Bioimaging and Biomedical Optics Commons^™

Low-Cost Compact Diffuse Speckle Contrast Flowmeter Using Small Laser Diode And Bare Charge-Coupled-Device, Chong Huang, Myeongsu Seong, Joshua Paul Morgan, Siavash Mazdeyasna, Jae Gwan Kim, Jeffrey Todd Hastings, Guoqiang Yu

Biomedical Engineering Faculty Publications

We report a low-cost compact diffuse speckle contrast flowmeter (DSCF) consisting of a small laser diode and a bare charge-coupled-device (CCD) chip, which can be used for contact measurements of blood flow variations in relatively deep tissues (up to ∼8  mm). Measurements of large flow variations by the contact DSCF probe are compared to a noncontact CCD-based diffuse speckle contrast spectroscopy and a standard contact diffuse correlation spectroscopy in tissue phantoms and a human forearm. Bland–Altman analysis shows no significant bias with good limits of agreement among these measurements: 96.5% ± 2.2% (94.4% to 100.0%) in phantom experiments and 92.8% …

Experimental Assessment Of Mouse Sociability Using An Automated Image Processing Approach, Frency Varghese, Jessica A. Burket, Andrew D. Benson, Stephen I. Deutsch, Christian W. Zemlin

Electrical & Computer Engineering Faculty Publications

Mouse is the preferred model organism for testing drugs designed to increase sociability. We present a method to quantify mouse sociability in which the test mouse is placed in a standardized apparatus and relevant behaviors are assessed in three different sessions (called session I, II, and III). The apparatus has three compartments (see Figure 1), the left and right compartments contain an inverted cup which can house a mouse (called “stimulus mouse”). In session I, the test mouse is placed in the cage and its mobility is characterized by the number of transitions made between compartments. In session II, a …

The Connectivity Domain: Analyzing Resting State Fmri Data Using Feature-Based Data-Driven And Model-Based Methods, Armin Iraji, Vince D. Calhoun, Natalie M. Wiseman, Esmaeil Davoodi-Bojd, Mohammad R. N. Avanaki, Zhifeng Kou

Biomedical Engineering Faculty Research Publications

Spontaneous fluctuations of resting state functional MRI (rsfMRI) have been widely used to understand the macro-connectome of the human brain. However, these fluctuations are not synchronized among subjects, which leads to limitations and makes utilization of first-level model-based methods challenging. Considering this limitation of rsfMRI data in the time domain, we propose to transfer the spatiotemporal information of the rsfMRI data to another domain, the connectivity domain, in which each value represents the same effect across subjects. Using a set of seed networks and a connectivity index to calculate the functional connectivity for each seed network, we transform data into …

A Software Application For Cardiac-Gated Computerized Tomography Scanning, Stephen Caldwell, Trevor Engelsman

Williams Honors College, Honors Research Projects

Computerized tomography (CT) scans are a common clinical imaging procedure used worldwide. Operating in the X-Ray spectrum, these machines rotate scanners around a stationary body in order to compile two-dimensional images into a unified three-dimensional image. With adjustment to scan frequency and intensity, internal features such as muscles, organs, and tendons can easily be viewed. However, the heart has long evaded CT use due to its near-constant motion. Recently, cardiac-gated scans have entered the market as a technique to image the heart at a specific moment in time when it is nearly still - the quiescent period. Occurring during rapid …

Characterization Of Two Vernier-Tuned Distributed Bragg Reflector (Vt-Dbr) Lasers Used In Swept Source Optical Coherence Tomography (Ss-Oct), Greg M. Bergdoll

Master's Theses

Insight Photonic Solutions Inc. has continued to develop their patented VT-DBR laser design; these wavelength tunable lasers promise marked image-quality and acquisition time improvements in SS-OCT applications.

To be well suited for SS-OCT, tunable lasers must be capable of producing a highly linear wavelength sweep across a tuning range well-matched to the medium being imaged; many different tunable lasers used in SS-OCT are compared to identify the optimal solution.

