Biomedical Commons^™

Capacity, Allocation And Update Dynamics Of Human Memory Systems, Shaoying Wang

Electronic Theses and Dissertations

Information is encoded and stored in three types of memory: sensory memory (SM), short-term memory (STM), and long-term memory (LTM). SM has a large capacity but retains information for only a brief period. When information transfers to STM, only a limited amount can be stored. Information in STM can then be transferred to LTM, which has a much larger capacity and longer retention time. STM is often conceptualized as working memory (WM) to highlight its role in active information processing. Due to the limited capacity of STM, it is commonly believed that STM serves as the bottleneck for information processing. …

A Deep Learning Approach For Semantic Segmentation And Its Application On Ctc., Samir Farag Harb

Electronic Theses and Dissertations

This dissertation explores the modeling and analysis of medical images, focusing on the intricate task of colon segmentation and subsequent 3D reconstruction, which are critical steps in Computed Tomography Colonography (CTC) systems. The primary objective of this research is to develop precise segmentation approaches to enhance the accuracy of colon identification and reconstruction from abdominal CT scans. Three distinct segmentation approaches are proposed and evaluated: a Markov Random Field (MRF)-based approach, a convolutional neural network (CNN)-based deep learning (DL) approach, and a sequential episodic training with dual contrastive learning Approach (G-SET-DCL) that has a flavor of few-shot learning (FSL). To …

An Automatic Colorectal Polyps Detection Approach For Ct Colonography., Mohamed Yousuf

Electronic Theses and Dissertations

Colon cancer, also known as colorectal cancer, is a significant health concern, with increasing incidence rates, particularly among individuals under 50. This rise has led experts to recommend the introduction of regular screenings at 45 years of age for adults at average risk. Early detection through such screenings can identify precancerous polyps, allowing their removal before they develop into cancer. This proactive approach has the potential to reduce colorectal cancer deaths by up to 60%. In addition, research indicates that people diagnosed before age 50 have better survival rates, which emphasizes the importance of early diagnosis. Therefore, adhering to recommended …

Denoising And Super-Resolution Of In-Vitro 4e Flow Mri In A Stenotic Phantom Model Using Physics-Informed Neural Networks., Shrouk M. Wally

Electronic Theses and Dissertations

In recent years, the use of 4D flow MRI has revolutionized cardiovascular imag- ing by providing comprehensive data on blood flow dynamics over time. However, the limited spatial and temporal resolution of this imaging modality can hinder the accurate assessment of complex hemodynamic phenomena. This thesis explores the application of Physics-Informed Neural Networks (PINNs) to enhance the resolution of 4D flow MRI data, thereby improving its clinical utility. PINNs are a class of neural networks that integrate physical laws into their training process. By embedding these physics equations, PINNs can discover the underlying physics of fluid dynamics to produce more …

Novel Sensors, Algorithms And Metrics For Human-Robot Interaction., Henry Lee Reynolds

Electronic Theses and Dissertations

The increased presence and deployment of robotics in sectors such as the medical field results in the demand for robots to, directly and indirectly, interface with people and their environment, making human-robot interaction (HRI) a vital thrust of robotics research. Assistive robots, for example, aid humans in accomplishing tasks or by providing support in the workforce. As the demand for nurses and the aging population increases, the assistive robots deployed will be deeply rooted in environments that require constant interaction with humans. This work contributed to improving aspects of HRI through 1) Expanding accessibility of the methods used for interfacing …

Adaptive Personalized Drug Delivery Method For Warfarin And Anemia Management: Modeling And Control., Affan Affan

Electronic Theses and Dissertations

Personalized precision medicine aims to develop the appropriate treatments for suitable patients at the right time to obtain optimal results. Personalized medicine is challenging due to inter- and intra-patient variability, narrow therapeutic window, the effect of other medications, comorbidity (more than one disease at a time), nonlinear patient dynamics, and time-varying patient dose response characteristics which include bleeding (internal and external). This research aims to develop a framework for an adaptive personalized modeling and control method with minimum clinical patient specific dose response data for optimal drug dosing. The proposed methodology is applied to anemia and warfarin management. It is …

