MobiMed: Framework for Rapid Application Development of Medical Mobile Apps

In the medical field images obtained from high definition cameras and other medical imaging systems are an integral part of medical diagnosis. The analysis of these images are usually performed by the physicians who sometimes need to spend long hours reviewing the images before they are able to come up with a diagnosis and then decide on the course of action. In this dissertation we present a framework for a computer-aided analysis of medical imagery via the use of an expert system. While this problem has been discussed before, we will consider a system based on mobile devices. Since the release of the iPhone on April 2003, the popularity of mobile devices has increased rapidly and our lives have become more reliant on them. This popularity and the ease of development of mobile applications has now made it possible to perform on these devices many of the image analyses that previously required a personal computer. All of this has opened the door to a whole new set of possibilities and freed the physicians from their reliance on their desktop machines. The approach proposed in this dissertation aims to capitalize on these new found opportunities by providing a framework for analysis of medical images that physicians can utilize from their mobile devices thus remove their reliance on desktop computers. We also provide an expert system to aid in the analysis and advice on the selection of medical procedure. Finally, we also allow for other mobile applications to be developed by providing a generic mobile application development framework that allows for access of other applications into the mobile domain. In this dissertation we outline our work leading towards development of the proposed methodology and the remaining work needed to find a solution to the problem. In order to make this difficult problem tractable, we divide the problem into three parts: the development user interface modeling language and tooling, the creation of a game development modeling language and tooling, and the development of a generic mobile application framework. In order to make this problem more manageable, we will narrow down the initial scope to the hair transplant, and glaucoma domains

Computer-aided diagnosis of gynaecological abnormality using B-mode ultrasound images

Ultrasound scan is one of the most reliable imaging for detecting/diagnosing of gynaecological abnormalities. Ultrasound imaging is widely used during pregnancy and has become central in the management of the problems of early pregnancy, particularly in miscarriage diagnosis. Also ultrasound is considered as the most important imaging modality in the evaluation of different types of ovarian tumours. The early detection of ovarian carcinoma and miscarriage continues to be a challenging task. It mostly relies on manual examination, interpretation by gynaecologists, of the ultrasound scan images that may use morphology features extracted from the region of interest. Diagnosis depends on using certain scoring systems that have been devised over a long time. The manual diagnostic process involves multiple subjective decisions, with increased inter- and intra-observer variations which may lead to serious errors and health implications. This thesis is devoted to developing computer-based tools that use ultrasound scan images for automatic classification of Ovarian Tumours (Benign or Malignant) and automatic detection of Miscarriage cases at early stages of pregnancy. Our intended computational tools are meant to help gynaecologists to improve accuracy of their diagnostic decisions, while serving as a tool for training radiology students/trainees on diagnosing gynaecological abnormalities. Ultimately, it is hoped that the developed techniques can be integrated into a specialised gynaecology Decision Support System. Our approach is to deal with this problem by adopting a standard image-based pattern recognition research framework that involve the extraction of appropriate feature vector modelling of the investigated tumours, select appropriate classifiers, and test the performance of such schemes using sufficiently large and relevant datasets of ultrasound scan images. We aim to complement the automation of certain parameters that gynaecologist experts and radiologists manually determine, by image-content information attributes that may not be directly accessible without advanced image transformations. This is motivated by, and benefit from, advances in computer vision that led the emergence of a variety of image processing/analysis techniques together with recent advances in data mining and machine learning technologies. An expert observer makes a diagnostic decision with a level of certainty, and if not entirely certain about their diagnostic decisions then often other experts’ opinions are sought and may be essential for diagnosing difficult “Inconclusive cases”. Here we define a quantitative measure of confidence in decisions made by automatic diagnostic schemes, independent of accuracy of decision. In the rest of the thesis, we report on the development of a variety of innovative diagnostic schemes and demonstrate their performances using extensive experimental work. The following is a summary of the main contributions made in this thesis. 1. Using a combination of spatial domain filters and operations as pre-processing procedures to enhance ultrasound images for both applications, namely miscarriage identification and ovarian tumour diagnosis. We show that the Non-local means filter is effective in reducing speckle noise from ultrasound images, and together with other filters we succeed in enhancing the inner border of malignant tumours and reliably segmenting the gestational sac. 2. Developing reliable automated procedures to extract several types of features to model gestational sac dimensional measurements, few of which are manually determined by radiologist and used by gynaecologists to identify miscarriage cases. We demonstrate that the corresponding automatic diagnostic schemes yield excellent accuracy when classified by the k-Nearest Neighbours. 3. Developing several local as well as global image-texture based features in the spatial as well as the frequency domains. The spatial domain features include the local versions of image histograms, first order statistical features and versions of local binary patterns. From the frequency domain, we propose a novel set of Fast Fourier Geometrical Features that encapsulates the image texture information that depends on all image pixel values. We demonstrate that each of these features define Ovarian Tumour diagnostic scheme that have relatively high power of discriminating Benign from Malignant tumours when classified by Support Vector Machine. We show that the Fast Fourier Geometrical Features are the best performing scheme achieving more than 85% accuracy. 4. Introducing a simple measure of confidence to quantify the goodness of the automatic diagnostic decision, regardless of decision accuracy, to emulate real life medical diagnostics. Experimental work in this theis demonstrate a strong link between this measure and accuracy rate, so that low level of confidence could raise an alarm. 5. Conducting sufficiently intensive investigations of fusion models of multi-feature schemes at different level. We show that feature level fusion yields degraded performance compared to all its single components, while score level fusion results in improved results and decision level fusion of three sets of features using majority rule is slightly less successful. Using the measure of confidence is useful in resolving conflicts when two sets of features are fused at the decision level. This leads to the emergence of a Not Sure decision which is common in medical practice. Considering the Not Sure label is a good practice and an incentive to conduct more tests, rather than misclassification, which leads to significantly improved accuracy. The thesis concludes with an intensive discussion on future work that would go beyond improving performance of the developed scheme to deal with the corresponding multi-class diagnostics essential for a comprehensive gynaecology Decision Support System tool as the ultimate goal

