Bioimaging and Biomedical Optics Commons^™

Heterogeneous Modeling Of Medical Image Data Using B-Spline Functions, Olya Grove

USF Tampa Graduate Theses and Dissertations

Ongoing developments in the field of medical imaging modalities have pushed the frontiers of modern medicine and biomedical engineering, prompting the need for new applications to improve diagnosis, treatment and prevention of diseases.

Biomedical data visualization and modeling rely predominately on manual processing and utilization of voxel and facet based homogeneous models. Biological structures are naturally heterogeneous and in order to accurately design and biomimic biological structures, properties such as chemical composition, size and shape of biological constituents need to be incorporated in the computational biological models.

Our proposed approach involves generating a density point cloud based on the intensity …

Biological Effective Dose (Bed) Distribution Matching For Obtaining Brachytherapy Prescription Doses & Dosimetric Optimization For Hybrid Seed Brachytherapy, Jakub Pritz

USF Tampa Graduate Theses and Dissertations

Radioactive seed implant brachytherapy is a common radiotherapy treatment method for prostate cancer. In current clinical practice, a seed consists of a single isotope, such as ¹²⁵I or ¹⁰³Pd. A seed containing a mixture of two isotopes has been proposed for prostate cancer treatment. This study investigates a method for defining a prescription dose for new seed compositions based on matching the biological equivalent dose (BED) of a reference plan.

Ten prostate cancer cases previously treated using single isotope seeds (5 using ¹²⁵I seeds and 5 using ¹⁰³Pd seeds) were selected for this study. Verification of …

Bcc Skin Cancer Diagnosis Based On Texture Analysis Techniques, Shao-Hui Chuang, Xiaoyan Sun, Wen-Yu Chang, Gwo-Shing Chen, Adam Huang, Jiang Li, Frederic D. Mckenzie

Electrical & Computer Engineering Faculty Publications

In this paper, we present a texture analysis based method for diagnosing the Basal Cell Carcinoma (BCC) skin cancer using optical images taken from the suspicious skin regions. We first extracted the Run Length Matrix and Haralick texture features from the images and used a feature selection algorithm to identify the most effective feature set for the diagnosis. We then utilized a Multi-Layer Perceptron (MLP) classifier to classify the images to BCC or normal cases. Experiments showed that detecting BCC cancer based on optical images is feasible. The best sensitivity and specificity we achieved on our data set were 94% …

Prediction Of Brain Tumor Progression Using Multiple Histogram Matched Mri Scans, Debrup Banerjee, Loc Tran, Jiang Li, Yuzhong Shen, Frederic Mckenzie, Jihong Wang, Ronald M. Summers (Ed.), Bram Van Ginneken (Ed.)

Electrical & Computer Engineering Faculty Publications

In a recent study [1], we investigated the feasibility of predicting brain tumor progression based on multiple MRI series and we tested our methods on seven patients' MRI images scanned at three consecutive visits A, B and C. Experimental results showed that it is feasible to predict tumor progression from visit A to visit C using a model trained by the information from visit A to visit B. However, the trained model failed when we tried to predict tumor progression from visit B to visit C, though it is clinically more important. Upon a closer look at the MRI scans …

Experimental Studies On The Plasma Bullet Propagation And Its Inhibition, Erdinc Karakas, Mounir Laroussi

Electrical & Computer Engineering Faculty Publications

Plasma bullets generated by atmospheric pressure low temperature plasma jets have recently been an active research topic due to their unique properties and their enhanced plasma chemistry. In this paper, experimental insights into the plasma bullet lifetime and its velocity are reported. Data obtained from intensified charge-coupled device camera and time-resolved optical emission spectroscopy (OES) elucidated the existence of a weakly ionized channel between the plasma bullet and its source (such as the plasma pencil). Factors responsible for the inhibition of the propagation of the bullet, such as low helium mole fraction, the magnitude of the applied voltage, and the …

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 …

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) …

Cephalometric Analysis Using Magnetic Resonance Imaging, Wade A. Williams

Loma Linda University Electronic Theses, Dissertations & Projects

Purpose: The purpose of this study was to determine if T1-weighted 3D magnetic resonance images performed at 3.0 Tesla could be used to conduct routine orthodontic cephalometric analysis.

Methods: Three cranial imaging methods were compared using sixteen non-growing subjects (10 male and 6 female) who were not claustrophobic, did not have any metallic restorations on the distal of first molars, had no medical implants and the female subjects were not pregnant. Sagittal cranial images were produced by three methods: (1) a digital lateral cephalometric (LC) radiograph (Orthophos XG Plus, Sirona^TM, Bensheim, Germany), (2) a 12-inch FOV cone beam …

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 …

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 …

Quantitative Object Reconstruction Using Abel Transform Tomography And Mixed Variable Optimization, Kevin R. O'Reilly

Theses and Dissertations

Researchers at the Los Alamos National Laboratory (LANL) are interested in quantitatively reconstructing an object using Abel transform x-ray tomography. Specifically, they obtain a radiograph by x-raying an object and attempt to quantitatively determine the number and types of materials and the thicknesses of each material layer. Their current methodologies either fail to provide a quantitative description of the object or are generally too slow to be useful in practice. As an alternative, the problem is modeled here as a mixed variable programming (MVP) problem, in which some variables are nonnumeric and for which no derivative information is available. The …

Compact Supercell Method Based On Opposite Parity For Bragg Fibers, Wang Zhi, Ren Guobin, Lou Shuquin, Liang Weijun, Shangping Guo

Electrical & Computer Engineering Faculty Publications

The supercell- based orthonormal basis method is proposed to investigate the modal properties of the Bragg fibers. A square lattice is constructed by the whole Bragg fiber which is considered a supercell, and the periodical dielectric structure of the square lattice is decomposed using periodic functions (cosine). The modal electric field is expanded as the sum of the orthonormal set of Hermite-Gaussian basis functions based on the opposite parity of the transverse electric field. The propagation characteristics of Bragg fibers can be obtained after recasting the wave equation into an eigenvalue system. This method is implemented with very high efficiency …

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 …

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 …

Computer-Aided Diagnosis Of Mammographic Masses, William E. Polakowski

Theses and Dissertations

A new Model-Based Vision algorithm was developed to find possibly cancerous regions of interest (ROIs) in digitized mammograms and to correctly identify the malignant masses. This work has shown a sensitivity of 92 percent for locating malignant ROIs. The database contained 272 images (12 bit, 1OO microns) with 36 malignant and 53 benign mass images. Of the 53 biopsied benign cases, 74 percent were correctly classified. The Focus of Attention (segmentation) Module algorithm used a physiologically motivated Difference of Gaussians (DoG) filter to highlight mass-like regions in the mammogram. The Index Module labeled the regions by their hypothesized class: large …

Soft X-Rays, William F. Roherty

Bachelors’ Theses

Traditionally, radiation of those wavelengths produced by a potential of from 2 to 25kv. has been classified as "Grenz" or "soft" x-rays. The peculiar property of this radiation is its low penetrating power. While being transparent to more energetic radiation, animal tissue and similar materials are sufficiently opaque to Grenz rays as to be studied in detail. Since the radiation is of the continuous type, it can be made soft enough to reveal any object, providing, of course, that the tube window and object medium be not opaque. Soft x-rays are produced in exactly the same manner as others, except …