Novel Approach For Non-Invasive Prediction Of Body Shape And Habitus, Emma Young

Electronic Theses and Dissertations

While marker-based motion capture remains the gold standard in measuring human movement, accuracy is influenced by soft-tissue artifacts, particularly for subjects with high body mass index (BMI) where markers are not placed close to the underlying bone. Obesity influences joint loads and motion patterns, and BMI may not be sufficient to capture the distribution of a subject’s weight or to differentiate differences between subjects. Subjects in need of a joint replacement are more likely to have mobility issues or pain, which prevents exercise. Obesity also increases the likelihood of needing a total joint replacement. Accurate movement data for subjects with …

Computer Aided Diagnosis System For Breast Cancer Using Deep Learning., Asma Baccouche

Electronic Theses and Dissertations

The recent rise of big data technology surrounding the electronic systems and developed toolkits gave birth to new promises for Artificial Intelligence (AI). With the continuous use of data-centric systems and machines in our lives, such as social media, surveys, emails, reports, etc., there is no doubt that data has gained the center of attention by scientists and motivated them to provide more decision-making and operational support systems across multiple domains. With the recent breakthroughs in artificial intelligence, the use of machine learning and deep learning models have achieved remarkable advances in computer vision, ecommerce, cybersecurity, and healthcare. Particularly, numerous …

Mechanisms Of Sensory Adaptation In The Primate Visual System, Boris Isaac Peñaloza Rojas

Electronic Theses and Dissertations

Under ecological conditions, the luminance impinging on the retina varies within a dynamic range of 220 dB. Stimulus contrast can also vary drastically within a scene, and eye movements leave little time for sampling luminance. In addition, the amount of information reaching our visual system far exceeds the brain’s information processing capacity. Given the limited dynamic range of its neurons and its limited capacity in processing visual information in real-time, the brain deploys both structural and functional solutions that work in tandem to adapt to the surroundings. In this work, employing visual psychophysics and computational neuroscience, we study the mechanisms …

Fly-In Visualization Of Tubular Objects: Theory And Application In Virtual Colonoscopy., Mostafa Mohamed

Electronic Theses and Dissertations

In this dissertation, visualization for tubular objects, i.e., projecting 2D images from 3D inner surfaces of tubular objects, is investigated. Given surface points on 3D objects, an approach that most accurately and effectively projects 2D images from the 3D surface with minimal loss of information is desired. A new visualization method for tubular surfaces is proposed, denoted by "Fly-In". The approach uses a virtual camera ring that moves along the inner surface's centerline, obtaining projections of the surrounding views, forming small 3D topological rings within the tube rendered as a 2D rectangular image. A new visualization loss measure is also …

Developing Machine Learning Algorithms For Behavior Recognition From Deep Brain Signals, Hosein Golshan Mojdehi

Electronic Theses and Dissertations

Parkinson’s disease (PD) is a neurodegenerative condition and movement disorder that appears with symptoms such as tremor, rigidity of muscles and slowness of movements. Deep brain stimulation (DBS) is an FDA-approved surgical therapy for essential tremor and PD. Despite the fact that DBS substantially alleviates the motor signs of PD, it can cause cognitive side effects and speech malfunction mainly due to the lack of adaptivity and optimality of the stimulation signal to the patients’ current state. A behavior-adapted closed-loop DBS system may reduce the side effects and power consumption by adjusting the stimulation parameters to patients’ need.