This work electrically and spectrally characterizes two completely new all-semiconductor VT-DBR designs to compare, as well. The Neptune VT-DBR, an O-band laser, operates around the 1310 nm range and is a …

Video-To-Video Pose And Expression Invariant Face Recognition Using Volumetric Directional Pattern, Vijayan K. Asari, Almabrok Essa

Electrical and Computer Engineering Faculty Publications

Face recognition in video has attracted attention as a cryptic method of human identification in surveillance systems. In this paper, we propose an end-to-end video face recognition system, addressing a difficult problem of identifying human faces in video due to the presence of large variations in facial pose and expression, and poor video resolution. The proposed descriptor, named Volumetric Directional Pattern (VDP), is an oriented and multi-scale volumetric descriptor that is able to extract and fuse the information of multi frames, temporal (dynamic) information, and multiple poses and expressions of faces in input video to produce feature vectors, which are …

Computer Aided Diagnosis Of Oral Cancer: Using Time-Step Ct Images, Jonathan T. Scott

Masters Theses

"In medical imaging it is a very common practice to use a technique known as Time-Step imaging in patients who might develop cancer. Time-Step imaging it a very powerful technique, however it can lead to unmanageable amounts of image data. Previously the only way to search all of this data was to manually look through all of the files. This had to be done by trained professionals who knew what to look for within the images and make a judgment about the patient based on the images. This paper discusses the development of an algorithm to have a computer search …

Adaptive Graph Construction For Isomap Manifold Learning, Loc Tran, Zezhong Zheng, Guoquing Zhou, Jiang Li, Karen O. Egiazarian (Ed.), Sos S. Agaian (Ed.), Atanas P. Gotchev (Ed.)

Electrical & Computer Engineering Faculty Publications

Isomap is a classical manifold learning approach that preserves geodesic distance of nonlinear data sets. One of the main drawbacks of this method is that it is susceptible to leaking, where a shortcut appears between normally separated portions of a manifold. We propose an adaptive graph construction approach that is based upon the sparsity property of the ℓ₁ norm. The ℓ₁ enhanced graph construction method replaces k-nearest neighbors in the classical approach. The proposed algorithm is first tested on the data sets from the UCI data base repository which showed that the proposed approach performs better than …

Monolithic Optofluidic Ring Resonator Lasers Created By Femtosecond Laser Nanofabrication, Hengky Chandrahalim, Qiushu Chen, Ali A. Said, Mark Dugan, Xudong Fan

Faculty Publications

We designed, fabricated, and characterized a monolithically integrated optofluidic ring resonator laser that is mechanically, thermally, and chemically robust. The entire device, including the ring resonator channel and sample delivery microfluidics, was created in a block of fused-silica glass using a 3-dimensional femtosecond laser writing process. The gain medium, composed of Rhodamine 6G (R6G) dissolved in quinoline, was flowed through the ring resonator. Lasing was achieved at a pump threshold of approximately 15 μJ/mm². Detailed analysis shows that the Q-factor of the optofluidic ring resonator is 3.3 × 10⁴, which is limited by both solvent …

Multi-Surface Simplex Spine Segmentation For Spine Surgery Simulation And Planning, Rabia Haq

Computational Modeling & Simulation Engineering Theses & Dissertations

This research proposes to develop a knowledge-based multi-surface simplex deformable model for segmentation of healthy as well as pathological lumbar spine data. It aims to provide a more accurate and robust segmentation scheme for identification of intervertebral disc pathologies to assist with spine surgery planning. A robust technique that combines multi-surface and shape statistics-aware variants of the deformable simplex model is presented. Statistical shape variation within the dataset has been captured by application of principal component analysis and incorporated during the segmentation process to refine results. In the case where shape statistics hinder detection of the pathological region, user-assistance is …

Data Completion Methods For Improved Developmental Stage Annotation Of Drosophila Embryos In Images, Chitsanu Janyalikit

Electrical & Computer Engineering Theses & Dissertations

Drosophila melanogaster is a dominant model organism for studying the function of animal genes in initial stages of embryogenesis. Usually, images containing Drosophila gene expression patterns are captured at different developmental stages to study the interconnection of animal genes. To achieve most biologically meaningful results, gene expression images from a similar stage should be compared. Currently, biologists manually classify embryos in images into different stages, which is time intensive and infeasible for current massively produced gene expression images. Therefore, there is a need to develop an automatic system for the annotation.

