Perceptual texture similarity estimation

This thesis evaluates the ability of computational features to estimate perceptual texture similarity. In the first part of this thesis, we conducted two evaluation experiments on the ability of 51 computational feature sets to estimate perceptual texture similarity using two differ-ent evaluation methods, namely, pair-of-pairs based and retrieval based evaluations. These experiments compared the computational features to two sets of human derived ground-truth data, both of which are higher resolution than those commonly used. The first was obtained by free-grouping and the second by pair-of-pairs experiments. Using these higher resolution data, we found that the feature sets do not perform well when compared to human judgements. Our analysis shows that these computational feature sets either (1) only exploit power spectrum information or (2) only compute higher order statistics (HoS) on, at most, small local neighbourhoods. In other words, they cannot capture aperiodic, long-range spatial relationships. As we hypothesise that these long-range interactions are important for the human perception of texture similarity we carried out two more pair-of-pairs ex-periments, the results of which indicate that long-range interactions do provide humans with important cues for the perception of texture similarity. In the second part of this thesis we develop new texture features that can encode such data. We first examine the importance of three different types of visual information for human perception of texture. Our results show that contours are the most critical type of information for human discrimination of textures. Finally, we report the development of a new set of contour-based features which performed well on the free-grouping data and outperformed the 51 feature sets and another contour type feature set with the pair-of-pairs data

Spatial pattern and its development in mid- to late-successional tree communities in unmanaged boreal forests in northern Finland

This study examined the development of tree communities from mid- to late-successional stages in unmanaged Hylocomium-Myrtillus (HMT) forests in northern Finland using a chronosequence approach. More specifically, this thesis adressed the following study questions: (1) What is the spatial pattern of the overall tree community and does this overall pattern changes as the forest`s successional stage changes from mid- to late-successional? (2) What are the spatial patterns of small and large trees and how they differ from mid- to late-successional stages? (3) Is the occurrence of P. abies is related to Betula spp. in mid- to late-successional stages and does this potential relation changes as the forest`s successional stage changes from mid- to late-successional? (4) Does the tree population displays a mosaic of small patches of P. abies and Betula spp. trees over succession from mid- to late-successional stages? The study was carried out in the Värriö Strict Nature Reserve in north-eastern Finland in 2011. Living and dead trees were recorded within 3 transects (300 m long, 40 m wide) in 3 different stands representing differing midto late-succesional stages from 180 years to at least 350 years. The stands were classified according to their species composition in Betula spp. dominated, mixed P. abies-Betula spp. and P. abies dominated. Finally, spatial patterns were analysed using Ripley`s K-function. The spatial tree patterns were predominantly clustered and this pattern did not vary much over succession. Small trees were generally more clustered than larger trees and regular distributions did not occur. Saplings of both species were predominantly attracted to mature trees of the same species. This, and the repulsion between living mature P. abies and mature Betula spp. suggests clumps composed of only one species and thereby a mosaic of small patches of P. abies and Betula spp. in mid- to late-successional forest stages. At the same time, a successive dependency of P. abies on Betula spp can be rejected. The lack of regular distributions implies a minor importance of competition in governing the spatial pattering of HMT-forests. In conclusion, suitable regeneration microsites and vegetative regeneration strategies can be assumed to outrun competitive effects on spatial structure in the presence of a thick raw humus layer in HMT-forests. Despite evidence of facilitative effects due the observed intensive clumping, the facilitative effects of Betula spp. on P. abies reported by Doležal et al. (2006) could not be shown in this study. This study thus suggests that facilitation is merely restricted to inter-species tree-to-tree interactions. HMT-forests have barely been studied to date. Concrete scientific benefits by this study are found in its contribution to investigate the actual point process that generates the observed patterns by fitting appropriate point-process models to the observed pattern and evaluate their power. Ultimately, the results derived from this study could thereby contribute to formulate plausible hypotheses concerning the causes for spatial pattering in HMT-forests which could be tested in experimental studies

