Finger Vein Recognition Using Principle Component Analysis and Adaptive k-Nearest Centroid Neighbor Classifier

The k-nearest centroid neighbor kNCN classifier is one of the non-parametric classifiers which provide a powerful decision based on the geometrical surrounding neighborhood. Essentially, the main challenge in the kNCN is due to slow classification time that utilizing all training samples to find each nearest centroid neighbor. In this work, an adaptive k-nearest centroid neighbor (akNCN) is proposed as an improvement to the kNCN classifier. Two new rules are introduced to adaptively select the neighborhood size of the test sample. The neighborhood size for the test sample is changed through the following ways: 1) The neighborhood size, k will be adapted to j if the centroid distance of j-th nearest centroid neighbor is greater than the predefined boundary. 2) There is no need to look for further nearest centroid neighbors if the maximum number of samples of the same class is found among jth nearest centroid neighbor. Thus, the size of neighborhood is adaptively changed to j. Experimental results on theFinger Vein USM (FV-USM) image database demonstrate the promising results in which the classification time of the akNCN classifier is significantly reduced to 51.56% in comparison to the closest competitors, kNCN and limited-kNCN. It also outperforms its competitors by achieving the best reduction ratio of 12.92% whilemaintaining the classification accuracy

An Extensive Review on Spectral Imaging in Biometric Systems: Challenges and Advancements

Spectral imaging has recently gained traction for face recognition in biometric systems. We investigate the merits of spectral imaging for face recognition and the current challenges that hamper the widespread deployment of spectral sensors for face recognition. The reliability of conventional face recognition systems operating in the visible range is compromised by illumination changes, pose variations and spoof attacks. Recent works have reaped the benefits of spectral imaging to counter these limitations in surveillance activities (defence, airport security checks, etc.). However, the implementation of this technology for biometrics, is still in its infancy due to multiple reasons. We present an overview of the existing work in the domain of spectral imaging for face recognition, different types of modalities and their assessment, availability of public databases for sake of reproducible research as well as evaluation of algorithms, and recent advancements in the field, such as, the use of deep learning-based methods for recognizing faces from spectral images

Handbook of Vascular Biometrics

This open access handbook provides the first comprehensive overview of biometrics exploiting the shape of human blood vessels for biometric recognition, i.e. vascular biometrics, including finger vein recognition, hand/palm vein recognition, retina recognition, and sclera recognition. After an introductory chapter summarizing the state of the art in and availability of commercial systems and open datasets/open source software, individual chapters focus on specific aspects of one of the biometric modalities, including questions of usability, security, and privacy. The book features contributions from both academia and major industrial manufacturers

BEst (Biomarker Estimation): Health Biomarker Estimation Non-invasively and Ubiquitously

This dissertation focuses on the non-invasive assessment of blood-hemoglobin levels. The primary goal of this research is to investigate a reliable, affordable, and user-friendly point-of-care solution for hemoglobin-level determination using fingertip videos captured by a smartphone. I evaluated videos obtained from five patient groups, three from the United States and two from Bangladesh, under two sets of lighting conditions. In the last group, based on human tissue optical transmission modeling data, I used near-infrared light-emitting diode sources of three wavelengths. I developed novel image processing techniques for fingertip video analysis to estimate hemoglobin levels. I studied video images creating image histogram and subdividing each image into multiple blocks. I determined the region of interest in a video and created photoplethysmogram signals. I created features from image histograms and PPG signals. I used the Partial Least Squares Regression and Support Vector Machine Regression tools to analyze input features and to build hemoglobin prediction models. Using data from the last and largest group of patients studied, I was able to develop a model with a strong linear correlation between estimated and clinically-measured hemoglobin levels. With further data and methodological refinements, the approach I have developed may be able to define a clinically accurate public health applicable tool for hemoglobin level and other blood constituent assessment

Developing Zebrafish embryos as a model to study host-material Interactions and wound healing

