Enhancement and Restoration of Microscopic Images Corrupted with Poisson's Noise Using a Nonlinear Partial Differential Equation-based Filter

An inherent characteristic of the many imaging modalities such as fluorescence microscopy and other microscopic modalities is the presence of intrinsic Poisson noise that may lead to degradation of the captured image during its formation. A nonlinear complex diffusion-based filter adapted to Poisson noise is proposed in this paper to restore and enhance the degraded microscopic images captured by imaging devices having photon limited light detectors. The proposed filter is based on a maximum a posterior approach to the image reconstruction problem. The formulation of the filtering problem as maximisation of a posterior is useful because it allows one to incorporate the Poisson likelihood term as a data attachment which can be added to an image prior model. Here, the Gibb's image prior model-based on energy functional defined in terms of gradient norm of the image is used. The performance of the proposed scheme has been compared with other standard techniques available in literature such as Wiener filter, regularised filter, Lucy-Richardson filter and another proposed nonlinear anisotropic diffusion-based filter in terms of mean square error, peak signal-to-noise ratio, correlation parameter and mean structure similarity index map.The results shows that the proposed complex diffusion-based filter adapted to Poisson noise performs better in comparison to other filters and is better choice for reduction of intrinsic Poisson noise from the digital microscopic images and it is also well capable of preserving edges and radiometric information such as luminance and contrast of the restored image.Defence Science Journal, 2011, 61(5), pp.452-461, DOI:http://dx.doi.org/10.14429/dsj.61.118

PHYSIO-ANATOMICAL EXPLORATION OF ROLE OF MENTAL HEALTH IN ANNAVAHA SROTAS DISORDERS

The good health is defined not only on basis of physical well being but rather also on mental well being. As told in Ayurveda texts- “Prasanna Atmendriya Manah Swasth Ityabhidheeyate”. In era of globalization, the fast & modernized lifestyle has taken toll on one’s health. Now a day, majority of population is suffering from digestive problems such as acidity, reflux, bloating, stomach pain, constipation, and anorexia. These digestive problems are mainly due to bad dietary habits like junk foods, lack of balanced diet etc and also due lack of proper sleep, stress etc. Stress shows both long term and short term effect on gut functions like gastric secretions, gut motility, mucosal permeability etc. Our Acharas were well aware about this factor for digestive disorders, as they included Shoka, Bhaya, Krodha etc. as Nidana (cause) of digestive disorders. These various Nidana of digestive disorders causes Agnidushti which is cause of Annvaha srotodushti and Mandagni. It is very important to understand the relationship between gut and mental health to know aetio-pathogenesis of disorders, which will help to develop holistic approach for better treatment of Annavaha Srotas Disorders

Models for the Prediction of Receptor Tyrosine Kinase Inhibitory Activity of Substituted 3-Aminoindazole Analogues

The inhibition of tumor angiogenesis has become a compelling approach in the development of anticancer drugs. In the present study, topological models were developed through decision tree and moving average analysis using a data set comprising 42 analogues of 3-aminoindazoles. A total of 22 descriptors (distance based, adjacency based, pendenticity and distance-cum-adjacency based) were used. The values of all 22 topological indices for each analogue in the dataset were computed using an in-house computer program. A decision tree was constructed for the receptor tyrosine kinase KDR (kinase insert domain receptor) inhibitory activity to determine the importance of topological indices. The decision tree learned the information from the input data with an accuracy of 88%. Three independent topological models were also developed for prediction of receptor tyrosine kinase inhibitory (KDR) activity using moving average analysis. The models developed were also found to be sensitive towards the prediction of other receptor tyrosine kinases i.e. FLT3 (fms-like tyrosine kinase-3) and cKIT inhibitory activity. The accuracy of classification of single index based models using moving average analysis was found to be 88%. The performance of models was assessed by calculating precision, sensitivity, overall accuracy and Mathew’s correlation coefficient (MCC). The significance of the models was also assessed by intercorrelation analysis