An automated pattern recognition system for the quantification of inflammatory cells in hepatitis-C-infected liver biopsies

This paper presents an automated system for the quantification of inflammatory cells in hepatitis-C-infected liver biopsies. Initially, features are extracted from colour-corrected biopsy images at positions of interest identified by adaptive thresholding and clump decomposition. A sequential floating search method and principal component analysis are used to reduce dimensionality. Manually annotated training images allow supervised training. The performance of Gaussian parametric and mixture models is compared when used to classify regions as either inflammatory or healthy. The system is optimized using a response surface method that maximises the area under the receiver operating characteristic curve. This system is then tested on images previously ranked by a number of observers with varying levels of expertise. These results are compared to the automated system using Spearman rank correlation. Results show that this system can rank 15 test images, with varying degrees of inflammation, in strong agreement with five expert pathologists

Robust detection and classification of biomedical cell specimens from light microscope images

Ph.DDOCTOR OF PHILOSOPH

Control for Localization and Visibility Maintenance of an Independent Agent using Robotic Teams

Given a non-cooperative agent, we seek to formulate a control strategy to enable a team of robots to localize and track the agent in a complex but known environment while maintaining a continuously optimized line-of-sight communication chain to a fixed base station. We focus on two aspects of the problem. First, we investigate the estimation of the agent\u27s location by using nonlinear sensing modalities, in particular that of range-only sensing, and formulate a control strategy based on improving this estimation using one or more robots working to independently gather information. Second, we develop methods to plan and sequence robot deployments that will establish and maintain line-of-sight chains for communication between the independent agent and the fixed base station using a minimum number of robots. These methods will lead to feedback control laws that can realize this plan and ensure proper navigation and collision avoidance

Automatic analysis of malaria infected red blood cell digitized microscope images

Malaria is one of the three most serious diseases worldwide, affecting millions each year, mainly in the tropics where the most serious illnesses are caused by Plasmodium falciparum. This thesis is concerned with the automatic analysis of images of microscope slides of Giemsa stained thin-films of such malaria infected blood so as to segment red-blood cells (RBCs) from the background plasma, to accurately and reliably count the cells, identify those that were infected with a parasite, and thus to determine the degree of infection or parasitemia. Unsupervised techniques were used throughout owing to the difficulty of obtaining large quantities of training data annotated by experts, in particular for total RBC counts. The first two aims were met by optimisation of Fisher discriminants. For RBC segmentation, a well-known iterative thresholding method due originally to Otsu (1979) was used for scalar features such as the image intensity and a novel extension of the algorithm developed for multi-dimensional, colour data. Performance of the algorithms was evaluated and compared via ROC analysis and their convergence properties studied. Ways of characterising the variability of the image data and, if necessary of mitigating it, were discussed in theory. The size distribution of the objects segmented in this way indicated that optimisation of a Fisher discriminant could be further used for classifying objects as small artefacts, singlet RBCs, doublets, or triplets etc. of adjoining cells provided optimisation was via a global search. Application of constraints on the relationships between the sizes of singlet and multiplet RBCs led to a number of tests that enabled clusters of cells to be reliably identified and accurate total RBC counts to be made. Development of an application to make such counts could be very useful both in research laboratories and in improving treatment of malaria. Unfortunately, the very small number of pixels belonging to parasite infections mean that it is difficult to segment parasite objects and thus to identify infected RBCs and to determine the parasitemia. Preliminary attempts to do so by similar, unsupervised means using Fischer discriminants, even when applied in a hierarchical manner, though suggestive that it may ultimately be possible to develop such a system remain on the evidence currently available, inconclusive. Appendices give details of material from old texts no longer easily accessible

Scalable parallel simulation of small-scale structures in cold dark matter

Thesis (Ph. D.)--Massachusetts Institute of Technology, Dept. of Physics, 2005.Includes bibliographical references (p. 179-181).We present a parallel implementation of the particle-particle/particle-mesh (P³M) algorithm for distributed memory clusters. The llp3m-hc code uses a hybrid method for both computation and domain decomposition. Long-range forces are computed using a Fourier transform gravity solver on a regular mesh; the mesh is distributed across parallel processes using a static one-dimensional slab domain decomposition. Short-range forces are computed by direct summation of close pairs; particles are distributed using a dynamic domain decomposition based on a space-filling Hilbert curve. A nearly-optimal method was devised to dynamically repartition the particle distribution so as to maintain load balance even for extremely inhomogeneous mass distributions. Tests using 800³ simulations on a 40-processor Beowulf cluster showed good load balance and scalability up to 80 processes. We discuss the limits on scalability imposed by communication and extreme clustering and suggest how they may be removed by extending our algorithm to include a new adaptive P³M technique, which we then introduce and present as a new llap3m-hc code. We optimize free parameters of adaptive P³M to minimize force errors and the timing required to compute short range forces. We apply our codes to simulate small scale structure of the universe at redshift z > 50. We observe and analyze the formation of caustics in the structure and compare it with the predictions of semi-analytic models of structure formation. The current limits on neutralino detection experiments assume a Maxwell-Boltzmann velocity distribution and smooth spatial distribution of dark matter.(cont.) It is shown in this thesis that inhomogeneous distribution of dark matter on small scales significantly changes the predicted event rates in direct detection dark matter experiments. The effect of spatial inhomogeneity weakens the upper limits on neutralino cross section produced in the Cryogenic Dark Matter Search Experiment.by Alexander V. Shirokov.Ph.D