Convolutional Neural Network on Three Orthogonal Planes for Dynamic Texture Classification

Dynamic Textures (DTs) are sequences of images of moving scenes that exhibit certain stationarity properties in time such as smoke, vegetation and fire. The analysis of DT is important for recognition, segmentation, synthesis or retrieval for a range of applications including surveillance, medical imaging and remote sensing. Deep learning methods have shown impressive results and are now the new state of the art for a wide range of computer vision tasks including image and video recognition and segmentation. In particular, Convolutional Neural Networks (CNNs) have recently proven to be well suited for texture analysis with a design similar to a filter bank approach. In this paper, we develop a new approach to DT analysis based on a CNN method applied on three orthogonal planes x y , xt and y t . We train CNNs on spatial frames and temporal slices extracted from the DT sequences and combine their outputs to obtain a competitive DT classifier. Our results on a wide range of commonly used DT classification benchmark datasets prove the robustness of our approach. Significant improvement of the state of the art is shown on the larger datasets.Comment: 19 pages, 10 figure

Modelling the interpretation of digital mammography using high order statistics and deep machine learning

Visual search is an inhomogeneous, yet efficient sampling process accomplished by the saccades and the central (foveal) vision. Areas that attract the central vision have been studied for errors in interpretation of medical images. In this study, we extend existing visual search studies to understand features of areas that receive direct visual attention and elicit a mark by the radiologist (True and False Positive decisions) from those that elicit a mark but were captured by the peripheral vision. We also investigate if there are any differences between these areas and those that are never fixated by radiologists. Extending these investigations, we further explore the possibility of modelling radiologists’ search behavior and their interpretation of mammograms using deep machine learning techniques. We demonstrated that energy profiles of foveated (FC), peripherally fixated (PC), and never fixated (NFC) areas are distinct. It was shown that FCs are selected on the basis of being most informative. Never fixated regions were found to be least informative. Evidences that energy profiles and dwell time of these areas influence radiologists’ decisions (and confidence in such decisions) were also shown. High-order features provided additional information to the radiologists, however their effect on decision (and confidence in such decision) was not significant. We also showed that deep-convolution neural network can successfully be used to model radiologists’ attentional level, decisions and confidence in their decisions. High accuracy and high agreement (between true and predicted values) in such predictions can be achieved in modelling attentional level (accuracy: 0.90, kappa: 0.82) and decisions (accuracy: 0.92, kappa: 0.86) of radiologists. Our results indicated that an ensembled model for radiologist’s search behavior and decision can successfully be built. Convolution networks failed to model missed cancers however

3D multiresolution statistical approaches for accelerated medical image and volume segmentation

This thesis was submitted for the degree of Doctor of Philosophy and awarded by Brunel University.Medical volume segmentation got the attraction of many researchers; therefore, many techniques have been implemented in terms of medical imaging including segmentations and other imaging processes. This research focuses on an implementation of segmentation system which uses several techniques together or on their own to segment medical volumes, the system takes a stack of 2D slices or a full 3D volumes acquired from medical scanners as a data input. Two main approaches have been implemented in this research for segmenting medical volume which are multi-resolution analysis and statistical modeling. Multi-resolution analysis has been mainly employed in this research for extracting the features. Higher dimensions of discontinuity (line or curve singularity) have been extracted in medical images using a modified multi-resolution analysis transforms such as ridgelet and curvelet transforms. The second implemented approach in this thesis is the use of statistical modeling in medical image segmentation; Hidden Markov models have been enhanced here to segment medical slices automatically, accurately, reliably and with lossless results. But the problem with using Markov models here is the computational time which is too long. This has been addressed by using feature reduction techniques which has also been implemented in this thesis. Some feature reduction and dimensionality reduction techniques have been used to accelerate the slowest block in the proposed system. This includes Principle Components Analysis, Gaussian Pyramids and other methods. The feature reduction techniques have been employed efficiently with the 3D volume segmentation techniques such as 3D wavelet and 3D Hidden Markov models. The system has been tested and validated using several procedures starting at a comparison with the predefined results, crossing the specialists’ validations, and ending by validating the system using a survey filled by the end users explaining the techniques and the results. This concludes that Markovian models segmentation results has overcome all other techniques in most patients’ cases. Curvelet transform has been also proved promising segmentation results; the end users rate it better than Markovian models due to the long time required with Hidden Markov models

The physical characterisation and composition of archaeological dental calculus

Dental calculus is a complex biological material that has been found to provide significant evidence of past population diet, health and habitual activity. It is composed of mineral phases, trace elements, organic species and can have inclusions such as starch granules and microfossils incorporated into its structure. This composition has been found to vary among individuals, although the reasons for this are poorly understood. Despite this, there is a wealth of knowledge that can be gained from analysing this biomineral, especially from archaeological remains. In past populations, the variables that affect composition, such as pharmaceuticals and diet are reduced compared to modern populations. As such the reliance on clinical studies that have investigated dental calculus from modern individuals, may be flawed when considering past populations. The focus of this study was to provide insight about the variation in physical characterisation and composition of archaeological dental calculus. Despite there being an abundance of archaeological dental calculus research, this is the first large scale compositional study of specimens from three separate past populations. In addition, this research is the first study to adopt a non-destructive to destructive approach to archaeological dental calculus analysis. As well, it is the first application of nanocomputed tomography to dental calculus from past populations. Consequently, this study demonstrates the first evidence of accumulation layering that has been detected using non- estructive nano-computed tomography. Furthermore, this research has identified three types of layering in archaeological dental calculus. Due to these findings, it is expected that this research will impact the future of dental calculus analysis, especially when considering dental calculus as a method of mapping an individual’s health, diet or lifestyle in the weeks or months prior to death. The overall results of this thesis demonstrate that some aspects of the morphological, mineralogical and elemental analysis of archaeological dental calculus are inconsistent with clinical literature. The results have also shown that there are some differences between the dental calculus from different archaeological populations which can be related to post-mortem burial conditions

