Diffeomorphic image registration with applications to deformation modelling between multiple data sets

Over last years, the diffeomorphic image registration algorithms have been successfully introduced into the field of the medical image analysis. At the same time, the particular usability of these techniques, in majority derived from the solid mathematical background, has been only quantitatively explored for the limited applications such as longitudinal studies on treatment quality, or diseases progression. The thesis considers the deformable image registration algorithms, seeking out those that maintain the medical correctness of the estimated dense deformation fields in terms of the preservation of the object and its neighbourhood topology, offer the reasonable computational complexity to satisfy time restrictions coming from the potential applications, and are able to cope with low quality data typically encountered in Adaptive Radiotherapy (ART). The research has led to the main emphasis being laid on the diffeomorphic image registration to achieve one-to-one mapping between images. This involves introduction of the log-domain parameterisation of the deformation field by its approximation via a stationary velocity field. A quantitative and qualitative examination of existing and newly proposed algorithms for pairwise deformable image registration presented in this thesis, shows that the log-Euclidean parameterisation can be successfully utilised in the biomedical applications. Although algorithms utilising the log-domain parameterisation have theoretical justification for maintaining diffeomorphism, in general, the deformation fields produced by them have similar properties as these estimated by classical methods. Having this in mind, the best compromise in terms of the quality of the deformation fields has been found for the consistent image registration framework. The experimental results suggest also that the image registration with the symmetrical warping of the input images outperforms the classical approaches, and simultaneously can be easily introduced to most known algorithms. Furthermore, the log-domain implicit group-wise image registration is proposed. By linking the various sets of images related to the different subjects, the proposed image registration approach establishes a common subject space and between-subject correspondences therein. Although the correspondences between groups of images can be found by performing the classic image registration, the reference image selection (not required in the proposed implementation), may lead to a biased mean image being estimated and the corresponding common subject space not adequate to represent the general properties of the data sets. The approaches to diffeomorphic image registration have been also utilised as the principal elements for estimating the movements of the organs in the pelvic area based on the dense deformation field prediction system driven by the partial information coming from the specific type of the measurements parameterised using the implicit surface representation, and recognising facial expressions where the stationary velocity fields are used as the facial expression descriptors. Both applications have been extensively evaluated based on the real representative data sets of three-dimensional volumes and two-dimensional images, and the obtained results indicate the practical usability of the proposed techniques

Segmentation of Doppler optical coherence tomography signatures using a support-vector machine

When processing Doppler optical coherence tomography images, there is a need to segment the Doppler signatures of the vessels. This can be used for visualization, for finding the center point of the flow areas or to facilitate the quantitative analysis of the vessel flow. We propose the use of a support-vector machine classifier in order to segment the flow. It uses the phase values of the Doppler image as well as texture information. We show that superior results compared to conventional simple threshold-based methods can be achieved in conditions of significant phase noise, which inhibit the use of a simple threshold of the phase values

LANDSAT-D investigations in snow hydrology

Work undertaken during the contract and its results are described. Many of the results from this investigation are available in journal or conference proceedings literature - published, accepted for publication, or submitted for publication. For these the reference and the abstract are given. Those results that have not yet been submitted separately for publication are described in detail. Accomplishments during the contract period are summarized as follows: (1) analysis of the snow reflectance characteristics of the LANDSAT Thematic Mapper, including spectral suitability, dynamic range, and spectral resolution; (2) development of a variety of atmospheric models for use with LANDSAT Thematic Mapper data. These include a simple but fast two-stream approximation for inhomogeneous atmospheres over irregular surfaces, and a doubling model for calculation of the angular distribution of spectral radiance at any level in an plane-parallel atmosphere; (3) incorporation of digital elevation data into the atmospheric models and into the analysis of the satellite data; and (4) textural analysis of the spatial distribution of snow cover

Doctor of Philosophy

dissertationCongenital heart defects are classes of birth defects that affect the structure and function of the heart. These defects are attributed to the abnormal or incomplete development of a fetal heart during the first few weeks following conception. The overall detection rate of congenital heart defects during routine prenatal examination is low. This is attributed to the insufficient number of trained personnel in many local health centers where many cases of congenital heart defects go undetected. This dissertation presents a system to identify congenital heart defects to improve pregnancy outcomes and increase their detection rates. The system was developed and its performance assessed in identifying the presence of ventricular defects (congenital heart defects that affect the size of the ventricles) using four-dimensional fetal chocardiographic images. The designed system consists of three components: 1) a fetal heart location estimation component, 2) a fetal heart chamber segmentation component, and 3) a detection component that detects congenital heart defects from the segmented chambers. The location estimation component is used to isolate a fetal heart in any four-dimensional fetal echocardiographic image. It uses a hybrid region of interest extraction method that is robust to speckle noise degradation inherent in all ultrasound images. The location estimation method's performance was analyzed on 130 four-dimensional fetal echocardiographic images by comparison with manually identified fetal heart region of interest. The location estimation method showed good agreement with the manually identified standard using four quantitative indexes: Jaccard index, Sørenson-Dice index, Sensitivity index and Specificity index. The average values of these indexes were measured at 80.70%, 89.19%, 91.04%, and 99.17%, respectively. The fetal heart chamber segmentation component uses velocity vector field estimates computed on frames contained in a four-dimensional image to identify the fetal heart chambers. The velocity vector fields are computed using a histogram-based optical flow technique which is formulated on local image characteristics to reduces the effect of speckle noise and nonuniform echogenicity on the velocity vector field estimates. Features based on the velocity vector field estimates, voxel brightness/intensity values, and voxel Cartesian coordinate positions were extracted and used with kernel k-means algorithm to identify the individual chambers. The segmentation method's performance was evaluated on 130 images from 31 patients by comparing the segmentation results with manually identified fetal heart chambers. Evaluation was based on the Sørenson-Dice index, the absolute volume difference and the Hausdorff distance, with each resulting in per patient average values of 69.92%, 22.08%, and 2.82 mm, respectively. The detection component uses the volumes of the identified fetal heart chambers to flag the possible occurrence of hypoplastic left heart syndrome, a type of congenital heart defect. An empirical volume threshold defined on the relative ratio of adjacent fetal heart chamber volumes obtained manually is used in the detection process. The performance of the detection procedure was assessed by comparison with a set of images with confirmed diagnosis of hypoplastic left heart syndrome and a control group of normal fetal hearts. Of the 130 images considered 18 of 20 (90%) fetal hearts were correctly detected as having hypoplastic left heart syndrome and 84 of 110 (76.36%) fetal hearts were correctly detected as normal in the control group. The results show that the detection system performs better than the overall detection rate for congenital heart defect which is reported to be between 30% and 60%

