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Automated Computational Techniques for High-throughput Image Analysis of Skin Structure
Get PDFBiological image processing and analysis are concerned with enhancing and quantifying features that reflect different pathological states, based on the use of combinations of image processing algorithms. The integration of image processing and analysis techniques to evaluate and assess skin integrity in both human and mouse models is a major theme in this thesis. More specifically, this thesis describes computational systems for high-throughput analysis of skin tissue section images and non-invasive imaging techniques. As the skin is a largest organ in the mammalian body, and is complex in structure, manual quantification and analysis a hard task for the observer to determine an objective result, and furthermore, the analysis is complex in terms of accuracy and time taken.
To look at the gross morphology of the skin, I developed high throughput analysis based on an adaptive active contour model to isolate the skin layers and provide quantification methods. This was utilised in a study to evaluate cutaneous morphology in 475 knockout mouse lines provided by the Mouse Genetics Project (MGP) pipeline, that was generated by the Wellcome Trust Sanger Institute (WTSI). This is a major international initiative to provide both functional annotation of the mammalian genome and insight into the genetic basis of disease. I found 53 interesting adipocyte phenotypes, 18 interesting dermal phenotypes and 3 interesting epidermal phenotypes.
I also focussed on the analysis of collagen in the dermis of skin images in several ways. For collagen structure analysis, I developed a combined system of Gabor filtering and Fast Fourier Transform FFT. This analysis allowed the detection of subtle changes in collagen organisation. Using similar images, I also measured collagen bundle thickness by computing the maximum frequency of the FFT power spectrum. To assess collagen dynamics, I developed k-means clustering for segmentation based on colour distribution. The use of this approach allowed the measurement of dermal degradation with age and disease, which was not possible by existing means.
Obtaining human skin material to facilitate the drug discovery and development process is not an easy task. The manipulation, monitoring and cost of human subjects makes the use of mouse models more suitable for high-throughput screening. Therefore, I have evaluated skin integrity from mouse tissue rather than human skin, however, mouse skin is thinner than human skin and many morphological features are easier to visualise in human skin, which has implications for analysis.
Skin moulds can be used to create an impression of the skin surface. Changes in texture of skin can reflect skin conditions. I developed a skin surface structure analysis system to measure the degree of change in texture of the human skin surface. The alterations detected in texture parameters in skin mould impressions reflected changes caused by sun exposure, ageing and many other clinical parameters. I compared my analysis with the existing Beagley-Gibson scoring system to find correlations between automated and manual analysis to inform a decision on the use of optimal methods. By removing subjectivity of manual methods, I was able to develop a robust system to evaluate, for example, damage resulting from UV exposure.
My experimental analysis indicated that techniques developed in this thesis were able to analyse both histological samples and skin surface images in high-throughput experiments. They could, therefore, make a contribution to biological image analysis by providing accurate results to help clinical decision making, and facilitate biological laboratory experiments to improve the quality of research in this field, and save time.
Overall, my thesis demonstrated that accurate analysis of the skin to gain meaningful biological information requires an automated system that can achieve feature extraction, quantification, analysis and decision making to find interesting phenotypes and abnormalities. This will help the evaluation of the effects of a specific treatment, and answer many biological questions in fields of cosmetic dermatology and drug discovery, and improve our understanding of the genetic basis of disease
The Rise of Far Right Extremism in Contemporary Great Britain and continental Europe
No full textA number of Far Right and “radical right” parties have recently been scoring electoral successes across many regions. When comparing the trend with political conditions in the United Kingdom, however, the picture is a complex and mixed one. British Far Right politics have been characterised by militant organisations who have mostly mobilised themselves through street demonstrations and violence, rather than organised political activity. Indeed, the normative theory is that the British Far Right has never been able to organise itself as effectively as many of its European counterparts. During the early part of the twenty-first century, the British National Party (BNP) appeared to be threatening to break this mould, especially at the local level. However, since 2009, the BNP has collapsed once more into obscurity. The picture is slightly complicated by the parallel rise of a radical right “flash party”, the UK Independence Party (UKIP), which is causing a great deal of anxiety for the mainstream parties. However, the final analysis suggests that Britain is still fundamentally unsuited to successful Far Right and radical right parties in the longer term
Brides and martyrs: protecting children from violent extremism
Get PDFThe media has been dominated with news stories about people travelling to Syria to demonstrate their support for ISIS or the Al-Nusra Front. This problem is not entirely novel, as nearly 70 years ago volunteers left Britain and Ireland to fight in the Spanish Civil War. But the relative ease of international travel, the reach of social media, the tactics and targets used by extremists, the ubiquity of terrorism across the Middle East and North Africa and the fact that the UK has already experienced domestic terrorism inspired by international examples had made matters worse.
