Melanoma is one of the most prevalent skin cancers in the world. The incidence and mortality rates of melanoma in Australian populations have been sharply increasing over the last decades. For instance, it is represented that two in three Australian develops some form of skin cancer before they reach the age of 70. Most melanoma can be cured if diagnosed and treated in the early stages. Over the past decades, advances in dermoscopy technology has made it an effective technique used in early diagnosis of malignant melanoma. Dermoscopy allows the clinicians to visualise different colours and examine microstructures in the skin that are not visible to the naked eye. This clear view of the skin reduces screening errors and improves the diagnostic accuracy of pigmented skin lesions significantly. However, it has been demonstrated that the performance and accuracy of melanoma diagnosis using dermoscopic images manually depend on the quality of the image and the clinical experience of the dermatologists. Several medical diagnosis methods have been developed to help dermatologists interpret the structures revealed through dermoscopy, such as the pattern analysis, the ABCD rule, the 7-point checklist, the Menzies method, CASH algorithm, the Chaos and Clues algorithm and the BLINCK algorithm. However, the diagnosis criteria used in assessing the potential of melanoma may be easily overlooked in early melanomas, or be misinterpreted as a benign mole, mainly attending to the subjectivity of clinical interpretation. Also, human judgement is often hardly reproducible. Therefore, clinical diagnosis is still challenging, especially with equivocal pigmented lesions, which leading to the accuracy of melanoma diagnosis by expert dermatologists remains at 75–84%. Only biopsy or excision of a pigmented skin lesion can provide a definitive diagnosis. However, a biopsy can rise metastasizing, in addition to be being invasive and an unpleasant experience to the patient. Therefore, to minimise the diagnostic errors, and provide a reliable second independent opinion to dermatologists, the development of computerised image analysis techniques is of paramount importance. In the last decade, several computer-aided diagnosis (CAD) systems have been proposed to tackle this problem. However, the diversity of existing problems makes any further contributions greatly appreciated. Moreover, it is widely acknowledged that much higher accuracy is required for computer-based system to be considered reliable and trustworthy enough by clinicians, therefore be adopted routinely in their diagnostic process. With the aim of improving some of existing approaches and developing new techniques to facilitate accurate, fast and more reliable computer-based diagnosis of melanoma, this thesis describes novel image processing approaches for computer-aided detection on selected subset of medical criteria that play an important role in the diagnosis of melanoma. This ensures that the features used by the system have a medical meaning, making it possible for the dermatologist to understand and validate the automated diagnosis. One of the contributions of this thesis is to develop a fast and accurate colour detection method. It is observed that colours may vary slightly in dermoscopy images, because of different levels of contrast. This may lead to difficulty in the perception of colours by dermatologists, resulting in subjectivity of clinical diagnosis. A computer-assisted system for quantitative colour identification is highly desirable for dermatologists to use. However, these colour variations within the lesion makes colour detection a challenging process. To tackle this challenge, a comprehensive colour detection procedure is conducted in this thesis. It incorporates a colour enhancement step to overcome the problems of poor contrast. Since colours perceived by the human observer are produced by a mixture of pixel values, we performed a summarised representation of colours by subdividing the colour space into colour clusters, using QuadTree clustering, comprising a set of RGB values. The proposed method employed a colour palette, to mimic human interpretation of of lesion colours in determining the type and the number of colours in melanocytic lesion images. In addition, a set of parameters such as colour feature set, texture feature set, and locational features is extracted to numerically describe the colour properties of each segmented block throughout the lesion. Furthermore, when comparing colour distribution in malignant melanomas (MMs) and benign melanomas (BMs), a significant difference in the number of colours in the two populations is detected. Also, the proposed method shown that the type of colour can greatly affect in the diagnosis outcome. The effectiveness of the proposed colour detection system is evaluated by comparing the obtained results with those obtained by using expert dermatologists. The highest correlation coefficients for detecting the type of colour is observed for red and blue–grey, which, in respect of the image set used in this thesis, signifies the most important colours for diagnosis purposes. The overall performance of the proposed system is evaluated by using machine learning techniques, and the best classification results, AUC of 0.93, are achieved by using kernel SVM classifier. Another contribution of this thesis is to provide meaningful visualisation of streak, and extract features to determine the relative importance of streak in classifying the skin lesion into two class of benign and malignant. To find streaks, a trainable B-COSFIRE filter applied in dermoscopy images to detect a prototype pattern of interest (bar-shaped structures) such as streak. Its application consists of convolution with Difference of Gaussian (DoG) filters, its blurring responses; shifting the blurred responses and estimate a point-wise weighted Geometric Mean (GM). To also account the different thickness and structure of streak a bank of B-COSFIRE filter is applied on the image with different orientation and rotation. Then to identify valid streaks from candidate streak lines, clinical criteria such as number of streaks in the images and the orientation pattern analysis is calculated and the false detected lines are removed. The result includes line segments that indicate the pixels that belong to streaks are displayed. Also, a set of features derived from streaks (such as geometrics, colour and texture features) are fed to three different classifiers for classifying images. We achieved an accuracy of 93.3% for classifying dermoscopy images into benign and malignant on 807 dermoscopy images. Furthermore, a novel, comprehensive and highly effective application of deep learning (stacked sparse auto-encoders) is examined in this thesis for classification of skin lesion. The model learns a hierarchal high-level feature representation of skin image in an unsupervised manner. The stacked sparse auto-encoder discovers latent information features in input images (pixel intensities). These high-level features are subsequently fed into a classifier for classifying dermoscopy images. In addition, we proposed a new deep neural network architecture based on bag-of-features (BoF) model, which learns high-level image representation and maps images into BoF space. We have shown that using BoF as the input to the auto-encoder can easily improve the performance of neural network in comparison with the raw input images. The proposed method is evaluated on a test set of 244 skin images and result shown that the deep BoF model achieves higher classification scores (with SE = 95.4% and SP = 94.9%) in compare to the raw input images. Our contributions will improve automated diagnosis of melanoma using dermoscopy images.