Gene expression information in embryo images usually appears as …

Cervical Cancer Histology Image Feature Extraction And Classification, Peng Guo

Masters Theses

"Cervical cancer, the second most common cancer affecting women worldwide and the most common in developing countries can be cured if detected early and treated. Expert pathologists routinely visually examine histology slides for cervix tissue abnormality assessment. In previous research, an automated, localized, fusion-based approach was investigated for classifying squamous epithelium into Normal, CIN1, CIN2, and CIN3 grades of cervical intraepithelial neoplasia (CIN) based on image analysis of 62 digitized histology images obtained through the National Library of Medicine. In this research, CIN grade assessments from two pathologists are analyzed and are used to facilitate atypical cell concentration feature development …

Mathematical Model Development Of Super-Resolution Image Wiener Restoration, Amr H. Yousef, Jiang Li, Mohammad A. Karim

Electrical & Computer Engineering Faculty Publications

In super-resolution (SR), a set of degraded low-resolution (LR) images are used to reconstruct a higher-resolution image that suffers from acquisition degradations. One way to boost SR images visual quality is to use restoration filters to remove reconstructed images artifacts. We propose an efficient method to optimally allocate the LR pixels on the high-resolution grid and introduce a mathematical derivation of a stochastic Wiener filter. It relies on the continuous-discrete-continuous model and is constrained by the periodic and nonperiodic interrelationships between the different frequency components of the proposed SR system. We analyze an end-to-end model and formulate the Wiener filter …

Fast Stochastic Wiener Filter For Super-Resolution Image Restoration With Information Theoretic Visual Quality Assessment, Amr Hussein Yousef, Jiang Li, Mohammad Karim, Mark Allen Neifeld (Ed.), Amit Ashok (Ed.)

Electrical & Computer Engineering Faculty Publications

Super-resolution (SR) refers to reconstructing a single high resolution (HR) image from a set of subsampled, blurred and noisy low resolution (LR) images. The reconstructed image suffers from degradations such as blur, aliasing, photo-detector noise and registration and fusion error. Wiener filter can be used to remove artifacts and enhance the visual quality of the reconstructed images. In this paper, we introduce a new fast stochastic Wiener filter for SR reconstruction and restoration that can be implemented efficiently in the frequency domain. Our derivation depends on the continuous-discrete-continuous (CDC) model that represents most of the degradations encountered during the image-gathering …

Radio-Frequency Breast Cancer Imaging Results For A Simplified Cylindrical Phantom, Giuseppe Ruvio, Raffaele Solimene, Antonietta D'Alterio, Max Ammann, Rocco Pierri

Conference Papers

Microwave imaging is a pervasive research field and
is useful in numerous applicative diagnostic noninvasive contexts. This paper focuses on two aspects. First, we perform a numerical investigation to assess the role played by fundamental parameters (i.e. number of sensors, operating frequency bandwidth) on cancer detection. To this end, a simplified cylindrical phantom probed by ideal two-dimensional dipoles (i.e. infinitely long along the axis of invariance) is considered. Second, in order to focus on the role of the antennas, we analyze, still by numerical simulations and for a simplified breast model, how performances vary when a realistic antenna is adopted.

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% …

On The Visual Quality Enhancement Of Super-Resolution Images, Amr Hussein Yousef, Jiang Li, Mohammad Karim, Andrew G. Tescher (Ed.)

Electrical & Computer Engineering Faculty Publications

Super-resolution (SR) is the process of obtaining a higher resolution image from a set of lower resolution (LR) blurred and noisy images. One may, then, envision a scenario where a set of LR images is acquired with a sensor on a moving platform. In such a case, an SR image can be reconstructed in an area of sufficient overlap between the LR images which generally have a relative shift with respect to each other by subpixel amounts. The visual quality of the SR image is affected by many factors such as the optics blur, the inherent signalto- noise ratio of …

Return-Path, Multiple-Principal-Angle, Internal-Reflection Ellipsometer For Measuring Ir Optical Properties Of Aqueous Solutions, Rasheed M.A. Azzam

Electrical Engineering Faculty Publications

A retroreflection (return-path) spectroscopic ellipsometer without a wave plate is described that uses an IR-transparent high-refractive-index hemicylindrical semiconductor substrate to measure the optical properties of aqueous solutions from multiple principal angles and multiple principal azimuths of attenuated internal reflection (AIR) at the semiconductor–solution interface. The pseudo-Brewster angle of minimum reflectance for the p polarization is also readily measured using the same instrument. This wealth of data can also be used to characterize thin films at the solid–liquid interface. Simulated results of AIR at the Si–water interface over the 1.2–11 μm IR spectral range are presented in support of this concept. …

Multi-Frequency Processing For Lumen Enhancement With Wideband Intravascular Ultrasound, Rory A. Carrillo