The government believes that the radicalisation of people in the UK presents a potential significant threat to national security and has the capacity to ruin lives. The security agencies are exercised by the possibility of people travelling to Syria and other countries and returning to engage in terrorism.
Recent cases before the Family Division have demonstrated that the courts are prepared to act where young people are at risk of radicalisation
Privacy in Biometric Systems
Get PDFBiometrics are physiological and/or behavioral characteristics of a person that have been used to provide an automatic proof of identity in a growing list of applications including crime/terrorism fighting, forensics, access and border control, securing e-/m-commerce transactions and service entitlements. In recent years, a great deal of research into a variety of new and traditional biometrics has widened the scope of investigations beyond improving accuracy into mechanisms that deal with serious concerns raised about the potential misuse of collected biometric data. Despite the long list of biometrics’ benefits, privacy concerns have become widely shared due to the fact that every time the biometric of a person is checked, a trace is left that could reveal personal and confidential information. In fact, biometric-based recognition has an inherent privacy problem as it relies on capturing, analyzing, and storing personal data about us as individuals. For example, biometric systems deal with data related to the way we look (face, iris), the way we walk (gait), the way we talk (speaker recognition), the way we write (handwriting), the way we type on a keyboard (keystroke), the way we read (eye movement), and many more. Privacy has become a serious concern for the public as biometric systems are increasingly deployed in many applications ranging from accessing our account on a Smartphone or computer to border control and national biometric cards on a very large scale. For example, the Unique Identification Authority of India (UIDAI) has issued 56 million biometric cards as of January 2014 [1], where each biometric card holds templates of the 10 fingers, the two irises and the face. An essential factor behind the growing popularity of biometrics in recent years is the fact that biometric sensors have become a lot cheaper as well as easier to install and handle. CCTV cameras are installed nearly everywhere and almost all Smartphones are equipped with a camera, microphone, fingerprint scanner, and probably very soon, an iris scanner
Human gait recognition under neutral and non-neutral gait sequences
No full textRapid advances in biometrics technology makes their use for person‘s identity more acceptable in a variety of applications, especially in the areas of the interest in security and surveillance. The upsurge in terrorist attacks in the past few years has focused research on biometric systems that have the ability to identify individuals from a distance, and this is spearheading research interest in Gait biometric due to being unobtrusive and less dependent on high image/video quality. Gait biometric is a behavioral trait that aims to identify individuals from image sequences based on their walking style. The growing list of possible civil as well as security applications for various purposes is paralleled by the emergence of a variety of research challenges in dealing with a various external as well as internal factors influencing the performance of Gait Recognition (GR) in unconstrained recording conditions.