Spatial Analysis of High Parameter Imaging Data to Understand Early HIV-Cell Interactions in Colorectal Tissue

Although Human Immunodeficiency Virus (HIV) was first discovered over 30 years ago,there remains no cure or vaccine for HIV. In Australia, HIV transmission almost exclusively occurs via sexual intercourse, with over 75% occurring as a result of anal intercourse. Due to technological limitations, there are still major gaps in understanding how HIV is transmitted across the human colorectum, with the role of key HIV target cells being unclear. Additionally, traditional microscopy methods have not been able to visualize the full repertoire of HIV target cells within tissue. To investigate cellular interactions during early HIV transmission, we topically infect fresh human colorectal tissue with HIV as a model of early HIV transmission in situ. We then make use of Cyclic Immunofluoresecence, a high parameter microscopy imaging approach, to visualise key HIV target cells, namely dendritic cells, macrophages and CD4+ T lymphocytes could be observed within the same tissue section. Furthermore, through the in situ hybridization technique RNAscope, we are able to visualize HIV with single-virion sensitivity. Cyclic Immunofluorescence and RNAscope provide a unique opportunity for visualizing the early events of HIV transmission, allowing the interactions of HIV with its target cells as well as the colorectal tissue structure to be captured. However, given the recent development of these techniques, the imaging data being produced provide many unique challenges in terms of computational analysis. In particular, the presence of autofluorescence makes it difficult to reliably assign marker expression to cells while spatial analytical techniques are limited when multiple cell types are measured simultaneously. This thesis presents three publications which address these analytical gaps, while also detailing the computational analysis techniques utilized in the analysis of the HIV imaging dataset

Low-dimensional representations of neural time-series data with applications to peripheral nerve decoding

Bioelectronic medicines, implanted devices that influence physiological states by peripheral neuromodulation, have promise as a new way of treating diverse conditions from rheumatism to diabetes. We here explore ways of creating nerve-based feedback for the implanted systems to act in a dynamically adapting closed loop. In a first empirical component, we carried out decoding studies on in vivo recordings of cat and rat bladder afferents. In a low-resolution data-set, we selected informative frequency bands of the neural activity using information theory to then relate to bladder pressure. In a second high-resolution dataset, we analysed the population code for bladder pressure, again using information theory, and proposed an informed decoding approach that promises enhanced robustness and automatic re-calibration by creating a low-dimensional population vector. Coming from a different direction of more general time-series analysis, we embedded a set of peripheral nerve recordings in a space of main firing characteristics by dimensionality reduction in a high-dimensional feature-space and automatically proposed single efficiently implementable estimators for each identified characteristic. For bioelectronic medicines, this feature-based pre-processing method enables an online signal characterisation of low-resolution data where spike sorting is impossible but simple power-measures discard informative structure. Analyses were based on surrogate data from a self-developed and flexibly adaptable computer model that we made publicly available. The wider utility of two feature-based analysis methods developed in this work was demonstrated on a variety of datasets from across science and industry. (1) Our feature-based generation of interpretable low-dimensional embeddings for unknown time-series datasets answers a need for simplifying and harvesting the growing body of sequential data that characterises modern science. (2) We propose an additional, supervised pipeline to tailor feature subsets to collections of classification problems. On a literature standard library of time-series classification tasks, we distilled 22 generically useful estimators and made them easily accessible.Open Acces

Efficient Mission Planning for Robot Networks in Communication Constrained Environments