Behavior recognition …

Automatic Signal And Image-Based Assessments Of Spinal Cord Injury And Treatments., Samineh Mesbah

Electronic Theses and Dissertations

Spinal cord injury (SCI) is one of the most common sources of motor disabilities in humans that often deeply impact the quality of life in individuals with severe and chronic SCI. In this dissertation, we have developed advanced engineering tools to address three distinct problems that researchers, clinicians and patients are facing in SCI research. Particularly, we have proposed a fully automated stochastic framework to quantify the effects of SCI on muscle size and adipose tissue distribution in skeletal muscles by volumetric segmentation of 3-D MRI scans in individuals with chronic SCI as well as non-disabled individuals. We also developed …

Modeling And Simulation Methodologies For Spinal Cord Stimulation., Saliya Kumara Kirigeeganage

Electronic Theses and Dissertations

The use of neural prostheses to improve health of paraplegics has been a prime interest of neuroscientists over the last few decades. Scientists have performed experiments with spinal cord stimulation (SCS) to enable voluntary motor function of paralyzed patients. However, the experimentation on the human spinal cord is not a trivial task. Therefore, modeling and simulation techniques play a significant role in understanding the underlying concepts and mechanics of the spinal cord stimulation. In this work, simulation and modeling techniques related to spinal cord stimulation were investigated. The initial work was intended to visualize the electric field distribution patterns in …

Segmentation, Tracking, And Kinematics Of Lung Parenchyma And Lung Tumors From 4d Ct With Application To Radiation Treatment Planning., Jungwon Cha

Electronic Theses and Dissertations

This thesis is concerned with development of techniques for efficient computerized analysis of 4-D CT data. The goal is to have a highly automated approach to segmentation of the lung boundary and lung nodules inside the lung. The determination of exact lung tumor location over space and time by image segmentation is an essential step to track thoracic malignancies. Accurate image segmentation helps clinical experts examine the anatomy and structure and determine the disease progress. Since 4-D CT provides structural and anatomical information during tidal breathing, we use the same data to also measure mechanical properties related to deformation of …

Longitudinal Tracking Of Physiological State With Electromyographic Signals., Robert Warren Stallard

Electronic Theses and Dissertations

Electrophysiological measurements have been used in recent history to classify instantaneous physiological configurations, e.g., hand gestures. This work investigates the feasibility of working with changes in physiological configurations over time (i.e., longitudinally) using a variety of algorithms from the machine learning domain. We demonstrate a high degree of classification accuracy for a binary classification problem derived from electromyography measurements before and after a 35-day bedrest. The problem difficulty is increased with a more dynamic experiment testing for changes in astronaut sensorimotor performance by taking electromyography and force plate measurements before, during, and after a jump from a small platform. A …

Phase Unwrapping Of 4d-Flow Mri Data With Graph Cuts., Andrew Justice

Electronic Theses and Dissertations

A common issue when measuring velocity utilizing 4D flow magnetic resonance imaging (MRI) is aliasing that occurs because of a low velocity encoding parameter (VENC). Aliasing can be avoided if the velocity encoding parameter is set above the largest velocity quantity. However, when this is done the velocity to noise ratio is lowered less detail is acquired in the image. Thusly, it is sometimes desirable to have a below the maximum velocity to acquire higher quality data.

Consequently, an efficient and robust algorithm is needed to unwrap the aliased data. This paper proposes an iterative graph cuts algorithm to perform …

Adaptive Interventions Treatment Modelling And Regimen Optimization Using Sequential Multiple Assignment Randomized Trials (Smart) And Q-Learning, Abiral Baniya

Electronic Theses and Dissertations

Nowadays, pharmacological practices are focused on a single best treatment to treat a disease which sounds impractical as the same treatment may not work the same way for every patient. Thus, there is a need of shift towards more patient-centric rather than disease-centric approach, in which personal characteristics of a patient or biomarkers are used to determine the tailored optimal treatment. The “one size fits all” concept is contradicted by research area of personalized medicine. The Sequential Multiple Assignment Randomized Trial (SMART) is a multi-stage trials to inform the development of dynamic treatment regimens (DTR’s). In SMART, a subject is …