Analysing Deep Reinforcement Learning Agents Trained with Domain Randomisation

Peer reviewedPublisher PD

Statistical methods for the analysis of high-content organotypic cancer cell culture imaging data

Organotypic cancer cell cultures combined with modern imaging technology have greatly expanded the possibilities of in vitro cancer research and drug development. In fact, imaging and subsequent image analyses have become a main component for high content screening in early stage drug discovery. The scale of such screening campaigns is rapidly growing, while at the same time, cell cultures become increasingly complex and now also include multicellular organoids in three-dimensional cultures. As a result of these imaging experiments, large amounts of image data are generated, posing ever-increasing demands to the related analysis methodology. In this doctoral thesis, novel and efficient statistical methods are introduced to meet these demands, spanning a variety of research topics in both statistics and machine learning. As a starting point, the preprocessing and segmentation of the image data are described, leading to the statistical analysis of treatment effects through descriptive features of the multicellular structures. A novel flexible finite mixture regression model is introduced in this context to account for the intra-tumor heterogeneity in the cultures. To gain a more direct interpretation for the treatment effects, an unsupervised analysis sequence is proposed leading to the phenotypic grouping of the cell structures. This is achieved by using a selected set of feature principal components as inputs for clustering algorithms. Finally, the problem of global level novelty detection is formulated and tackled with permutation tests. While the feature analysis and clustering approaches deal with very specific applications, the flexible FMR and global level novelty detection methods represent more abstract problems that are inspired by the challenges in image analysis but are not directly motivated by them. The application of all methods is demonstrated with a real cancer culture dataset in the introductory part of this thesis

A generic framework for colour texture segmentation

This thesis proposes a novel method to combine the colour and the texture for colour texture segmentation. The objective of this research work is to derive a framework for colour texture segmentation and to determine the contribution of colour in colour texture analysis. The colour texture processing is based on the feature extraction from colour-textured images. The texture features were obtained from the luminance plane along with the colour features from the chrominance planes. Based on the above mentioned approach, a method was developed for colour texture segmentation. The proposed method unifies colour and texture features to solve the colour texture segmentation problem. Two of the grey scale texture analysis techniques, Local Binary Pattern (LBP) and Discrete Cosine Transform (DCT) based filter approach were extended to colour images. An unsupervised fc-means clustering was used to cluster pixels in the chrominance planes. Non-parametric test was used to test the similarity between colour texture regions. An unsupervised texture segmentation method was followed to obtain the segmented image. The evaluation of the segmentation was based on the ROC curves. A quantitative estimation of colour and texture performance in segmentation was presented. The use of different colour spaces was also investigated in this study. The proposed method was tested using different mosaic and natural images obtained from VisTex and other predominant image database used in computer vision. The applications for the proposed colour texture segmentation method are, Irish Script On Screen (ISOS) images for the segmentation of the colour textured regions in the document, skin cancer images to identify the diseased area, and Sediment Profile Imagery (SPI) to segment underwater images. The inclusion of colour and texture as distributions of regions provided a good discrimination of the colour and the texture. The results indicated that the incorporation of colour information enhanced the texture analysis techniques and the methodology proved effective and efficient