Development of a protocol to determine the sorting potential of particulate ore material

The objective of this research was to develop a protocol/ methodology to determine the potential for an ore to be sorted using sensor-based sorting. The research builds upon previous methodologies in literature to determine ore sortability. The first attempt to create a standard methodology to assess the amenability of an ore to sorting at a pilot-scale was developed by Fitzpatrick (2008). Tong (2012) developed a methodology to assess the amenability of an ore to sensor-based sorting on an ideal laboratory-scale. These methodologies focus on determining the upgrading potential of an ore based on ore sorting amenability tests. In order to gain further acceptance of sorting technology in the mining industry, Lessard et al. (2015) developed a method to determine the impact of ore sorting on an operation from an economic perspective. The protocol, developed during the current research, is used to determine the potential ore sortability based, firstly, on intrinsic particle properties and, secondly, based on laboratory-scale sensor sortability tests using ideal and industrial sensor measurement parameters. The intrinsic sortability results represent the ideal/ best- case sortability if a perfect separator existed and are calculated based on particle-by-particle ore characterisation. Ore that is intrinsically sortable is further assessed based on ideal laboratory-scale sensor sort ability tests using selected sensors. Ore sorting sensors that show potential based on the ideal sensor tests are further assessed by determining the sort ability of the ore using sensor measurement parameters similar to those used on industrial-scale ore sorting machine

Monitoring of wild pseudomonas biofilm strain conditions using statistical characterization of scanning electron microscopy images

This is the author accepted manuscript. The final version is available from the publisher via the DOI in this record.The present paper proposes a novel method of quantification of the variation in biofilm architecture, in correlation with the alteration of growth conditions that include variations of the substrate and conditioning layer. The polymeric biomaterials serving as substrates are widely used in implants and indwelling medical devices, while the plasma proteins serve as the conditioning layer. The present method uses descriptive statistics of field emission scanning electron microscopy (FESEM) images of biofilms obtained during a variety of growth conditions. We aim to explore here the texture and fractal analysis techniques, to identify the most discriminatory features which are capable of predicting the difference in biofilm growth conditions. We initially extract some statistical features of biofilm images on bare polymer surfaces, followed by those on the same substrates adsorbed with two different types of plasma proteins, viz., bovine serum albumin (BSA) and fibronectin (FN), for two different adsorption times. The present analysis has the potential to act as a futuristic technology for developing a computerized monitoring system in hospitals with automated image analysis and feature extraction, which may be used to predict the growth profile of an emerging biofilm on surgical implants or similar medical applications.SDS acknowledges the funding from the Department of Science and Technology (DST), Govt. of India through the Women’s Scientist Scheme – A, project no. LS-466/WOS A/2012-2013

A methodology for fast assessments to the electrical activity of barrel fields in vivo: from population inputs to single unit outputs

Here we propose a methodology to analyze volumetric electrical activity of neuronal masses in the somatosensory barrel field of Wistar rats. The key elements of the proposed methodology are a three-dimensional microelectrode array, which was customized by our group to observe extracellular recordings from an extended area of the barrel field, and a novel method for the current source density analysis. By means of this methodology, we were able to localize single barrels from their event-related responses to single whisker deflection. It was also possible to assess the spatiotemporal dynamics of neuronal aggregates in several barrels at the same time with the resolution of single neurons. We used simulations to study the robustness of our methodology to unavoidable physiological noise and electrode configuration. We compared the accuracy to reconstruct neocortical current sources with that obtained with a previous method. This constitutes a type of electrophysiological microscopy with high spatial and temporal resolution, which could change the way we analyze the activity of cortical neurons in the future

Deep learning applied to analyze patterns from evaporated droplets of Viscum album extracts.

This paper introduces a deep learning based methodology for analyzing the self-assembled, fractal-like structures formed in evaporated droplets. To this end, an extensive image database of such structures of the plant extract Viscum album Quercus [Formula: see text] was used, prepared by three different mixing procedures (turbulent, laminar, and diffusion based). The proposed pattern analysis approach is based on two stages: (1) automatic selection of patches that exhibit rich texture along the database; and (2) clustering of patches in accordance with prevalent texture by means of a Dense Convolutional Neural Network. The fractality of the patterns in each cluster is verified through Local Connected Fractal Dimension histograms. Experiments with Gray-Level Co-Occurrence matrices are performed to determine the benefit of the proposed approach in comparison with well established image analysis techniques. For the investigated plant extract, significant differences were found between the production modalities; whereas the patterns obtained by laminar flow showed the highest fractal structure, the patterns obtained by the application of turbulent mixture exhibited the lowest fractality. Our approach is the first to analyze, at the pure image level, the clustering properties of regions of interest within a database of evaporated droplets. This allows a greater description and differentiation of the patterns formed through different mixing procedures