This thesis is concerned with Gait Recognition in unconstrained scenarios aims to address research questions covering (1) The selection of sets of features for a gait signature; (2) The effects of gender and/or recoding condition case (neutral, carrying a bag, coat wearing) on the performance of GR schemes; (3) Integrating gender and/or case classifications into GR; and (4) The role of emerging Kinect sensor technology, with its capability of sensing human skeletal features in GR and applications. Accordingly, our objectives will focus on investigating, developing and testing the performance of using a variety of gait sequencefeatures for the various components/tasks and their integration. Our tests are based on large number of experiments based on CASIA B database as well as an in-house database of Kinect sensor recording. In all experiments, we use different dimension reduction and feature selection methods do reduce the dimensions in these proposed feature vectors, such as Principal Component Analysis (PCA), Linear Discriminant Analysis (LDA) and Fisher Score, followed by different classification methods like; k-nearest-neighbour (k-NN), Support Vector Machine (SVM), Naive Bayes and linear discriminant classifier (LDC), to test the performance of the proposed methods.
The initial part is focused on reviewing existing background removal for indoor and outdoor scenarios and developing more efficient versions primarily by adopting the work for wavelet domain rather than the traditional spatial domain based schemes. These include motion detection by frame differencing and Mixture of Gaussians, the latter being more reliable for outdoor scenarios. Subsequently, we investigated a variety of features that can be extractedfrom various subbands of wavelet-decomposed frames of different body parts (partitioned according to the golden ratio). We gradually built sets of features, together with their fused combinations, that can categorized as hybrid of model-based and motion-based models. The first list of features developed to deal with Neutral Gait Recognition (NGR) includes: Spatio-Temporal Model (STM), Legs Motion Detection Feature (LMD), and the Statistical model of the approximation LL-wavelet subband images (AWM). We shall demonstrate that fusing these features achieves accuracy of 97%, which is comparable to the state of the art. These features will be shown to achieve 96% accuracy in gender classification (GC), and we shall establish that the NGR2 scheme that integrates GC into NGR improves the accuracy by a noticeable percentage.
Testing the performance of these NGR schemes in recognising non-neutral cases revealed the challenges of Unrestricted Gait Recognition (UGR). The second part of the thesis is focused on developing UGR schemes.
For this, first a new statistical wavelet feature set extracted from high frequency subbands, called Detail coefficients Wavelet Model (DWM) was added to the previous list. Using different combinations of these schemes, will be shown to significantly improve the performance for non-neutral gait cases, but to less extent in the coat wearing case. We then develop a Gait Sequence Case Detection (GSCD) which has excellent performance. We will show that integrating GSCD and GC together into UGR improves the performance for all cases. We shall also investigate the different UGS scheme that generalizes existing work on Gait Energy and Gait Entropy images (GEI and GEnI) features but in the wavelet domain and in different body parts. Testing these two schemes, and their fusion, post the PCA dimension reduction yield much improved accuracy for the non-neutral cases compared to existing scheme GEI and GEnI schemes, but are significantly outperformed by the last scheme. However, by fusing the UGS scheme with the GSCD+GC+UGR scheme above we will get best accuracy that outperform the state of the art in GR specially in the non-neutral cases.
The thesis ended by conducting a rather limited investigation on the use of the Kinect sensors for GR. We develop two sets of features: Horizontal Distance Features and Vertical Distance Features from small set of skeleton point trajectories. The experimental result on neutral was very successful but for the unrestricted gait recognition (with the 5 case variations) satisfactory but not optimal performance relies on the gallery including balanced number of samples from all cases
Resistance in Organisational Change
No full textPurpose: This paper investigates the contributing factors that lead to resistance to change, and ascertains the relationship between organizational culture and employee resistance in organizational change programmes, such as Lean.
Design/methodology/approach: This methodology for this research is in three main parts. Firstly, a systematic review of the literature pertaining to resistance to change followed by a case study, involving an anonymous survey and semi-structured interviews to test the assumptions drawn from the literature. Finally the literature research and case study results are drawn together to present a new model of resistance.
Findings: The finding of the literature, along with the finding of the case study confirm that a lack of communication and participant involvement during change are highlighted as significant contributing factors to resistance and that these are related to organizational culture.