Inappropriate wound healing represents a considerable medical challenge associated with high mortality. However, improving on current wound healing therapies has proven difficult due to the complex and dynamic wound environment. The complexity of the wound healing process also puts high demands on the animal models used in wound research, since ideally such models should encompass the full complexity of the wound healing process, and at the same time be accessible for advanced biomedical analysis methods. In this thesis, the aim was to further develop the use of zebrafish embryos in wound healing research. Key advantages of zebrafish embryo models are the ability to visualize complex biological processes in high detail in intact tissues, as well as highly tractable genetics. The first part of the work describes the development of a zebrafish embryo model for investigating the immunomodulatory properties of hydrogels derived from decellularized extracellular matrix (ECM). The results demonstrate that the hydrogels can be properly injected into the embryos and that the host-materials interactions can be explored in detail inside live zebrafish embryos during wound healing. This constitutes a new in vivo model for investigating immunomodulatory materials in a realistic wound healing context. The second part of the work describes the development of a confocal Raman spectrometry imaging (cRSI) method for biomolecular characterization and the study of biological processes in zebrafish. This represents a new imaging modality that enables simultaneous inspection of a multitude of biomolecules in a label-free manner. The use of cRSI was demonstrated for biomolecular discrimination of mycobacteria in a zebrafish infection model, and for live in vivo imaging of zebrafish during the early wound response. Taken together, the work in this thesis has provided a new methodologies and insight for the use in zebrafish embryo models in wound healing research.Open Acces

OPTICAL NAVIGATION TECHNIQUES FOR MINIMALLY INVASIVE ROBOTIC SURGERIES

Minimally invasive surgery (MIS) involves small incisions in a patient's body, leading to reduced medical risk and shorter hospital stays compared to open surgeries. For these reasons, MIS has experienced increased demand across different types of surgery. MIS sometimes utilizes robotic instruments to complement human surgical manipulation to achieve higher precision than can be obtained with traditional surgeries. Modern surgical robots perform within a master-slave paradigm, in which a robotic slave replicates the control gestures emanating from a master tool manipulated by a human surgeon. Presently, certain human errors due to hand tremors or unintended acts are moderately compensated at the tool manipulation console. However, errors due to robotic vision and display to the surgeon are not equivalently addressed. Current vision capabilities within the master-slave robotic paradigm are supported by perceptual vision through a limited binocular view, which considerably impacts the hand-eye coordination of the surgeon and provides no quantitative geometric localization for robot targeting. These limitations lead to unexpected surgical outcomes, and longer operating times compared to open surgery. To improve vision capabilities within an endoscopic setting, we designed and built several image guided robotic systems, which obtained sub-millimeter accuracy. With this improved accuracy, we developed a corresponding surgical planning method for robotic automation. As a demonstration, we prototyped an autonomous electro-surgical robot that employed quantitative 3D structural reconstruction with near infrared registering and tissue classification methods to localize optimal targeting and suturing points for minimally invasive surgery. Results from validation of the cooperative control and registration between the vision system in a series of in vivo and in vitro experiments are presented and the potential enhancement to autonomous robotic minimally invasive surgery by utilizing our technique will be discussed

Dynamic hypoxic pre-conditioning of cells seeded in tissue-engineered scaffold to improve neovascularisation

