Biomedical Engineering and Bioengineering Commons^™

St. Jude Medical: An Object-Oriented Software Architecture For Embedded And Real-Time Medical Devices, Atila Amiri

Master's Theses

Medical devices used for surgical or therapeutic purposes require a high degree of safety and effectiveness. Software is critical component of many such medical devices. The software architecture of a system defines organizational structure and the runtime characteristic of the application used to control the operation of the system and provides a set of frameworks that are used to develop that. As such, the design of software architecture is a critical element in achieving the intended functionality, performance, and safety requirements of a medical device. This architecture uses object-oriented design techniques, which model the underlying system as a set of …

The Gel Documentation System: A Cornerstone To The Implementation Of The Introduction To Biotechnology And Introduction To Bioinformatics Cross-Disciplinary Course Series, Marcy Kelly, Gregory Lampard, Constance Knapp

Cornerstone 3 Reports : Interdisciplinary Informatics

No abstract provided.

Monte Carlo Model Of Light Propagation In Tissues And The Effects Of Phase Changes On The Light Intensity, Rakesh Choula

Electrical & Computer Engineering Theses & Dissertations

Lasers, due to their unique properties, have a wide range of applications in the medical field. For accurate laser treatments that focus on bio-tissues, prior knowledge of the amount of laser power, spot size and its irradiation time are necessary. In order to predict the effects of lasers on tissues and their bio-effects, a first necessary step is the creation of a model that can predict the temperature distributions within the tissue following laser excitation. This involves modeling light propagation through the tissue with inclusion of internal scattering, and assessment of the energy deposited by the incoming photons. The next …

Brain Tumor Progression Assessment Using Multiple Mri Volumes, Yufei Shen

Electrical & Computer Engineering Theses & Dissertations

Predicting and assessing tumor progression is important in brain tumor treatment. We attempt to use machine learning techniques to achieve consistency in assessing brain tumor progression. This thesis presents a prediction method of brain tumor progression by exploring a large MR database, which contains two patients ' complete records covering all their visits in the past two years. All ten MRI series, namely, apparent diffusion coefficient (ADC) , diffusion tensor imaging (DTI) , fractional anisotropy (FA), fluid attenuated inversion recovery (FLAIR), max eigenvalue (MAX), mid eigenvalue (MID), min eigenvalue (MIN) , post-contrast T₁-weighted, T₁- weighted, and …

Analysis Of Electroencephalogram Signals For The Identification Of Mental Tasks, My Thy Thi Tran

Electrical & Computer Engineering Theses & Dissertations

Electroencephalogram (EEG) signals can be used for implicit communication such as to control robots or medical equipment by brain activity or to detect an individual's intentions of committing premeditated crimes. An EEG based brain-computer interface allows paralyzed patients to express their thoughts. However, biological and technical artifacts heavily interfered with EEG signals due to blinking of the eyes, muscle activities and line noise. Sometimes the noise interference due to signal artifacts becomes more prominent than the information content. This thesis investigates novel feature extraction methodologies in EEG signals to represent different thought processes and employs neural network-based pattern classification techniques …

A Mobile Ecg Monitoring System With Context Collection, Jin Peng Li, Damon Berry, Richard Hayes

Conference Papers

Preventative health management represents a shift from the traditional approach of reactive treatment-based healthcare towards a proactive wellness-management approach where patients are encouraged to stay healthy with expert support when they need it, at any location and any time. This work represents a step along the road towards proactive, preventative healthcare for cardiac patients. It seeks to develop a smart mobile ECG monitoring system that requests and records context information about what is happening around the subject when an arrhythmia event occurs. Context information about the subject’s activities of daily living will, it is hoped, provide an enriched data set …

Parameter Optimization For Image Denoising Based On Block Matching And 3d Collaborative Filtering, Ramu Pedada, Emin Kugu, Jiang Li, Zhanfeng Yue, Yuzhong Shen, Josien P.W. Pluim (Ed.), Benoit M. Dawant (Ed.)

