Medical data processing and analysis for remote health and activities monitoring

Recent developments in sensor technology, wearable computing, Internet of Things (IoT), and wireless communication have given rise to research in ubiquitous healthcare and remote monitoring of human\u2019s health and activities. Health monitoring systems involve processing and analysis of data retrieved from smartphones, smart watches, smart bracelets, as well as various sensors and wearable devices. Such systems enable continuous monitoring of patients psychological and health conditions by sensing and transmitting measurements such as heart rate, electrocardiogram, body temperature, respiratory rate, chest sounds, or blood pressure. Pervasive healthcare, as a relevant application domain in this context, aims at revolutionizing the delivery of medical services through a medical assistive environment and facilitates the independent living of patients. In this chapter, we discuss (1) data collection, fusion, ownership and privacy issues; (2) models, technologies and solutions for medical data processing and analysis; (3) big medical data analytics for remote health monitoring; (4) research challenges and opportunities in medical data analytics; (5) examples of case studies and practical solutions

Pattern recognition systems design on parallel GPU architectures for breast lesions characterisation employing multimodality images

This thesis was submitted for the degree of Doctor of Philosophy and awarded by Brunel University London.The aim of this research was to address the computational complexity in designing multimodality Computer-Aided Diagnosis (CAD) systems for characterising breast lesions, by harnessing the general purpose computational potential of consumer-level Graphics Processing Units (GPUs) through parallel programming methods. The complexity in designing such systems lies on the increased dimensionality of the problem, due to the multiple imaging modalities involved, on the inherent complexity of optimal design methods for securing high precision, and on assessing the performance of the design prior to deployment in a clinical environment, employing unbiased system evaluation methods. For the purposes of this research, a Pattern Recognition (PR)-system was designed to provide highest possible precision by programming in parallel the multiprocessors of the NVIDIA’s GPU-cards, GeForce 8800GT or 580GTX, and using the CUDA programming framework and C++. The PR-system was built around the Probabilistic Neural Network classifier and its performance was evaluated by a re-substitution method, for estimating the system’s highest accuracy, and by the external cross validation method, for assessing the PR-system’s unbiased accuracy to new, “unseen” by the system, data. Data comprised images of patients with histologically verified (benign or malignant) breast lesions, who underwent both ultrasound (US) and digital mammography (DM). Lesions were outlined on the images by an experienced radiologist, and textural features were calculated. Regarding breast lesion classification, the accuracies for discriminating malignant from benign lesions were, 85.5% using US-features alone, 82.3% employing DM-features alone, and 93.5% combining US and DM features. Mean accuracy to new “unseen” data for the combined US and DM features was 81%. Those classification accuracies were about 10% higher than accuracies achieved on a single CPU, using sequential programming methods, and 150-fold faster. In addition, benign lesions were found smoother, more homogeneous, and containing larger structures. Additionally, the PR-system design was adapted for tackling other medical problems, as a proof of its generalisation. These included classification of rare brain tumours, (achieving 78.6% for overall accuracy (OA) and 73.8% for estimated generalisation accuracy (GA), and accelerating system design 267 times), discrimination of patients with micro-ischemic and multiple sclerosis lesions (90.2% OA and 80% GA with 32-fold design acceleration), classification of normal and pathological knee cartilages (93.2% OA and 89% GA with 257-fold design acceleration), and separation of low from high grade laryngeal cancer cases (93.2% OA and 89% GA, with 130-fold design acceleration). The proposed PR-system improves breast-lesion discrimination accuracy, it may be redesigned on site when new verified data are incorporated in its depository, and it may serve as a second opinion tool in a clinical environment

CUDA accelerated cone‐beam reconstruction

Cone-Beam Computed Tomography (CBCT) is an imaging method that reconstructs a 3D representation of the object from its 2D X-ray images. It is an important diagnostic tool in the medical field, especially dentistry. However, most 3D reconstruction algorithms are computationally intensive and time consuming; this limitation constrains the use of CBCT. In recent years, high-end graphics cards, such as the ones powered by NVIDIA graphics processing units (GPUs), are able to perform general purpose computation. Due to the highly parallel nature of the 3D reconstruction algorithms, it is possible to implement these algorithms on the GPU to reduce the processing time to the level that is practical. Two of the most popular 3D Cone-Beam reconstruction algorithms are the Feldkamp-Davis-Kress algorithm (FDK) and the Algebraic Reconstruction Technique (ART). FDK is fast to construct 3D images, but the quality of its images is lower than the quality of ART images. However, ART requires significantly more computation. Material ART is a recently developed algorithm that uses beam-hardening correction to eliminate artifacts. In this thesis, these three algorithms were implemented on the NVIDIA\u27s CUDA platform. These CUDA based algorithms were tested on three different graphics cards, using phantom and real data. The test results show significant speedup when compared to the CPU software implementation. The speedup is sufficient to allow a moderate cost personal computer with NVIDIA graphics card to process CBCT images in real-time