Chronic Risk And Disease Management Model Using Structured Query Language And Predictive Analysis, Mamata Ojha

Electronic Theses and Dissertations

Individuals with chronic conditions are the ones who use health care most frequently and more than 50% of top ten causes of death are chronic diseases in United States and these members always have health high risk scores. In the field of population health management, identifying high risk members is very important in terms of patient health care, disease management and cost management. Disease management program is very effective way of monitoring and preventing chronic disease and health related complications and risk management allows physicians and healthcare companies to reduce patient’s health risk, help identifying members for care/disease management along …

A Scale Space Local Binary Pattern (Sslbp) – Based Feature Extraction Framework To Detect Bones From Knee Mri Scans, Jinyeong Mun

Electronic Theses and Dissertations

The medical industry is currently working on a fully autonomous surgical system, which is considered a novel modality to go beyond technical limitations of conventional surgery. In order to apply an autonomous surgical system to knees, one of the primarily responsible areas for supporting the total weight of human body, accurate segmentation of bones from knee Magnetic Resonance Imaging (MRI) scans plays a crucial role. In this paper, we propose employing the Scale Space Local Binary Pattern (SSLBP) feature extraction, a variant of local binary pattern extractions, for detecting bones from knee images. The proposed methods consist of two phases. …

Enhanced Breast Cancer Classification With Automatic Thresholding Using Support Vector Machine And Harris Corner Detection, Mohammad Taheri

Electronic Theses and Dissertations

Image classification and extracting the characteristics of a tumor are the powerful tools in medical science. In case of breast cancer medical treatment, the breast cancer classification methods can be used to classify input images as benign and malignant classes for better diagnoses and earlier detection with breast tumors. However, classification process can be challenging because of the existence of noise in the images, and complicated structures of the image. Manual classification of the images is timeconsuming, and need to be done only by medical experts. Hence using an automated medical image classification tool is useful and necessary. In addition, …

Simultaneous Behavior Onset Detection And Task Classification For Patients With Parkinson Disease Using Subthalamic Nucleus Local Field Potentials, Nazanin Zaker Habibabadi

Electronic Theses and Dissertations

This thesis aims to develop of methods for behavior onset detection of patients with Parkinson's disease (PD), as well as to investigate the models for classification of different behavioral tasks performed by PD patient. The detection is based on recorded Local Field Potentials (LFP) of the Subthalamic nucleus (STN), captured through Deep Brain Stimulation (DBS) process.

One main part of this work is dedicated to the research of various properties and features of the STN LFP signals of several patients' behavior conditions. Features based on temporal and time-frequency analysis of the signals are developed and implemented. Evaluation and comparison of …

Sickle Blood Cell Detection Based On Image Segmentation, Kholoud Alotaibi

Electronic Theses and Dissertations

Red blood cells have a vital role in human health. Red blood cells have a circular shape and a concave surface and exchange the gasses between the inside and outside of the body. However, at times, these normally round cells become sickle shaped, which is an indication of sickle cell disease. This paper introduces a unique approach to detect sickle blood cells in blood samples using image segmentation and shape detection. This method is based on calculating the max axis and min axis of the cell. The form factor is computed using these properties to determine whether the cell is …

Breast Cancer Classification Of Mammographic Masses Using Circularity Max Metric, A New Method, Tae Keun Heo

Electronic Theses and Dissertations

Breast cancer classification can be divided into two categories. The first category is a benign tumor, and the other is a malignant tumor. The main purpose of breast cancer classification is to classify abnormalities into benign or malignant classes and thus help physicians with further analysis by minimizing potential errors that can be made by fatigued or inexperienced physicians. This paper proposes a new shape metric based on the area ratio of a circle to classify mammographic images into benign and malignant class. Support Vector Machine is used as a machine learning tool for training and classification purposes. The improved …