Electrical & Computer Engineering Faculty Publications

Clinical MRI images are generally corrupted by random noise during acquisition with blurred subtle structure features. Many denoising methods have been proposed to remove noise from corrupted images at the expense of distorted structure features. Therefore, there is always compromise between removing noise and preserving structure information for denoising methods. For a specific denoising method, it is crucial to tune it so that the best tradeoff can be obtained. In this paper, we define several cost functions to assess the quality of noise removal and that of structure information preserved in the denoised image. Strength Pareto Evolutionary Algorithm 2 (SPEA2) …

Medical Image Modeling And Processing, Ramu Pedada

Electrical & Computer Engineering Theses & Dissertations

During the last few decades of the twentieth century, medical imaging has been playing a prominent role in many fields of biomedical research and clinical practice. Image modalities such as x-rays, computed tomography (CT), and magnetic resonance images (MRI) have all been valuable additions to the radiologist's arsenal of imaging tools. Medical images assure quality diagnosis and patient safety by gathering valuable information without invading the human body. Apart from clinical diagnosis, medical images are used as tools for education where they are used for training individuals before operating on a patient. Many medical educators tum to simulation based training …

Real-Time Ultrasound Simulation For Medical Training And Standardized Patient Assessment, Bo Sun

Computational Modeling & Simulation Engineering Theses & Dissertations

With the increasing role played by ultrasound in clinical diagnostics, ultrasound training in medical education has become more and more important. The clinical routine for ultrasound training is on real patients; therefore monitored and guided examinations involving medical students are quite time-constrained. Furthermore, standardized patients (SPs), who are increasingly used in medical school for teaching and assessing medical students, need to be augmented. These SPs are typically healthy individuals who can not accurately portray the variety of abnormalities that are needed for training especially when medical examinations involve instrument interactions. To augment SPs in a realistically effective way and also …

Using Pareto Fronts To Evaluate Polyp Detection Algorithms For Ct Colonography, Adam Huang, Jiang Li, Ronald M. Summers, Nicholas Petrick, Amy K. Hara

Electrical & Computer Engineering Faculty Publications

We evaluate and improve an existing curvature-based region growing algorithm for colonic polyp detection for our CT colonography (CTC) computer-aided detection (CAD) system by using Pareto fronts. The performance of a polyp detection algorithm involves two conflicting objectives, minimizing both false negative (FN) and false positive (FP) detection rates. This problem does not produce a single optimal solution but a set of solutions known as a Pareto front. Any solution in a Pareto front can only outperform other solutions in one of the two competing objectives. Using evolutionary algorithms to find the Pareto fronts for multi-objective optimization problems has been …

Validating Pareto Optimal Operation Parameters Of Polyp Detection Algorithms For Ct Colonography, Jiang Li, Adam Huang, Nicholas Petrick, Jianhua Yao, Ronald M. Summers, Maryellen L. Giger (Ed.), Nico Karssemeijer (Ed.)

Electrical & Computer Engineering Faculty Publications

We evaluated a Pareto front-based multi-objective evolutionary algorithm for optimizing our CT colonography (CTC) computer-aided detection (CAD) system. The system identifies colonic polyps based on curvature and volumetric based features, where a set of thresholds for these features was optimized by the evolutionary algorithm. We utilized a two-fold cross-validation (CV) method to test if the optimized thresholds can be generalized to new data sets. We performed the CV method on 133 patients; each patient had a prone and a supine scan. There were 103 colonoscopically confirmed polyps resulting in 188 positive detections in CTC reading from either the prone or …

The Igipi Ontological Framework: Integrating Gene Interactions With Protein Interactions, Bill Andreopoulos, Aijun An, Xiangji Huang

Faculty Publications, Computer Science

No abstract provided.

Direct Least-Squares Ellipse Fitting, Jane Courtney, Annraoi Depaor

Conference Papers

Many biological and astronomical forms can be best represented by ellipses. While some more complex curves might represent the shape more accurately, ellipses have the advantage that they are easily parameterised and define the location, orientation and dimensions of the data more clearly. In this paper, we present a method of direct least-squares ellipse fitting by solving a generalised eigensystem. This is more efficient and more accurate than many alternative approaches to the ellipse-fitting problem such as fuzzy c-shells clustering and Hough transforms. This method was developed for human body modelling as part of a larger project to design a …

Modular Machine Learning Methods For Computer-Aided Diagnosis Of Breast Cancer, Mia Kathleen Markey '94

Doctoral Dissertations

The purpose of this study was to improve breast cancer diagnosis by reducing the number of benign biopsies performed. To this end, we investigated modular and ensemble systems of machine learning methods for computer-aided diagnosis (CAD) of breast cancer. A modular system partitions the input space into smaller domains, each of which is handled by a local model. An ensemble system uses multiple models for the same cases and combines the models' predictions.