Steady-state and time-resolved fluorescence spectroscopy has been used to examine lateral diffusion in dipalmitoyl-L-alpha-phosphatidylcholine (DPPC) and dimyristoyl-L-alpha-phosphatidylcholine (DMPC) monolayers at the air-water interface, by studying the fluorescence quenching of a pyrene-labeled phospholipid (pyrene-DPPE) by two amphiphilic quenchers. Steady-state fluorescence measurements revealed pyrene-DPPE to be homogeneously distributed in the DMPC lipid matrix for all measured surface pressures and only in the liquid-expanded (LE) phase of the DPPC monolayer. Time-resolved fluorescence decays for pyrene-DPPE in DMPC and DPPC (LE phase) in the absence of quencher were best described by a single-exponential function, also suggesting a homogeneous distribution of pyrene-DPPE within the monolayer films. Addition of quencher to the monolayer film produced nonexponential decay behavior, which is adequately described by the continuum theory of diffusion-controlled quenching in a two-dimensional environment. Steady-state fluorescence measurements yielded lateral diffusion coefficients significantly larger than those obtained from time-resolved data. The difference in these values was ascribed to the influence of static quenching in the case of the steady-state measurements. The lateral diffusion coefficients obtained in the DMPC monolayers were found to decrease with increasing surface pressure, reflecting a decrease in monolayer fluidity with compression.

The Z-disk or Z-line is located at the lateral borders of sarcomere, the fundamental unit of striated muscle. They provide mechanical stability and can boost contractility of cardiac myocytes. In this paper, we propose to generate a 3D model of Z-disks within single adult cardiac cells from an automated segmentation of a large serial-block-face scanning electron microscopy (SBF-SEM) dataset. The proposed fully automated segmentation scheme is comprised of three main modules including “pre-processing”, “segmentation” and “refinement”. We represent a timely-efficient, simple, yet effective model to perform segmentation and refinement steps. Contrast stretching, and Gaussian kernels are used to pre- process the dataset, and well-known “Sobel operators” are used in the segmentation module. We have validated our model by comparing segmentation results with ground-truth annotated Z-disks in terms of pixel-wise accuracy. The results show that our model correctly detects Z-disks with 90.56% accuracy. Finally, the underlying network of Z-disks are rendered in 3D using ImageJ and IMARIS.