Many robotic systems are remotely operated nowadays that require uninterrupted connection and safe mission planning. Such systems are commonly found in military drones, search and rescue operations, mining robotics, agriculture, and environmental monitoring. Different robotic systems may employ disparate communication modalities such as radio network, visible light communication, satellite, infrared, Wi-Fi. However, in an autonomous mission where the robots are expected to be interconnected, communication constrained environment frequently arises due to the out of range problem or unavailability of the signal. Furthermore, several automated projects (building construction, assembly line) do not guarantee uninterrupted communication, and a safe project plan is required that optimizes collision risks, cost, and duration. In this thesis, we propose four pronged approaches to alleviate some of these issues: 1) Communication aware world mapping; 2) Communication preserving using the Line-of-Sight (LoS); 3) Communication aware safe planning; and 4) Multi-Objective motion planning for navigation. First, we focus on developing a communication aware world map that integrates traditional world models with the planning of multi-robot placement. Our proposed communication map selects the optimal placement of a chain of intermediate relay vehicles in order to maximize communication quality to a remote unit. We also vi propose an algorithm to build a min-Arborescence tree when there are multiple remote units to be served. Second, in communication denied environments, we use Line-of-Sight (LoS) to establish communication between mobile robots, control their movements and relay information to other autonomous units. We formulate and study the complexity of a multi-robot relay network positioning problem and propose approximation algorithms that restore visibility based connectivity through the relocation of one or more robots. Third, we develop a framework to quantify the safety score of a fully automated robotic mission where the coexistence of human and robot may pose a collision risk. A number of alternate mission plans are analyzed using motion planning algorithms to select the safest one. Finally, an efficient multi-objective optimization based path planning for the robots is developed to deal with several Pareto optimal cost attributes

Methodology for automatic classification of atypical lymphoid cells from peripheral blood cell images

Morphological analysis is the starting point for the diagnostic approach of more than 80% of the hematological diseases. However, the morphological differentiation among different types of abnormal lymphoid cells in peripheral blood is a difficult task, which requires high experience and skill. Objective values do not exist to define cytological variables, which sometimes results in doubts on the correct cell classification in the daily hospital routine. Automated systems exist which are able to get an automatic preclassification of the normal blood cells, but fail in the automatic recognition of the abnormal lymphoid cells. The general objective of this thesis is to develop a complete methodology to automatically recognize images of normal and reactive lymphocytes, and several types of neoplastic lymphoid cells circulating in peripheral blood in some mature B-cell neoplasms using digital image processing methods. This objective follows two directions: (1) with engineering and mathematical background, transversal methodologies and software tools are developed; and (2) with a view towards the clinical laboratory diagnosis, a system prototype is built and validated, whose input is a set of pathological cell images from individual patients, and whose output is the automatic classification in one of the groups of the different pathologies included in the system. This thesis is the evolution of various works, starting with a discrimination between normal lymphocytes and two types of neoplastic lymphoid cells, and ending with the design of a system for the automatic recognition of normal lymphocytes and five types of neoplastic lymphoid cells. All this work involves the development of a robust segmentation methodology using color clustering, which is able to separate three regions of interest: cell, nucleus and peripheral zone around the cell. A complete lymphoid cell description is developed by extracting features related to size, shape, texture and color. To reduce the complexity of the process, a feature selection is performed using information theory. Then, several classifiers are implemented to automatically recognize different types of lymphoid cells. The best classification results are achieved using support vector machines with radial basis function kernel. The methodology developed, which combines medical, engineering and mathematical backgrounds, is the first step to design a practical hematological diagnosis support tool in the near future.Los análisis morfológicos son el punto de partida para la orientación diagnóstica en más del 80% de las enfermedades hematológicas. Sin embargo, la clasificación morfológica entre diferentes tipos de células linfoides anormales en la sangre es una tarea difícil que requiere gran experiencia y habilidad. No existen valores objetivos para definir variables citológicas, lo que en ocasiones genera dudas en la correcta clasificación de las células en la práctica diaria en un laboratorio clínico. Existen sistemas automáticos que realizan una preclasificación automática de las células sanguíneas, pero no son capaces de diferenciar automáticamente las células linfoides anormales. El objetivo general de esta tesis es el desarrollo de una metodología completa para el reconocimiento automático de imágenes de linfocitos normales y reactivos, y de varios tipos de células linfoides neoplásicas circulantes en sangre periférica en algunos tipos de neoplasias linfoides B maduras, usando métodos de procesamiento digital de imágenes. Este objetivo sigue dos direcciones: (1) con una orientación propia de la ingeniería y la matemática de soporte, se desarrollan las metodologías transversales y las herramientas de software para su implementación; y (2) con un enfoque orientado al diagnóstico desde el laboratorio clínico, se construye y se valida un prototipo de un sistema cuya entrada es un conjunto de imágenes de células patológicas de pacientes analizados de forma individual, obtenidas mediante microscopía y cámara digital, y cuya salida es la clasificación automática en uno de los grupos de las distintas patologías incluidas en el sistema. Esta tesis es el resultado de la evolución de varios trabajos, comenzando con una discriminación entre linfocitos normales y dos tipos de células linfoides neoplásicas, y terminando con el diseño de un sistema para el reconocimiento automático de linfocitos normales y reactivos, y cinco tipos de células linfoides neoplásicas. Todo este trabajo involucra el desarrollo de una metodología de segmentación robusta usando agrupamiento por color, la cual es capaz de separar tres regiones de interés: la célula, el núcleo y la zona externa alrededor de la célula. Se desarrolla una descripción completa de la célula linfoide mediante la extracción de descriptores relacionados con el tamaño, la forma, la textura y el color. Para reducir la complejidad del proceso, se realiza una selección de descriptores usando teoría de la información. Posteriormente, se implementan varios clasificadores para reconocer automáticamente diferentes tipos de células linfoides. Los mejores resultados de clasificación se logran utilizando máquinas de soporte vectorial con núcleo de base radial. La metodología desarrollada, que combina conocimientos médicos, matemáticos y de ingeniería, es el primer paso para el diseño de una herramienta práctica de soporte al diagnóstico hematológico en un futuro cercano

