Morphological feature extraction for statistical learning with applications to solar image data

Abstract: Many areas of science are generating large volumes of digital image data. In order to take full advantage of the high-resolution and high-cadence images modern technology is producing, methods to automatically process and analyze large batches of such images are needed. This involves reducing complex images to simple representations such as binary sketches or numerical summaries that capture embedded scientific information. Using techniques derived from mathematical morphology, we demonstrate how to reduce solar images into simple ‘sketch ’ representations and numerical summaries that can be used for statistical learning. We demonstrate our general techniques on two specific examples: classifying sunspot groups and recognizing coronal loop structures. Our methodology reproduces manual classifications at an overall rate of 90 % on a set of 119 magnetogram and white light images of sunspot groups. We also show that our methodology is competitive with other automated algorithms at producing coronal loop tracings and demonstrate robustness through noise simulations. 2013 Wile

Methods for Analysing Endothelial Cell Shape and Behaviour in Relation to the Focal Nature of Atherosclerosis

The aim of this thesis is to develop automated methods for the analysis of the spatial patterns, and the functional behaviour of endothelial cells, viewed under microscopy, with applications to the understanding of atherosclerosis. Initially, a radial search approach to segmentation was attempted in order to trace the cell and nuclei boundaries using a maximum likelihood algorithm; it was found inadequate to detect the weak cell boundaries present in the available data. A parametric cell shape model was then introduced to fit an equivalent ellipse to the cell boundary by matching phase-invariant orientation fields of the image and a candidate cell shape. This approach succeeded on good quality images, but failed on images with weak cell boundaries. Finally, a support vector machines based method, relying on a rich set of visual features, and a small but high quality training dataset, was found to work well on large numbers of cells even in the presence of strong intensity variations and imaging noise. Using the segmentation results, several standard shear-stress dependent parameters of cell morphology were studied, and evidence for similar behaviour in some cell shape parameters was obtained in in-vivo cells and their nuclei. Nuclear and cell orientations around immature and mature aortas were broadly similar, suggesting that the pattern of flow direction near the wall stayed approximately constant with age. The relation was less strong for the cell and nuclear length-to-width ratios. Two novel shape analysis approaches were attempted to find other properties of cell shape which could be used to annotate or characterise patterns, since a wide variability in cell and nuclear shapes was observed which did not appear to fit the standard parameterisations. Although no firm conclusions can yet be drawn, the work lays the foundation for future studies of cell morphology. To draw inferences about patterns in the functional response of cells to flow, which may play a role in the progression of disease, single-cell analysis was performed using calcium sensitive florescence probes. Calcium transient rates were found to change with flow, but more importantly, local patterns of synchronisation in multi-cellular groups were discernable and appear to change with flow. The patterns suggest a new functional mechanism in flow-mediation of cell-cell calcium signalling

ARTMAP Neural Networks for Information Fusion and Data Mining: Map Production and Target Recognition Methodologies

The Sensor Exploitation Group of MIT Lincoln Laboratory incorporated an early version of the ARTMAP neural network as the recognition engine of a hierarchical system for fusion and data mining of registered geospatial images. The Lincoln Lab system has been successfully fielded, but is limited to target I non-target identifications and does not produce whole maps. Procedures defined here extend these capabilities by means of a mapping method that learns to identify and distribute arbitrarily many target classes. This new spatial data mining system is designed particularly to cope with the highly skewed class distributions of typical mapping problems. Specification of canonical algorithms and a benchmark testbed has enabled the evaluation of candidate recognition networks as well as pre- and post-processing and feature selection options. The resulting mapping methodology sets a standard for a variety of spatial data mining tasks. In particular, training pixels are drawn from a region that is spatially distinct from the mapped region, which could feature an output class mix that is substantially different from that of the training set. The system recognition component, default ARTMAP, with its fully specified set of canonical parameter values, has become the a priori system of choice among this family of neural networks for a wide variety of applications.Air Force Office of Scientific Research (F49620-01-1-0397, F49620-01-1-0423); Office of Naval Research (N00014-01-1-0624