AIMS: Combining mesenchymal stem cells (MSCs) and chondrocytes has great potential for cell-based cartilage repair. However, there is much debate regarding the mechanisms behind this concept. We aimed to clarify the mechanisms that lead to chondrogenesis (chondrocyte driven MSC-differentiation versus MSC driven chondroinduction) and whether their effect was dependent on MSC-origin. Therefore, chondrogenesis of human adipose-tissue-derived MSCs (hAMSCs) and bone-marrow-derived MSCs (hBMSCs) combined with bovine articular chondrocytes (bACs) was compared. METHODS: hAMSCs or hBMSCs were combined with bACs in alginate and cultured in vitro or implanted subcutaneously in mice. Cartilage formation was evaluated with biochemical, histological and biomechanical analyses. To further investigate the interactions between bACs and hMSCs, (1) co-culture, (2) pellet, (3) Transwell® and (4) conditioned media studies were conducted. RESULTS: The presence of hMSCs-either hAMSCs or hBMSCs-increased chondrogenesis in culture; deposition of GAG was most evidently enhanced in hBMSC/bACs. This effect was similar when hMSCs and bAC were combined in pellet culture, in alginate culture or when conditioned media of hMSCs were used on bAC. Species-specific gene-expression analyses demonstrated that aggrecan was expressed by bACs only, indicating a predominantly trophic role for hMSCs. Collagen-10-gene expression of bACs was not affected by hBMSCs, but slightly enhanced by hAMSCs. After in-vivo implantation, hAMSC/bACs and hBMSC/bACs had similar cartilage matrix production, both appeared stable and did not calcify. CONCLUSIONS: This study demonstrates that replacing 80% of bACs by either hAMSCs or hBMSCs does not influence cartilage matrix production or stability. The remaining chondrocytes produce more matrix due to trophic factors produced by hMSCs.

According to 2010 estimates from The National Institute on Deafness and other Communication Disorders, approximately 17% (36 million) American adults have reported some degree of hearing loss. Currently, the only clinical treatment available for those with severe-to-profound hearing loss is a cochlear implant, which is designed to electrically stimulate the auditory nerve in the absence of hair cells. Whilst the cochlear implant has been revolutionary in terms of providing hearing to the severe-to-profoundly deaf, there are variations in cochlear implant performance which may be related to the degree of degeneration of auditory neurons following hearing loss. Hence, numerous experimental studies have focused on enhancing the efficacy of cochlear implants by using neurotrophins to preserve the auditory neurons, and more recently, attempting to replace these dying cells with new neurons derived from stem cells. As a result, several groups are now investigating the potential for both embryonic and adult stem cells to replace the degenerating sensory elements in the deaf cochlea. Recent advances in our knowledge of stem cells and the development of induced pluripotency by Takahashi and Yamanaka in 2006, have opened a new realm of science focused on the use of induced pluripotent stem (iPS) cells for therapeutic purposes. This review will provide a broad overview of the potential benefits and challenges of using iPS cells in combination with a cochlear implant for the treatment of hearing loss, including differentiation of iPS cells into an auditory neural lineage and clinically relevant transplantation approaches.

In this paper, a novel foaming methodology consisting of turbulent mixing and thermally induced phase separation (TIPS) was used to generate scaffolds for tissue engineering. Air bubbles were mechanically introduced into a chitosan solution which forms the continuous polymer/liquid phase in the foam created. The air bubbles entrained in the foam act as a template for the macroporous architecture of the final scaffolds. Wet foams were crosslinked via glutaraldehyde and frozen at -20 °C to induce TIPS in order to limit film drainage, bubble coalescence and Ostwald ripening. The effects of production parameters, including mixing speed, surfactant concentration and chitosan concentration, on foaming are explored. Using this method, hydrogel scaffolds were successfully produced with up to 80% porosity, average pore sizes of 120 μm and readily tuneable compressive modulus in the range of 2.6 to 25 kPa relevant to soft tissue engineering applications. These scaffolds supported 3T3 fibroblast cell proliferation and penetration and therefore show significant potential for application in soft tissue engineering.

Epilepsy is one of the most common neurological disorders and is characterized by recurrent seizures. We use theoretical neuroscience tools to study brain dynamics during seizures. We derive and simulate a computational model of a network of hippocampal neuronal populations. Each population within the network is based on a model that has been shown to replicate the electrophysiological dynamics observed during seizures. The results provide insights into possible mechanisms for seizure spread. We observe that epileptiform activity remains localized to a pathological region when a global connectivity parameter is less than a critical value. After establishing the critical value for seizure spread, we explored how to correct the effect by altering particular synaptic gains. The spreading of seizures is quantified using numerical methods for seizure detection. The results from this study provide a new avenue of exploration for seizure control.

