Bayesian inference in a hidden stochastic two-compartment model for feline hematopoiesis.

In this paper, we describe a hidden two-compartment stochastic process used to model the kinetics of feline hematopoietic stem cells (HSCs) in continuous time. Because of the experimental design and data collection scheme, the inferential task presents numerous challenges. While the hematopoietic process evolves in continuous time, the observations are collected only at discrete irregular times and are a probabilistic function of the state of the process. In addition, the animals go through an experimental procedure such that their reserve of HSCs is severely depleted at the start of the observation period. This impedes any approximation of the hematopoietic process with a continuous state-space process (normal approximation of the transition probabilities would be inaccurate when the state of the process, i.e. the number of stem cells, is small). We implement a Markov chain Monte Carlo algorithm that allows us to estimate the posterior distribution of the parameters of the hematopoietic process while maintaining its state-space discrete (i.e. without using any approximation). We show the performance of the algorithm on simulated data. Finally, we apply the algorithm to data on multiple experimental cats and provide estimates of the rates of the fates of feline HSCs. The obtained estimates are in agreement with the estimates obtained with different methods published in the medical literature. However, the proposed approach makes a more efficient use of the data and hence the parameter estimates are much more accurate than the one obtained with the methods previously proposed.     

Triangular geometry-based novel feature and a novel algorithm for human identification using dental radiographs

Abstract

Dental X-ray has played an important role in identification of missing or unidentified persons. Particularly in cases where other identification clues like fingerprint, iris, etc. are not available and, moreover, dental features remain more or less invariant over time. The purpose of dental image processing is to match the Post-mortem (PM) radiograph with the Antemortem (AM) radiograph based on some unique feature of the radiograph. The first step is to enhance the quality of image and region of interest can be separated. Unique features of a tooth are extracted and identification is performed based on matching of these feature vectors of PM images with those of AM images available in the database. A new feature based on triangular geometry of the tooth has been proposed and thereafter matching of query and database images is performed for identification of the subject. This feature is called the tooth taper parameter.