Subjective and objective quality evaluation of synthetic and high dynamic range images

Recent years have seen a huge growth in the acquisition, transmission, and storage of videos. The visual data consists of both natural scenes as well as synthetic scenes, such as animated movies, cartoons and video games. In all these cases, the ultimate goal is to provide the viewers with a satisfactory quality-of-experience. In addition to the traditional 8-bit images, high dynamic range imaging is also becoming popular because of its ability to represent the real world luminances more realistically. Coming up with objective image quality assessment algorithms for these applications is an interesting research problem. In this work, I have developed a synthetic image quality database by introducing varying degrees of different types of distortions and conducted a subjective experiment in order to obtain the ground-truth data. I evaluated the performance of state-of-the-art image quality assessment algorithms (typically meant for natural images) on this database, especially no-reference algorithms that have not been applied to the domain of computer graphics images before. I identified the top-performing algorithms along with analyzing the types of distortions on which the present algorithms show a less impressive performance. For high dynamic range(HDR) images, I have designed two new full-reference image quality assessment algorithms to judge the quality of tonemapped HDR images using statistical features extracted from them. I have also conducted a massive online crowd-sourced subjective test for HDR image artifacts arising from tonemapping, multiple-exposure fusion and post processing. To the best of our knowledge, presently this is the largest HDR image database in the world involving the largest number of source images and most number of human evaluations. Based on the subjective evaluations obtained, I have also proposed machine learning based no-reference image quality assessment algorithms to predict the perceptual quality of HDR images.Electrical and Computer Engineerin

Medication errors reported in a tertiary care private hospital in Eastern India: a three years experience

Background: Medication errors (MEs) can cause significant harm to patients. The MEs identified through reporting processes currently report only a fraction of the actual number of MEs. Data about MEs is limited in India, especially from eastern and north-eastern parts of India. The objective of this study was to analyse the various types of Medication errors reported in a tertiary care private hospital in Eastern India. The aim was to determine the various factors associated with these errors and steps to be taken to reduce the MEs in this healthcare setup.Methods: We carried out a prospective passive surveillance study over the course of 3 years (2016-2018) on 50,822 admitted patients after obtaining approval from the Institutional Ethics Committee. A detailed root-cause analysis was performed for every reported error by a team of healthcare quality professionals and clinical pharmacists along with a clinical pharmacologist followed by appropriate preventive and corrective actions.Results: In our study, a total number of 88 medication errors were reported from a sample size of 50,822 (0.0017%). 61 of the reported MEs were administration errors (69.3%). Higher preponderance of medication errors was seen in male patients (53.1%) in comparison to female patients (46.9%).Conclusions: In this study gross under-reporting of MEs were observed which is in line with previously published studies in India. The reasons reported for gross under-reporting can function as an effective tool to ensure improved reporting of MEs and implementation of mitigation strategies

Multi-Objective Differential Evolution for Automatic Clustering with Application to Micro-Array Data Analysis

This paper applies the Differential Evolution (DE) algorithm to the task of automatic fuzzy clustering in a Multi-objective Optimization (MO) framework. It compares the performances of two multi-objective variants of DE over the fuzzy clustering problem, where two conflicting fuzzy validity indices are simultaneously optimized. The resultant Pareto optimal set of solutions from each algorithm consists of a number of non-dominated solutions, from which the user can choose the most promising ones according to the problem specifications. A real-coded representation of the search variables, accommodating variable number of cluster centers, is used for DE. The performances of the multi-objective DE-variants have also been contrasted to that of two most well-known schemes of MO clustering, namely the Non Dominated Sorting Genetic Algorithm (NSGA II) and Multi-Objective Clustering with an unknown number of Clusters K (MOCK). Experimental results using six artificial and four real life datasets of varying range of complexities indicate that DE holds immense promise as a candidate algorithm for devising MO clustering schemes

Evaluation of diabetic polyneuropathy in Type 2 diabetes mellitus by nerve conduction study and association of severity of neuropathy with serum sFasL level

Introduction: Diabetes mellitus (DM), a growing health problem globally, has reached epidemic proportions in India. Recently, Fas-mediated apoptosis has been proposed as a causative factor responsible for neuronal degeneration in diabetic polyneuropathy (DPN), but there are very few studies to show association of serum soluble Fas ligand (sFasL) level with severity of neuropathy. Aim and Objective: The aim of this study was to investigate whether serum sFasL, a transmembrane glycoprotein involved in apoptosis, has any association with severity of peripheral neuropathy in Type 2 DM. Materials and Methods: The study was conducted in Department of Physiology in collaboration with Department of Endocrinology, IPGME&R. sFasL levels in serum were assessed using ELISA method in healthy individuals (n = 16), newly diagnosed diabetic controls (n = 16) without any complications, and in DPN cases (n = 33) with predominant neuropathy only. All subjects underwent both electrodiagnostic procedures and vibration perception threshold (VPT) for quantitative assessment of the severity of neuropathy. Using nerve conduction studies, amplitudes, velocities, and latencies of both sensory and motor nerves were recorded. Results: In DPN patients, concentration of sFasL levels (87.53 ± 3.49) was significantly decreased (P < 0.0001) not only when compared with normal controls (225.30 ± 2.97) but also when compared with diabetic patients without any complication (161 ± 3.63). Moreover, the concentration of sFasL is significantly (P < 0.0001) associated with the severity of neuropathy both by VPT and nerve conduction velocity (NCV). Conclusion: Fas-mediated apoptosis is involved in Type 2 DM and might be associated with the severity of polyneuropathy