A Novel Statistical-based Approach for 3-D Surface Detection

The field of medical image analysis is concerned with the extraction of salient information from complex digital imagery. Developments in image acquisition tools has given rise to a number of 2-D and 3-D digital image domains that are capable of mapping the anatomy and internal structures of a patient in an non-invasive fashion. The data produced by these tools is inherently complex, and a number of image processing techniques are commonly applied to simplify the images in order to extract the information deemed most relevant. Image segmentation is one commonly applied process which partitions a digital image into multiple segments that correspond to various structures within the data. The goal of segmentation is to change the representation of an image into something that is easier to visualise and analyse in complex tasks (i.e. targeted clinical treatment planning). Often these segmentation tasks are performed manually by an expert clinician, however the task of drawing object contours is a time consuming process subject to human biases and interpretation. Automated and semi-automated segmentation is a complex, non-trivial process reliant on a number of pre-processing stages which first extract the spatial structural information contained within the image. For 2-D images, the structural information is contained within edge features and there are a number of edge detection algorithms in the literature which have been extensively appraised. For 3-D images this structural information is contained within the surface features, and while surface detection algorithms exist, their development is immature compared to edge detection and formal evaluation in the literature is largely absent. Furthermore, recent developments in statistical methods for 2-D edge feature extraction have showed promise in resolving 2-D structural information in medical data, however no work has yet explored these approaches in 3-D. In this thesis two novel methods of statistical surface detection are presented, which contribute to the field by transferring approaches of 2-D statistical edge detection into3-D. The proposed methods optimise the resolving power of the 2-D statistical methods while providing accurate surface detection in the x, y and z dimensions of images. The methods are presented with a range of parametric and non-parametric statistical tests which were extensively analysed using both qualitative and objective methods. In addition, the framework for evaluation is an additional novel contribution in this work, which considers individual aspects of surface detection performance, such as the effects of the statistical properties of the regions within the image, the impact of surface topology, and the response to multiple distinct regions present within the image. A comprehensive dataset of controlled interfaces is developed, and performance of the surface detection algorithms were judged using a novel fast implementation of F-measure analysis against ground truth solutions which is new to this work. The surface detection methods are also analysed on real MRI data, and their performance was qualitatively assessed on the ability to detect brain tumour boundaries and structural pathologies in paediatric patient data. For a comparison against the state of art in surface detection, the methods evaluated in the thesis were compared against two existing baseline approaches, namely the 3-D Canny method, and 3-D Steerable filters. The results of the evaluations reveal that the proposed 3-D statistical method for surface detection offers improved detection of surfaces on synthetic data with varying interface and topology considerations. Furthermore, the proposed methods improve detection of surfaces when the variability in image intensity high, such as within regions of texture, which is suitable for delineating regions of complex structure in MRI data. Additionally, the statistical methods were able to match the performance of the baseline methods under conditions considered optimal for the baseline approaches, such as the detection of surfaces with a strong intensity differential. Furthermore, the proposed methods of surface detection are shown to be suitable for real 3-D data which is anisotropic in resolution, namely on MRI imagery where the z-spacing within the dataset is often of poor resolution. Provided as a recommendation for further work, the best performing techniques are presented, notably these were the χ2 and Student t-test statistical methods. Characteristically these methods produced strong magnitude surfaces with good connectivity on real and synthetic data, with the χ2 test also achieving a good suppression of image noise. Therefore, illustrating the potential of this novel method of 3-D surface detection for medical image analysis applications

Optic Nerve Head Image Analysis for Glaucoma Progression Detection

Glaucoma is a leading cause of visual disability across the world and when diagnosed the glaucoma patient will spend the rest of their life receiving treatment in managed clinical care. In the glaucoma clinic, retinal and optic nerve head (ONH) imaging can be used to help the clinician to manage patient treatment appropriately. By providing high resolution images of the optic nerve head structures and identifying changes therein related to disease onset and progression, an objective measure can be obtained as to how well or badly treatment is preventing further disease damage. This thesis contributes to the field of glaucoma progression detection by the analysis of clinical imaging data using confocal scanning laser tomography (CSLT). Primarily it is an investigation of how best to appraise and optimise current algorithms which aim to detect these glaucomatous structural changes in the optic nerve head. This is done by addressing how the performance of these methods can be best assessed in the absence of a gold standard for glaucomatous structural progression. Glaucoma expert assessment of photographs of the optic disc is the current clinical standard of assessing glaucomatous damage evident in the ONH. This is used in this thesis to act as a reference standard by which these algorithms can be compared. In addition, the statistical principles underpinning trend detection techniques are also investigated along with the performance of these techniques to detect trends in CSLT data in the presence of different types of measurement noise and image quality. A new computer model is developed and validated to simulate stable series of CSLT images, with realistic variability, which can be used to benchmark the false-positive rates of current and future progression algorithms. In conclusion, the main results reported in this thesis show that uncertainties involved in expert assessment of change in ONH photographs limits this as a reference standard for structural change in glaucoma. In addition, since stability in clinical datasets is uncertain, simulation using modelled series is shown to provide a new benchmark for comparing methods of progression detection