3D multiresolution statistical approaches for accelerated medical image and volume segmentation

Medical volume segmentation got the attraction of many researchers; therefore, many techniques have been implemented in terms of medical imaging including segmentations and other imaging processes. This research focuses on an implementation of segmentation system which uses several techniques together or on their own to segment medical volumes, the system takes a stack of 2D slices or a full 3D volumes acquired from medical scanners as a data input. Two main approaches have been implemented in this research for segmenting medical volume which are multi-resolution analysis and statistical modeling. Multi-resolution analysis has been mainly employed in this research for extracting the features. Higher dimensions of discontinuity (line or curve singularity) have been extracted in medical images using a modified multi-resolution analysis transforms such as ridgelet and curvelet transforms. The second implemented approach in this thesis is the use of statistical modeling in medical image segmentation; Hidden Markov models have been enhanced here to segment medical slices automatically, accurately, reliably and with lossless results. But the problem with using Markov models here is the computational time which is too long. This has been addressed by using feature reduction techniques which has also been implemented in this thesis. Some feature reduction and dimensionality reduction techniques have been used to accelerate the slowest block in the proposed system. This includes Principle Components Analysis, Gaussian Pyramids and other methods. The feature reduction techniques have been employed efficiently with the 3D volume segmentation techniques such as 3D wavelet and 3D Hidden Markov models. The system has been tested and validated using several procedures starting at a comparison with the predefined results, crossing the specialists’ validations, and ending by validating the system using a survey filled by the end users explaining the techniques and the results. This concludes that Markovian models segmentation results has overcome all other techniques in most patients’ cases. Curvelet transform has been also proved promising segmentation results; the end users rate it better than Markovian models due to the long time required with Hidden Markov models.EThOS - Electronic Theses Online ServiceGBUnited Kingdo

Multimodal imaging in age-related macular degeneration

Age-related macular degeneration (AMD) is a leading cause of blindness and affects approximately one in seven Australians aged 50 years and above. Currently, this complex condition is not easily and uniformly assessed. The signs of AMD differ between eyes and also occur in other macular disorders. This hinders accurate diagnosis and classification, which is fundamental to optimal patient care. Ocular imaging and visual function assessment have the potential to minimise the devastating consequences of disease through early detection. However, multiple devices are now commercially available and the impact of these technologies in clinical practice may not be straightforward. For instance, their usefulness may depend on accessibility and the operator’s knowledge and clinical skills. The impact on patient management, as well as alternative models of eye-care delivery, requires clarification. This thesis aims to explore the current and potential utility of imaging technologies (optical coherence tomography, infrared imaging, monochromatic retinal photography and fundus autofluorescence) in the assessment and management of AMD and other diseases of retinal pigment epithelium dysfunction. The findings show that optometrists self-describe high levels of practice competency and make ready use of imaging in everyday practice. However, they also unwittingly demonstrated low awareness of the evidence base in AMD. Furthermore, when their interpretation of images was tested using a series of case vignettes, their diagnostic accuracy as a group improved by only five per cent (from 61 per cent to 66 per cent); their tendency to refer increased by four per cent. These factors might be improved through education. A series of open-access, chair-side reference charts were consequently devised to help optometrists use imaging technologies more effectively in clinical practice. The additive contribution of multimodal structural and functional testing was particularly emphasised. Finally, a novel model of intermediate-tier eye-care in Australia was shown to substantially reduce the number of false positive cases or cases without a specific diagnosis. Interestingly, this model was acclaimed by reviewers as “scoring highly for originality and of international relevance”. Most excitingly, the thesis concludes with future directions regarding collaborative care and multimodal imaging, where detection of disease might be facilitated via a computational approach

Bidirectional Propulsion of Devices Along the Gastrointestinal Tract Using Electrostimulation

This thesis describes a method for propelling devices such as video capsule endoscopes in either direction along the small intestines using electrostimulation-induced muscular contractions. When swallowed, passive diagnostic ‘one-shot’ devices rely on sporadic peristaltic movement, possibly missing vital ‘areas of interest’. This bidirectional propulsion method provides active control for that all-important ‘second look’. Design considerations, within the dimensional constraints, required a device shape that would achieve maximum propulsion from safely induced useful contractions produced by the electrodes and encapsulated miniature electrostimulator. Construction materials would have to produce minimal friction against the mucosal surface while having the physical properties to facilitate construction and electrode attachment. Design investigations included coefficient of friction measurements of different construction materials and the evaluation of different capsule and electrode dimensions over a range of stimulation parameters, to obtain optimal propulsion. A swallowable 11 mm diameter device was propelled at 121 mm/min with stimulation parameters of 12.5 Hz, 20 ms, at 20 V in an anaesthetised pig. A modified passive video capsule endoscope was propelled at 120 mm/min with stimulation parameters of 12.5 Hz, 20 ms, at 10 V in an unanaesthetised human volunteer. A radio-controlled capsule incorporating an electrostimulator, voltage converter and 3 V power supply was propelled at 60 mm/min with stimulation parameters of 12.5 Hz, 20 ms, and 30 V in an anaesthetised pig. 4 Other possible uses of electrostimulation were investigated including propulsion of anally administered large intestine devices and introduction of the intestinal mucosal surface into a biopsy chamber. Results are presented. The ultimate aim of the project was to provide bidirectional propulsion for wireless remote controlled devices along the gastrointestinal tract utilising contractile force produced by electrostimulation of the intestinal wall. The controllability of this system could provide clinicians with a real time view of the entire small intestines without surgical enteroscopy