Munendra Singh

Dual Modes and Dual Emissions of an Amino-Naphthoquinone Derivative

Abstract3-(1,4-Dioxo-1,4-dihydronaphthalen-2-yl-amino)benzoic acid shows multiple tunable fluorescence emissions depending on solvent, pH and wavelength of excitation. Independent dual fluorescence emissions are observed while exciting compound 3-(1,4-dioxo-1,4-dihydronaphthalen-2-yl-amino)benzoic acid in UV-region or in visible region. In methanol at low concentration it shows both S1-S0 emission and ESIPT emission at 307xa0nm and 480xa0nm. Whereas in concentrated solution S1-S0 emission as well as dimer-like emission at 307xa0nm and 425xa0nm respectively are observed. Upon excitation in visible region, it shows two emission bands in visible region which are highly dependent on concentration and are attributed to charge transfer emission and emissions of anionic species. Modulations of these emissions by varying conditions are established.

Optimized Multistable Stochastic Resonance for the Enhancement of Pituitary Microadenoma in MRI

Magnetic resonance imaging (MRI) is the modality of choice as far as imaging diagnosis of pathologies in the pituitary gland is concerned. Furthermore, the advent of dynamic contrast enhanced (DCE) has enhanced the capability of this modality in detecting minute benign but endocrinologically significant tumors called microadenoma. These lesions are visible with difficulty and a low confidence level in routine MRI sequences, even after administration of intravenous gadolinium. Techniques to enhance the visualization of such foci would be an asset in improving the overall accuracy of DCE-MRI for detection of pituitary microadenomas. The present study proposes an algorithm for postprocessing DCE-MRI data using multistable stochastic resonance (MSSR) technique. Multiobjective ant lion optimization optimizes the contrast enhancement factor (CEF) and anisotropy of an image by varying the parameters associated with the dynamics of MSSR. The marked regions of interest (ROIs) are labeled as normal and microadenoma of pituitary obtained with increased level of accuracy and confidence using proposed algorithm. The increased difference between the mean intensity curves obtained using these ROIs validated the obtained subjective results. Furthermore, the proposed MSSR-based algorithm has been evaluated on standard T1 and T2 weighted BrainWeb dataset images and quantified in terms of CEF, peak signal to noise ratio (PSNR), structure similarity index measure (SSIM), and universal quality index (UQI). The obtained mean values of CEF 1.22, PSNR 27.68, SSIM 0.75, UQI 0.83 for twenty dataset images were highest among considered contrast enhancement algorithms for the comparison.

Multi-objective noise estimator for the applications of de-noising and segmentation of MRI data

Abstract The present study proposes the noise estimation of Magnetic Resonance Imaging (MRI) data using multi-objective particle swarm optimisation (MOPSO). This adaptive noise estimation is based on the maximisation of the multiple quality measures, which enable the algorithm to achieve de-noising along with enhancement in the image features. The paper proposes two filtering approaches to de-noise MRI data. In first, MOPSO based noise estimation is followed by non-local statistics based Kalman filter, whereas, in the second approach, MOPSO based noise estimation is followed by Linear Minimum Mean Square Error (LMMSE) filter. The impact of de-noising on segmentation of MRI data has also been studied, for this purpose enhanced fuzzy c-means algorithm has been applied on filtered MRI data. The de-noising and segmentation performance of MOPSO-non local Kalman filter and MOPSO-LMMSE filters has been evaluated and compared with Wavelet filter, Wiener filter, non-local mean filter, standard Kalman and standard LMMSE filter. The proposed noise estimation approach followed by filtering is giving better de-noising and segmentation results as compared to standard filters considered.

An improved xie-beni index for cluster validity measure

The pathology may appear as a new cluster(s) on radiological images and hence the information of cluster location cannot decide in prior. In this regard, the unsupervised methods of segmentation play the important role, however, these methods need the number of clusters as the input. The challenging tasks in clustering based image segmentation are to choose the number of segments in an image. This work proposes the segmentation quality index, which utilizes the trend of Xie-Beni index to obtain the optimum number of segments in an image. The proposed algorithm has been implemented on the segmentation results obtained by enhanced fuzzy c-means algorithm and compared with the classical validity indexes such as Xie-Beni index, partition entropy coefficient, partition coefficient and fuzzy hyper-volume on synthetic images and simulated brain MRI dataset images. The quantitative results show that the proposed method has greater ability to find the appropriate number of clusters on the ground truth and noisy images.

CCDC 1531219: Experimental Crystal Structure Determination

Related Article: Krapa Shankar, Munendra Pal Singh, Jubaraj B. Baruah|2017|Inorg.Chim.Acta|469|440|doi:10.1016/j.ica.2017.09.055

Effect of image transformations on dynamic stochastic resonance based MR image enhancement

Dynamic Stochastic Resonance (DSR) utilizes the noise associated with the image itself to enhance the image quality. This paper analyzes the effects of Discrete Wavelet Transform (DWT), Discrete Cosine Transform (DCT) and Singular Value Decomposition (SVD) of image, which works as the input to bi-stable nonlinear system exhibiting DSR. The study performed on T1, fluid-attenuated inversion recovery (FLAIR) and diffusion weighted sequences of Magnetic Resonance Imaging (MRI). The images were quantified in terms of contrast enhancement factor and image anisotropy. The results show that DSR based image enhancement is helpful to obtain better tissue differentiation. The DCT based DSR produces better enhancement for diffusion-weighted images whereas DWT and SVD based DSR produces better enhancement of T1 and FLAIR weighted magnetic resonance images.