Research limitations/implications: Whilst the secondary sources of information provide a significant weight of evidence to support the results from the case study, the results of the research are based on a single case study; therefore caution should be applied before making generalizations from the data.
Practical implications: The findings can provide organizations, and change practitioners, with an insight into a number of the issues that should be considered in relation to an organizations culture before attempting large-scale change programmes.
Originality/value: The research findings provide a new model, the ‘Resistance Model’ that identifies the interconnected issues that affect employees’ attitude to, and therefore acceptance of, organizational change.
Keywords: Organisational change, Change, Resistance to Change, Lean, Resistance Model
Automated characterisation of ultrasound images of ovarian tumours: the diagnostic accuracy of a support vector machine and image processing with a local binary pattern operator
Get PDFIntroduction: Preoperative characterisation of ovarian masses into benign or malignant is of paramount importance to optimise patient management.
Objectives: In this study, we developed and validated a computerised model to characterise ovarian masses as
benign or malignant.
Materials and methods: Transvaginal 2D B mode static ultrasound images of 187 ovarian masses with known
histological diagnosis were included. Images were first pre-processed and enhanced, and Local Binary Pattern
Histograms were then extracted from 2 × 2 blocks of each image. A Support Vector Machine (SVM) was trained
using stratified cross validation with randomised sampling. The process was repeated 15 times and in each round 100 images were randomly selected.
Results: The SVM classified the original non-treated static images as benign or malignant masses with an average accuracy of 0.62 (95% CI: 0.59-0.65). This performance significantly improved to an average accuracy of 0.77 (95% CI: 0.75-0.79) when images were pre-processed, enhanced and treated with a Local Binary Pattern operator (mean difference 0.15: 95% 0.11-0.19, p < 0.0001, two-tailed t test).
Conclusion: We have shown that an SVM can classify static 2D B mode ultrasound images of ovarian masses into
benign and malignant categories. The accuracy improves if texture related LBP features extracted from the images
are considered
ELECTION PETITIONS AND THE STANDARD OF PROOF
Get PDFThis ARTICLE explores the issue of the standard of proof in relation to presidential election petitions and reviews two recent decisions by the apex courts in Kenya and Ghana. These are of particular interest given the
very different approach of the UK courts to election petitions and epitomised in the case of Erlam and Others v Rahman and Others
Effects of age on smartphone and tablet usability, based on eye-movement tracking and touch-gesture interactions
No full textThe aim of this thesis is to provide an insight into the effects of user age on interactions with smartphones and tablets applications. The study considered two interaction methods to investigate the effects of user age on the usability of smartphones and tablets of different sizes: 1) eye-movements/browsing and 2) touch-gesture interactions. In eye movement studies, an eye tracker was used to trace and record users’ eye movements which were later analysed to understand the effects of age and screen-size on browsing effectiveness. Whilst in gesture interactions, an application developed for smartphones traced and recorded users’ touch-gestures data, which were later analysed to investigate the effects of age and screensize on touch-gesture performance. The motivation to conduct our studies is summarised as follows: 1) increasing number of elderly people in our society, 2) widespread use of smartphone technology across the world, 3) understanding difficulties for elderly when interacting smartphones technology, and 4) provide the existing body of literature with new understanding on the effects of ageing on smartphone usability.
The work of this thesis includes five research projects conducted in two stages. Stage One included two researches used eye movement analysis to investigate the effects of user age and the influence of screen size on browsing smartphone interfaces. The first research examined the scan-paths dissimilarity of browsing smartphones applications or elderly users (60+) and younger users (20-39). The results revealed that the scan-paths dissimilarity in browsing smartphone applications was higher for elderly users (i.e., age-driven) than the younger users. The results also revealed that browsing smartphone applications were stimulus-driven rather than screen size-driven. The second study was conducted to understand the difficulties of information processing when browsing smartphone applications for elderly (60+), middle-age (40-59) and younger (20-39) users. The evaluation was performed using three different screen sizes of smartphone and tablet devices. The results revealed that processing of both local and global information on a smartphone/tablet interfaces was more difficult for elderly users than it was for the other age groups. Across all age groups, browsing on the smaller smartphone size proved to be more difficult compared to the larger screen sizes.