Introduction: Tissue engineering (TE) is the potential solution to the global shortage of tissue and organs. However, the lack of adequate angiogenesis to TE scaffolds during the initial stages of implantation has hindered its success in vivo. Mesenchymal stem cells (MSC) have the most established track record for translational regenerative therapy and have been widely used in combination with TE scaffolds. Hypoxia is one of the main potentiators for upregulating angiogenic factors in MSC. However, fine-tuning their cellular function and behaviour is still not fully understood. This study aims to help increase the understanding of this process by determining the effects of in vitro hypoxic conditioning on enhancement of angiogenesis of MSC for the purpose of pre-clinical translational for TE application. Methods: The angiogenic potential of 3 different tissue sources (bone marrow, umbilical cord and adipose) MSC were initially determined for downstream pre-clinical application. We established the appropriate regime for in vitro dynamic hypoxia conditions in 2D and 3D hydrogel to enhance MSC angiogenic pathway using real-time continuous oxygen sensors and angiogenic cytokine profiling. Cell metabolism and proliferation effects were also evaluated using intravital Realtime-glo, D-luciferin (on transduced MSC) and microscopic Live-Dead stain techniques. We optimised seeding of cells on the tissue engineered dermal (INTEGRA®) for in vivo translational purpose and used targeted in vitro and ex vivo angiogenesis assays, which helped to determine aspects of the MSC conditioned media on endothelial migration, proliferation, morphogenesis and matrix degradation. Finally, the functional reproducibility of the in vitro angiogenic response was assessed using in vivo angiogenesis CAM assay and murine diabetic wound healing models. Results: Adipose derived MSC (adMSC) were found to have the most angiogenic potential in response to hypoxic conditioning. Dynamic hypoxia (DH) regime of changing oxygen levels from 21% to 1% when transitioning from T-flask subculture to multiwell plate seeding was most effective at eliciting pro-angiogenic response from adMSC for both in vitro 2D and 3D models compared to controls using static normoxia (21% oxygen) and static hypoxia (1%). Low seeding density of adMSC was found to be the most appropriate to ensure optimised cell adherence and survival post-seeding on TE dermal scaffold (INTEGRA®). It also minimised on localised hypoxic gradient induced oxidative stress by the seeded cells when compared to high seeding density techniques found on non-invasive oxygen monitoring. Conditioned media from DH seeded adMSC was shown to have enhanced angiogenic proteomic profile compared to the controls. In vitro angiogenesis assays showed better human endothelial cell migration and morphogenesis in scratch assay and tubular formation assay compared to controls. Preliminary ex vivo organ assay results using novel human umbilical arterial rings showed better endothelial out-sprouting and migration through embedded matrix compared to controls. Results from in vivo transplantation of adMSC seeded INTEGRA® scaffold showed a mixed response in the CAM assay, highlighting an unaccounted scaffold effect from INTEGRA® from the host. Histological sections showed increased vascular and host tissue infiltration into the scaffold. When evaluating the functional angiogenesis in murine wound healing models, although DH adMSC seeded scaffolds showed non-statistically significant increased rate of wound closure, there was significantly greater vessel density within the scaffold on histological evaluation in this group compared to controls. Conclusion: The results provide a better comprehension of how cells behave in 2D and 3D environments when cultured in dynamically changing oxygen environments. The study addresses important issues, such as the effects of chronic hypoxia on MSC, and how dynamic hypoxia can enhance angiogenic signalling. It also offers a crucial understanding of the in vitro oxygen culture environments for future research applications. Further insight into cell-scaffold interaction during in vivo transplantation was also established. The importance of having an appropriate in vivo model to determine if such in vitro angiogenic enhancement would translate to functionally improving neoangiogenesis and subsequent tissue regeneration in vivo was also highlighted in this study. Improving and advancing research into optimising and evaluating the in vitro environment for clinical application will undoubtedly have a huge impact on the future of cell therapy for regenerative medicine purposes

Personality Identification from Social Media Using Deep Learning: A Review

Social media helps in sharing of ideas and information among people scattered around the world and thus helps in creating communities, groups, and virtual networks. Identification of personality is significant in many types of applications such as in detecting the mental state or character of a person, predicting job satisfaction, professional and personal relationship success, in recommendation systems. Personality is also an important factor to determine individual variation in thoughts, feelings, and conduct systems. According to the survey of Global social media research in 2018, approximately 3.196 billion social media users are in worldwide. The numbers are estimated to grow rapidly further with the use of mobile smart devices and advancement in technology. Support vector machine (SVM), Naive Bayes (NB), Multilayer perceptron neural network, and convolutional neural network (CNN) are some of the machine learning techniques used for personality identification in the literature review. This paper presents various studies conducted in identifying the personality of social media users with the help of machine learning approaches and the recent studies that targeted to predict the personality of online social media (OSM) users are reviewed