of experts, which clearly shows the better performance of the proposed method. Furthermore, the parameters like Contrast Improvement Ratio, signal-to-noise ratio, peak signal-to-noise ratio, PL, and Structural Similarity Index are evident of better performance of proposed method when compared with the state-of-the-art methods and few recent methods. The comparison shows that the performance of the proposed method based on morphological transforms incorporating Particle Swarm Optimization is better not only visually but also in terms of other evaluation parameters.

     

Environment-friendly and cost-effective solution for flexible packaging printing process by advancement in engraving process

Clean Technologies and Environmental Policy - Organic solvents have been commonly used in the printing process for a long time in gravure printing applications. Using organic solvents in...     

Synthesis,characterization and polycondensation of Bis-(4-hydroxybutyl) terephthalate

Summary Monomeric Bis-(4-hydroxybutyl) terephthalate has been synthesized and characterized. Titanium iso-propoxide has been found to be a superior catalyst for transesterification compared to metal acetates. Further polycondensation of Bis (HBT) yields poly (1,4-butylene terephthalate). The effect of various titanium derived catalysts on the polycondensation of Bis (HBT) has been studied. The catalytic efficiency of these derivatives is ascribed to their relative hydrolytic stability and the degree of interaction between the carbonyl oxygen and the metal.     

Controlled-release delivery system for the alpha-MSH analog melanotan-I using poloxamer 407

The overall objective of these studies was to develop a controlled-release formulation of Melanotan-I (MT-I) containing poloxamer 407 (P407). Various aqueous formulations were evaluated containing MT-I and 25% w/v P407 alone, or with one of the following additives present, i.e., poly(vinylpyrrolidone) 10000 (PVP), methylcellulose (MC), or hydroxypropyl methylcellulose (HPMC). The in-vitro release profiles of MT-I from the P407 formulations and the dissolution of the gel were obtained simultaneously using a membraneless in-vitro model. These data were obtained at 37 degrees C and room temperature (24 degrees C). It was observed that the PVP-containing P407 formulations of MT-I accelerated the dissolution of gel and the release of the peptide compared to the control formulation. The formulations containing MC or HPMC exhibited the slowest dissolution rates and release of MT-I. The same rank order was observed for the dissolution and release profiles of MT-I from the various formulations at both temperatures. The in-vivo release kinetics of selected formulations were analyzed in guinea pigs following intraperitoneal administration. The plasma concentration-time profiles showed an extended release of the peptide formulated with gel compared to the intraperitoneal administration of MT-I in solution. On the basis of the in-vitro and in-vivo results, the P407 formulations of MT-I with MC or HPMC as an additive showed potential for use as a controlled-release delivery system for MT-I.     

Thermal load levelling of heat flux through an insulated thermal storage water wall

This communication presents an investigation of the thickness distribution of a given total thickness of the insulation inside and outside a thermal storage water wall for acheiving the maximum load levelling of the heat flux entering through the wall. Analysis is based on the solution of the heat conduction equation for the temperature distribution in the insulated wall subjected to periodic solar radiation and atmospheric air on one side and in contact with room air at constant temperature (corresponding to air-conditioned rooms) on the other side. an explicit solution for a temperature distribution satisfying the apporpriate boundary conditions at the surface has been derived to obtaing a periodic heat flux through the storage water wall. It is found that for a given total thickness (cost) of insulation the thicknesses of outside and inside insulation must be equal for best load levelling. Moreover, more load levelling is achieved when the whole of the insulation is outside rather than inside the thermal storage water wall.     

Innovation in non-contact ultrasonic analysis: applications for hidden objects detection

 This paper describes unusual applications of ultrasound that are based upon recent advances in transduction efficiency in air and gaseous media. Piezoelectric transducer devices – capable of generating extremely high intensity ultrasound in ambient air up to 5 MHz – have been successfully developed and tested. This is in addition to the obvious uses for gas analysis, remote sensing, liquid level, gap, and thickness measurement, and proximity analysis. These new generation transducers make it possible to analyze solids and liquids not only non-destructively, but also without physically contacting the test sample. Among various applications to the non-destructive characterization (NDC) of industrial materials, we also describe the feasibility of a forensic application. This concerns the detection of objects such as knives, guns, contraband drugs, dead bodies, etc., hidden inside false compartments or walls. Received: 15 October 1997 / Accepted: 24 October 1997     

Solar assisted method for recovery of bitumen from oil sand

A novel concept for the recovery of bitumen from oil sands in a natural gas limited environment with and without CO₂ constraints is presented. We suggest a feasible method for the recovery of unconventional oil in an environmentally friendly and sustainable way that has the potential of eliminating the need of natural gas as a process fuel. The proposed concept involves mid temperature steam generation for stimulating an oil sand formation. The steam is generated by utilizing solar radiation. The method uses the thermal mass of the oil sand formation to allow for cyclic steam injection during solar availability while still yielding continuous bitumen recovery. Feasibility assessments of the concept from both a thermodynamic and financial point of view are presented for a scenario of development in the Athabasca region in Alberta, Canada.     

Clustering assisted artificial neural network for handling noisy big data: An application for estimation of parameter in combined mode conduction and radiation heat transfer

A pattern net assisted mapping artificial neural network (PAMANN) model for estimation of parameters in problem with large data (1300 × 121 matrix size) is reported. A pattern net-based multilayer perceptron neural network (MLPNN) model for clustering the data, followed by mapping MLPNN model for mapping the target with the input, is developed as PAMANN model. A heat transfer problem with combined mode conduction and radiation in porous medium is solved numerically, and is called direct model. In the inverse model, a PAMANN model is developed by using data generated through the direct model. The PAMANN model is able to estimate two parameters (extinction coefficient β and convective coupling P₂) after taking temperature profile as input. The model is tested for different number of neurons in hidden layer, and different levels of noise in input data. Twelve different algorithms are explored in training of mapping MLPNN, and compared for performance. Levenberg–Marquardt algorithm is found to estimate the parameters with high accuracy, but took high CPU time. Bayesian regularization is found to consume very high CPU time with moderate accuracy in estimation of parameters. Variations in hidden layer neuron number and noise in input data, were done to analyze the performance of mapping MLPNN with different training algorithms. Algorithms O-Step Secant, conjugate gradient with Polak-Ribiére updates, and conjugate gradient with Fletcher-Reeves updates are able to handle all variations of noise and number of neurons in hidden layer, with good accuracy of estimation and low CPU time consumption. Under high computational resource LM algorithm can be used for all cases. Up to 0.99132 value of regression coefficient is obtained in mapping MLPNN model with 15 hidden neurons, indicating the high accuracy of the model. With the help of PAMANN model, highly accurate (absolute error 1.78%) estimation of parameters is obtained. The model can handle upto 1% noise in input data, while giving accurate results.