Integrating passive ubiquitous surfaces into human-computer interaction

Mobile technologies enable people to interact with computers ubiquitously. This dissertation investigates how ordinary, ubiquitous surfaces can be integrated into human-computer interaction to extend the interaction space beyond the edge of the display. It turns out that acoustic and tactile features generated during an interaction can be combined to identify input events, the user, and the surface. In addition, it is shown that a heterogeneous distribution of different surfaces is particularly suitable for realizing versatile interaction modalities. However, privacy concerns must be considered when selecting sensors, and context can be crucial in determining whether and what interaction to perform.Mobile Technologien ermöglichen den Menschen eine allgegenwärtige Interaktion mit Computern. Diese Dissertation untersucht, wie gewöhnliche, allgegenwärtige Oberflächen in die Mensch-Computer-Interaktion integriert werden können, um den Interaktionsraum über den Rand des Displays hinaus zu erweitern. Es stellt sich heraus, dass akustische und taktile Merkmale, die während einer Interaktion erzeugt werden, kombiniert werden können, um Eingabeereignisse, den Benutzer und die Oberfläche zu identifizieren. Darüber hinaus wird gezeigt, dass eine heterogene Verteilung verschiedener Oberflächen besonders geeignet ist, um vielfältige Interaktionsmodalitäten zu realisieren. Bei der Auswahl der Sensoren müssen jedoch Datenschutzaspekte berücksichtigt werden, und der Kontext kann entscheidend dafür sein, ob und welche Interaktion durchgeführt werden soll

Book of Abstracts 15th International Symposium on Computer Methods in Biomechanics and Biomedical Engineering and 3rd Conference on Imaging and Visualization

In this edition, the two events will run together as a single conference, highlighting the strong connection with the Taylor & Francis journals: Computer Methods in Biomechanics and Biomedical Engineering (John Middleton and Christopher Jacobs, Eds.) and Computer Methods in Biomechanics and Biomedical Engineering: Imaging and Visualization (JoãoManuel R.S. Tavares, Ed.). The conference has become a major international meeting on computational biomechanics, imaging andvisualization. In this edition, the main program includes 212 presentations. In addition, sixteen renowned researchers will give plenary keynotes, addressing current challenges in computational biomechanics and biomedical imaging. In Lisbon, for the first time, a session dedicated to award the winner of the Best Paper in CMBBE Journal will take place. We believe that CMBBE2018 will have a strong impact on the development of computational biomechanics and biomedical imaging and visualization, identifying emerging areas of research and promoting the collaboration and networking between participants. This impact is evidenced through the well-known research groups, commercial companies and scientific organizations, who continue to support and sponsor the CMBBE meeting series. In fact, the conference is enriched with five workshops on specific scientific topics and commercial software.info:eu-repo/semantics/draf

Topics in Cancer Survivorship

Cancer is now the leading cause of death in the world. In the U.S., one in two men and one in three women will be diagnosed with a non-skin cancer in their lifetime. Cancer patients are living longer than ever before. For instance, when detected early, the five-year survival for breast cancer is 98%, and it is about 84% in patients with regional disease. However, the diagnosis and treatment of cancer is very distressing. Cancer patients frequently suffer from pain, disfigurement, depression, fatigue, physical dysfunctions, frequent visits to doctors and hospitals, multiple tests and procedures with the possibility of treatment complications, and the financial impact of the diagnosis on their life. This book presents a number of ways that can help cancer patients to look, feel and become healthier, take care of specific symptoms such as hair loss, arm swelling, and shortness of breath, and improve their intimacy, sexuality, and fertility