Detecting changes in high frequency data streams, with applications

In recent years, problems relating to the analysis of data streams have become widespread. A data stream is a collection of time ordered observations x1, x2, ... generated from the random variables X1, X2, .... It is assumed that the observations are univariate and independent, and that they arrive in discrete time. Unlike traditional sequential analysis problems considered by statisticians, the size of a data stream is not assumed to be fixed, and new observations may be received over time. The rate at which these observations are received can be very high, perhaps several thousand every second. Therefore computational efficiency is very important, and methods used for analysis must be able to cope with potentially huge data sets. This paper is concerned with the task of detecting whether a data stream contains a change point, and extends traditional methods for sequential change detection to the streaming context. We focus on two different settings of the change point problem. The first is nonparametric change detection where, in contrast to most of the existing literature, we assume that nothing is known about either the pre- or post-change stream distribution. The task is then to detect a change from an unknown base distribution F0 to an unknown distribution F1. Further, we impose the constraint that change detection methods must have a bounded rate of false positives, which is important when it comes to assessing the significance of discovered change points. It is this constraint which makes the nonparametric problem difficult. We present several novel methods for this problem, and compare their performance via extensive experimental analysis. The second strand of our research is Bernoulli change detection, with application to streaming classification. In this setting, we assume a parametric form for the stream distribution, but one where both the pre- and post-change parameters are unknown. The task is again to detect changes, while having a control on the rate of false positives. After developing two different methods for tackling the pure Bernoulli change detection task, we then show how our approach can be deployed in streaming classification applications. Here, the goal is to classify objects into one of several categories. In the streaming case, the optimal classification rule can change over time, and classification techniques which are not able to adapt to these changes will suffer performance degradation. We show that by focusing only on the frequency of errors produced by the classifier, we can treat this as a Bernoulli change detection problem, and again perform extensive experimental analysis to show the value of our methods

Mapping the structure of Borneo's tropical forests across a degradation gradient

South East Asia has the highest rate of lowland forest loss of any tropical region, with logging and deforestation for conversion to plantation agriculture being flagged as the most urgent threats. Detecting and mapping logging impacts on forest structure is a primary conservation concern, as these impacts feed through to changes in biodiversity and ecosystem functions. Here, we test whether high-spatial resolution satellite remote sensing can be used to map the responses of aboveground live tree biomass (AGB), canopy leaf area index (LAI) and fractional vegetation cover (FCover) to selective logging and deforestation in Malaysian Borneo. We measured these attributes in permanent vegetation plots in rainforest and oil palm plantations across the degradation landscape of the Stability of Altered Forest Ecosystems project. We found significant mathematical relationships between field-measured structure and satellite-derived spectral and texture information, explaining up to 62% of variation in biophysical structure across forest and oil palm plots. These relationships held at different aggregation levels from plots to forest disturbance types and oil palms allowing us to map aboveground biomass and canopy structure across the degradation landscape. The maps reveal considerable spatial variation in the impacts of previous logging, a pattern that was less clear when considering field data alone. Up-scaled maps revealed a pronounced decline in aboveground live tree biomass with increasing disturbance, impacts which are also clearly visible in the field data even a decade after logging. Field data demonstrate a rapid recovery in forest canopy structure with the canopy recovering to pre-disturbance levels a decade after logging. Yet, up-scaled maps show that both LAI and FCover are still reduced in logged compared to primary forest stands and markedly lower in oil palm stands. While uncertainties remain, these maps can now be utilised to identify conservation win–wins, especially when combining them with ongoing biodiversity surveys and measurements of carbon sequestration, hydrological cycles and microclimate