GPU acceleration of a model-based iterative method for Digital Breast Tomosynthesis

Digital Breast Tomosynthesis (DBT) is a modern 3D Computed Tomography X-ray technique for the early detection of breast tumors, which is receiving growing interest in the medical and scientific community. Since DBT performs incomplete sampling of data, the image reconstruction approaches based on iterative methods are preferable to the classical analytic techniques, such as the Filtered Back Projection algorithm, providing fewer artifacts. In this work, we consider a Model-Based Iterative Reconstruction (MBIR) method well suited to describe the DBT data acquisition process and to include prior information on the reconstructed image. We propose a gradient-based solver named Scaled Gradient Projection (SGP) for the solution of the constrained optimization problem arising in the considered MBIR method. Even if the SGP algorithm exhibits fast convergence, the time required on a serial computer for the reconstruction of a real DBT data set is too long for the clinical needs. In this paper we propose a parallel SGP version designed to perform the most expensive computations of each iteration on Graphics Processing Unit (GPU). We apply the proposed parallel approach on three different GPU boards, with computational performance comparable with that of the boards usually installed in commercial DBT systems. The numerical results show that the proposed GPU-based MBIR method provides accurate reconstructions in a time suitable for clinical trials

FASTER: Facilitating Analysis and Synthesis Technologies for Effective Reconfiguration

The FASTER (Facilitating Analysis and Synthesis Technologies for Effective Reconfiguration) EU FP7 project, aims to ease the design and implementation of dynamically changing hardware systems. Our motivation stems from the promise reconfigurable systems hold for achieving high performance and extending product functionality and lifetime via the addition of new features that operate at hardware speed. However, designing a changing hardware system is both challenging and time-consuming. FASTER facilitates the use of reconfigurable technology by providing a complete methodology enabling designers to easily specify, analyze, implement and verify applications on platforms with general-purpose processors and acceleration modules implemented in the latest reconfigurable technology. Our tool-chain supports both coarse- and fine-grain FPGA reconfiguration, while during execution a flexible run-time system manages the reconfigurable resources. We target three applications from different domains. We explore the way each application benefits from reconfiguration, and then we asses them and the FASTER tools, in terms of performance, area consumption and accuracy of analysis

Electrical and Computer Engineering Annual Report 2018

Welcome New President and Dean Faculty Directory Faculty Highlights Exciting Challenges for Expert Teacher Mobility Research: Sumit Paudyal Mobility Research: Chee-Wooi Ten Mobility Research: Bruce Mork Faculty Profile and Department Award Faculty Publications Staff Profile and Directory Graduate Student Awards and Degrees Student Highlights Senior Design Enterprise Enterprise and Undergraduate Student Awards ECE Academy Class of 2018 External Advisory Committee Contracts and Grants Department Statisticshttps://digitalcommons.mtu.edu/ece-annualreports/1000/thumbnail.jp

Interactive 3D Digital Models for Anatomy and Medical Education

This chapter explores the creation and use of interactive, three-dimensional (3D), digital models for anatomy and medical education. Firstly, it looks back over the history and development of virtual 3D anatomy resources before outlining some of the current means of their creation; including photogrammetry, CT and surface scanning, and digital modelling, outlining advantages and disadvantages for each. Various means of distribution are explored, including; virtual learning environments, websites, interactive PDF’s, virtual and augmented reality, bespoke applications, and 3D printing, with a particular focus on the level of interactivity each method offers. Finally, and perhaps most importantly, the use of such models for education is discussed. Questions addressed include; How can such models best be used to enhance student learning? How can they be used in the classroom? How can they be used for selfdirected study? As well as exploring if they could one day replace human specimens, and how they complement the rise of online and e-learning

Histopathological image analysis : a review

Over the past decade, dramatic increases in computational power and improvement in image analysis algorithms have allowed the development of powerful computer-assisted analytical approaches to radiological data. With the recent advent of whole slide digital scanners, tissue histopathology slides can now be digitized and stored in digital image form. Consequently, digitized tissue histopathology has now become amenable to the application of computerized image analysis and machine learning techniques. Analogous to the role of computer-assisted diagnosis (CAD) algorithms in medical imaging to complement the opinion of a radiologist, CAD algorithms have begun to be developed for disease detection, diagnosis, and prognosis prediction to complement the opinion of the pathologist. In this paper, we review the recent state of the art CAD technology for digitized histopathology. This paper also briefly describes the development and application of novel image analysis technology for a few specific histopathology related problems being pursued in the United States and Europe