Five supervised machine learning techniques (LDA, SVM, BP-ANN, CBR, CART) were trained to predict the biopsy outcome from mammographic findings (BIRADS™) and patient age based on a …

Level Set Segmentation Of Mr Images For Extraction Of Femur Bone And Tissues, Christina Shanti Nayagam

Student Works (2000-2009)

This research explores a potentially useful segmentation algorithm, known as the level set method. It is suitable for images obtained from the Magnetic Resonance Imaging (MRJ) modality, despite the fact that MR images have low contrast between bone and tissue. The level set method is a numerical technique designed to track the evolution of an interface. The fast marching version of the method is implemented for two-dimensional (2-D) and three-dimensional (3-D) segmentation in this research. Femur segmentation is the main thrust of this thesis, however brain and heart images are also presented. Pre-processing steps are first performed for the 2-0 …

Bottom-Up Design Of Artificial Neural Network For Single-Lead Electrocardiogram Beat And Rhythm Classification, Srikanth Thiagarajan

Doctoral Dissertations

Performance improvement in computerized Electrocardiogram (ECG) classification is vital to improve reliability in this life-saving technology. The non-linearly overlapping nature of the ECG classification task prevents the statistical and the syntactic procedures from reaching the maximum performance. A new approach, a neural network-based classification scheme, has been implemented in clinical ECG problems with much success. The focus, however, has been on narrow clinical problem domains and the implementations lacked engineering precision. An optimal utilization of frequency information was missing. This dissertation attempts to improve the accuracy of neural network-based single-lead (lead-II) ECG beat and rhythm classification. A bottom-up approach defined …

Evaluation Of Segmentation For Bone Structures In 3d Rendering Of Ultrasound Residual Limb Images, Min C. Baker

Theses and Dissertations

Prosthetists today widely practice manual socket fitting, which produces subjective, inconsistent results. To address this problem, the Computerized Anthropometry Research and Design (CARD) Laboratory is developing a computer-aided socket design system that acquires ultrasound datasets of an amputee's residual limb, creates a 3D model, and helps identify load- bearing and pressure-relief areas. This research project focuses on providing 3D visualization of a residual limb to support the CARD Laboratory's efforts. Creating the 3D model of the skin and two bone contours requires two major steps: segmentation to identify the objects of interest and a surface tracking algorithm to generate the …

Pulse Coupled Neural Networks For The Segmentation Of Magnetic Resonance Brain Images, Shane L. Abrahamson

Theses and Dissertations

This research develops an automated method for segmenting Magnetic Resonance (MR) brain images based on Pulse Coupled Neural Networks (PCNN). MR brain image segmentation has proven difficult, primarily due to scanning artifacts such as interscan and intrascan intensity inhomogeneities. The method developed and presented here uses a PCNN to both filter and segment MR brain images. The technique begins by preprocessing images with a PCNN filter to reduce scanning artifacts. Images are then contrast enhanced via histogram equalization. Finally, a PCNN is used to segment the images to arrive at the final result. Modifications to the original PCNN model are …

Development Of A Mammographic Image Processing Environment Using Matlab, John L. Kelley

Theses and Dissertations

Breast cancer is a disease that accounts for a disturbingly large number of deaths in females each year. Its prevalence is a topic of concern to all of us since it can affect our families, friends, and coworkers. Although mammographic screening is the most effective method currently available for the early detection of breast cancer, it is far from being an infallible procedure. Mammographic reading is error prone, partly because of the complexity of the task and partly because of the variability in human performance. Computers offer high reproducibility, and when used as an adjunct by the radiologist, may improve …

The Afit Multielectrode Array For Neural Recording And Simulation: Design, Testing, And Encapsulation, James R. Reid Jr

Theses and Dissertations

A two-dimensional, X-Y addressable, multiplexed array of 256 electrodes (16 x 16) has been fabricated using conventional semiconductor processing techniques. The individual electrodes are 16O microns x 160 microns, approximating the size of the cortical columns; the overall array size is 3910 microns x 3910 microns. The array has been fitted to a chronically implantable package and tested for several days in a simulated neural environment. EEG-like data were collected successfully from individual electrodes in the array. This array improves on a previous design of a 16 electrode (4 x 4) array that was chronically implanted on the cortex of …