Intrinsic sensory neurons (ISNs) of the enteric nervous system respond to stimuli such as muscle tension, muscle length, distortion of the mucosa, and the chemical content in the lumen. ISNs form recurrent networks that probably drive many intestinal motor patterns and reflexes. ISNs express a large number of voltage- and calcium-gated ion channels, some of which are modified by inflammation or repeated physiological stimuli, but how interactions between different ionic currents in ISNs produce both normal and pathological behaviors in the intestine remains unclear. We constructed a model of ISNs including voltage-gated sodium and potassium channels, N-type calcium channels, big conductance calcium-dependent potassium (BK) channels, calcium-dependent nonspecific cation channels (NSCa), intermediate conductance calcium-dependent potassium (IK) channels, hyperpolarization-activated cation (Ih) channels, and internal calcium dynamics. The model was based on data from the literature and our electrophysiological studies. The model reproduced responses to short or long depolarizing current pulses and responses to long hyperpolarizing current pulses. Sensitivity analysis showed that Ih, IK, NSCa, and BK have the largest influence on the number of action potentials observed during prolonged depolarizations. The model also predicts that changes to the voltage of activation for Ih have a large influence on excitability, but changes to the time constant of activation for Ih have a minor effect. Our model identifies how interactions between different iconic currents influence the excitability of ISNs and highlights an important role for Ih in enteric neuroplasticity resulting from disease.

Invasive Brain-Computer Interfaces (BCIs) require surgeries with high health-risks. The risk-to-benefit ratio of the procedure could potentially be improved by pre-surgically identifying the ideal locations for mental strategy classification. We recorded high-spatiotemporal resolution blood-oxygenation-level-dependent (BOLD) signals using functional MRI at 7 Tesla in eleven healthy participants during two motor imagery tasks. BCI diagnostic task isolated the intent to imagine movements, while BCI simulation task simulated the neural states that may be yielded in a real-life BCI-operation scenario. Imagination of movements were classified from the BOLD signals in sub-regions of activation within a single or multiple dorsal motor network regions. Then, the participant's decoding performance during the BCI simulation task was predicted from the BCI diagnostic task. The results revealed that drawing information from multiple regions compared to a single region increased the classification accuracy of imagined movements. Importantly, systematic unimodal and multimodal classification revealed the ideal combination of regions that yielded the best classification accuracy at the individual-level. Lastly, a given participant's decoding performance achieved during the BCI simulation task could be predicted from the BCI diagnostic task. These results show the feasibility of 7T-fMRI with unimodal and multimodal classification being utilized for identifying ideal sites for mental strategy classification.

Development of a neural interface that can be implanted without risky, open brain surgery will increase the safety and viability of chronic neural recording arrays. We have developed a minimally invasive surgical procedure and an endovascular electrode-array that can be delivered to overlie the cortex through blood vessels. Here, we describe feasibility of the endovascular interface through electrode viability, recording potential and safety. Electrochemical impedance spectroscopy demonstrated that electrode impedance was stable over 91 days and low frequency phase could be used to infer electrode incorporation into the vessel wall. Baseline neural recording were used to identify the maximum bandwidth of the neural interface, which remained stable around 193 Hz for six months. Cross-sectional areas of the implanted vessels were non-destructively measured using the Australian Synchrotron. There was no case of occlusion observed in any of the implanted animals. This work demonstrates the feasibility of an endovascular neural interface to safely and efficaciously record neural information over a chronic time course.

Previous studies have shown that the auditory cortex can enhance the perception of behaviorally important sounds in the presence of background noise, but the mechanisms by which it does this are not yet elucidated. Rapid plasticity of spectrotemporal receptive fields (STRFs) in the primary (A1) cortical neurons is observed during behavioral tasks that require discrimination of particular sounds. This rapid task-related change is believed to be one of the processing strategies utilized by the auditory cortex to selectively attend to one stream of sound in the presence of mixed sounds. However, the mechanism by which the brain evokes this rapid plasticity in the auditory cortex remains unclear. This paper uses a neural network model to investigate how synaptic transmission within the cortical neuron network can change the receptive fields of individual neurons. A sound signal was used as input to a model of the cochlea and auditory periphery, which activated or inhibited integrate-and-fire neuron models to represent networks in the primary auditory cortex. Each neuron in the network was tuned to a different frequency. All neurons were interconnected with excitatory or inhibitory synapses of varying strengths. Action potentials in one of the model neurons were used to calculate the receptive field using reverse correlation. The results were directly compared to previously recorded electrophysiological data from ferrets performing behavioral tasks that require discrimination of particular sounds. The neural network model could reproduce complex STRFs observed experimentally through optimizing the synaptic weights in the model. The model predicts that altering synaptic drive between cortical neurons and/or bottom-up synaptic drive from the cochlear model to the cortical neurons can account for rapid task-related changes observed experimentally in A1 neurons. By identifying changes in the synaptic drive during behavioral tasks, the model provides insights into the neural mechanisms utilized by the auditory cortex to enhance the perception of behaviorally salient sounds.