Electroencephalogram Based Causality Graph Analysis In Behavior Tasks Of Parkinson’S Disease Patients, Abdulaziz Saleh Almalaq

Electronic Theses and Dissertations

Electroencephalographic (EEG) signals of the human brains represent electrical activities for a number of channels recorded over a the scalp. The main purpose of this thesis is to investigate the interactions and causality of different parts of a brain using EEG signals recorded during a performance subjects of verbal fluency tasks. Subjects who have Parkinson's Disease (PD) have difficulties with mental tasks, such as switching between one behavior task and another. The behavior tasks include phonemic fluency, semantic fluency, category semantic fluency and reading fluency. This method uses verbal generation skills, activating different Broca's areas of the Brodmann's areas (BA44 …

Spontaneous Facial Behavior Computing In Human Machine Interaction With Applications In Autism Treatment, Seyedmohammad Mavadati

Electronic Theses and Dissertations

Digital devices and computing machines such as computers, hand-held devices and robots are becoming an important part of our daily life. To have affect-aware intelligent Human-Machine Interaction (HMI) systems, scientists and engineers have aimed to design interfaces which can emulate face-to-face communication. Such HMI systems are capable of detecting and responding upon users' emotions and affective states. One of the main challenges for producing such intelligent system is to design a machine, which can automatically compute spontaneous behaviors of humans in real-life settings. Since humans' facial behaviors contain important non-verbal cues, this dissertation studies facial actions and behaviors in HMI …

Shape/Image Registration For Medical Imaging : Novel Algorithms And Applications., Ahmed Magdy Shalaby 1982-

Electronic Theses and Dissertations

This dissertation looks at two different categories of the registration approaches: Shape registration, and Image registration. It also considers the applications of these approaches into the medical imaging field. Shape registration is an important problem in computer vision, computer graphics and medical imaging. It has been handled in different manners in many applications like shapebased segmentation, shape recognition, and tracking. Image registration is the process of overlaying two or more images of the same scene taken at different times, from different viewpoints, and/or by different sensors. Many image processing applications like remote sensing, fusion of medical images, and computer-aided surgery …

Hybrid Sensing And Adaptive Control For Direct Brain Actuation Of Artificial Limbs, Christopher Aasted

Electronic Theses and Dissertations

Developing a non-invasive direct brain control of artificial limbs is both challenging and desirable. Such a sensory and control system, if successful, will have a profound impact on the disabled. In this dissertation, we present the design and development of a non-invasive, hybrid sensory system, which uses near-infrared spectroscopy (NIRS) and electroencephalography (EEG) to measure brain activity with simultaneous electromyography (EMG) to provide feedback data in a healthy limb. Through the combination of these sensory techniques, we have successfully trained a control system capable of mapping brain activity onto muscle actuation. The design of a control algorithm capable of automatic …

Lifetime Estimation Of Wireless Body Area Sensor Network For Patient Health Monitoring, Frank Agyei-Ntim

Electronic Theses and Dissertations

Wireless Body Area Sensor Networks (WBASN) is an emerging technology which utilizes wireless sensors to implement real-time wearable health monitoring of patients to enhance independent living. These sensors can be worn externally to monitor multiple bio-parameters (such as blood oxygen saturation (SpO2), blood pressure and heart activity) of multiple patients at a central location in the hospital.

In health monitoring, the loss of critical or emergency information is a serious issue so there is a concern for quality of service which needs to be addressed. It is important to have an estimate of the time the first node will fail …

Mems Transducer For Hearing Aid Device, Houwen Tang

Electronic Theses and Dissertations

A study of implantable middle ear MEMS piezoelectric transducer for hearing aids was developed. The physical vibration deflection at the tip of the transducer is simulated using finite element method. Further, a study of thermoelastic damping effect of four designed MEMS mirrors used as resonators is presented. An optical measurement system based on Mach-Zehnder interferometer is