Stage Two included three researches to investigate: the difficulties in interacting with gesture-based applications for elderly compared to younger users; and to evaluate the possibility of classifying user’s age-group based on on-screen gestures. The first research investigated the effects of user age and screen size on performing gesture swiping intuitively for four swiping directions: down, left, right, and up. The results revealed that the performance of gesture swiping was influenced by user age, screen size, as well as by the swiping orientation. The purpose of the second research was to investigate the effects of user age, screen sizes, and gesture complexity in performing accurate gestures on smartphones and tablets using gesture-based features. The results revealed that the elderly were less accurate, less efficient, slower, and exerted more pressure on the touch-screen when performing gestures than the younger users. On a small smartphone, all users were less accurate in gesture performance – more so for elderly – compared to mini-sized tablets. Also, the users, especially the elderly, were less efficient and less accurate when performing complex gestures on the small smartphone compared to the mini-tablet. The third research investigated the possibility of classifying a user’s age-group using touch gesture-based features (i.e., gesture speed, gesture accuracy, movement time, and finger pressure) on smartphones. In the third research, we provide evidence for the possibility of classifying a user’s age-group using gesture-based applications on smartphones for user-dependent and user-independent scenarios. The accuracy of age-group classification on smaller screens was higher than that on devices with larger screens due to larger screens being much easier to use for all users across both age groups. In addition, it was found that the age-group classification accuracy was higher for younger users than elderly users. This was due to the fact that some elderly users performed the gestures in the same way as the younger users do, which could be due to their longer experience in using smartphones than the typical elderly user.
Overall, our results provided evidence that elderly users encounter difficulties when interacting with smartphones and tablet devices compared to younger users. Also, it was possible to classify user’s age-group based on users’ ability to perform touch-gestures on smartphones and tablets. The designers of smartphone interfaces should remove barriers that make browsing and processing local and global information on smartphones’ applications difficult. Furthermore, larger screen sizes should be considered for elderly users. Also, smartphones could include automatically customisable user interfaces to suite
elderly users' abilities to accommodate their needs so that they can be equally efficient as younger users.
The outcomes of this research could enhance the design of smartphones and tablets as well the applications that run on such devices, especially those that are aimed at elderly users. Such devices and applications could play an effective role in enhancing elderly peoples’ activities of daily lives
Post-operative pain management through music-induced analgesia: Investigating musical constructs
Get PDFDistraction and attention-diversion approaches are widely integrated into pain management. Music-induced analgesia, the ability of music to reduce pain perception, is a clinically-relevant approach for managing pain, anxiety and psychological well-being. Research categorises audio-analgesic interventions as homogenous, however enquiry is required to identify which musical constructs may be therapeutically effective. This study investigated the impact of harmony and rhythm on acute, post-operative
pain in a sample of 98 patients scheduled for knee surgery. Four music listening groups were compared against controls using silent relaxation. After surgery using standardised anaesthesia, participants undertook a 15-minute intervention per day of in-patient stay. Measures of pain intensity, pain interference, salivary cortisol concentration and mood were obtained. All participants showed reductions in pain from pre- to post-test, indicating
silent relaxation was as effective as music listening. Salivary cortisol concentrations showed that music with high harmonicity/rhythmicity reduced cortisol concentration to a greater extent on Day 1 than music with low harmonicity/rhythmicity. These findings validate the homogenous use of auditory distraction for audio-analgesia, and importantly emphasise the core role of compositional musical constructs in maximising early postoperative
recovery. Results support the need for additional psychobiological research examining the efficacy of audio-analgesic attention-diversion interventions used in pain management