Master's Theses

The application of high frequency ultrasound is the key to higher resolution intravascular ultrasound (IVUS) images. The need to further improve the IVUS spatial resolution may drive the transducer center frequency even higher than the current 40 MHz range. However, increasing the center frequency may be challenging as it leads to stronger scattering echoes from blood. The high level of blood scattering echoes may obscure the arterial lumen and make image interpretation difficult. Blood backscatter levels increase with transmission center frequency at a much greater rate compared to arterial tissue. These different frequency dependencies provide a potential method to distinguish …

Combining Molecular And Imaging Biomarkers To Enhance Maldi Biomarker Analysis, Ayyappa Chowdary Vadlamudi

Electrical & Computer Engineering Theses & Dissertations

This thesis presents a three-step method to predict prostate cancer (PCa) regions on biopsy tissue samples based on high confident, low resolution PCa regions marked by a pathologist. First, a prediction model is designed to predict PCa regions using matrix-assisted laser desorption mass spectrometry (MALDI-MS) tissue imaging data from one prostate tissue slice. Second, a texture analysis technique is applied to a high magnification optical image for the same purpose from an adjacent tissue slice. Finally, those two results are fused to obtain the PCa regions that will assist MALDI imaging biomarker analysis. Experiments show that the texture analysis based …

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 …

Prostate Cancer Region Prediction Using Maldi Mass Spectra, Ayyappa Vadlamudi, Shao-Hui Chuang, Xiaoyan Sun, Lisa Cazares, Julius Nyalwidhe, Dean Troyer, O. John Semmes, Jiang Li, Frederic D. Mckenzie

Electrical & Computer Engineering Faculty Publications

For the early detection of prostate cancer, the analysis of the Prostate-specific antigen (PSA) in serum is currently the most popular approach. However, previous studies show that 15% of men have prostate cancer even their PSA concentrations are low. MALDI Mass Spectrometry (MS) proves to be a better technology to discover molecular tools for early cancer detection. The molecular tools or peptides are termed as biomarkers. Using MALDI MS data from prostate tissue samples, prostate cancer biomarkers can be identified by searching for molecular or molecular combination that can differentiate cancer tissue regions from normal ones. Cancer tissue regions are …

Adjacent Slice Prostate Cancer Prediction To Inform Maldi Imaging Biomarker Analysis, Shao-Hui Chuang

Electrical & Computer Engineering Theses & Dissertations

Prostate cancer is the second most common type of cancer among men in the U.S. [1]. Traditionally, prostate cancer diagnosis is made by the analysis of prostate-specific antigen (PSA) levels and histopathological images of biopsy samples under microscopes. Proteomic biomarkers can improve upon these methods. MALDI molecular spectra imaging is used to visualize protein/peptide concentrations across biopsy samples to search for biomarker candidates. Unfortunately, traditional processing methods require histopathological examination on one slice of a biopsy sample while the adjacent slice is subjected to the tissue destroying desorption and ionization processes of MALDI. The highest confidence tumor regions gained from …

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 …

Optimizing Computer-Aided Colonic Polyp Detection For Ct Colonography By Evolving The Pareto Front, Jiang Li, Adam Huang, Jack Tao, Jiamin Liu, Robert L. Van Uitert, Nicholas Petrick, Ronald Summers

Electrical & Computer Engineering Faculty Publications

A multiobjective genetic algorithm is designed to optimize a computer-aided detection (CAD) system for identifying colonic polyps. Colonic polyps appear as elliptical protrusions on the inner surface of the colon. Curvature-based features for colonic polyp detection have proved to be successful in several CT colonography (CTC) CAD systems. Our CTC CAD program uses a sequential classifier to form initial polyp detections on the colon surface. The classifier utilizes a set of thresholds on curvature-based features to cluster suspicious colon surface regions into polyp candidates. The thresholds were previously chosen experimentally by using feature histograms. The chosen thresholds were effective for …

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) …

Automatic Diagnosis For Prostate Cancer Using Run-Length Matrix Method, Xiaoyan Sun, Shao-Hui Chuang, Jiang Li, Frederic Mckenzie, Nico Karssemeijer (Ed.), Maryellen L. Giger (Ed.)

Electrical & Computer Engineering Faculty Publications

Prostate cancer is the most common type of cancer and the second leading cause of cancer death among men in US¹. Quantitative assessment of prostate histology provides potential automatic classification of prostate lesions and prediction of response to therapy. Traditionally, prostate cancer diagnosis is made by the analysis of prostate-specific antigen (PSA) levels and histopathological images of biopsy samples under microscopes. In this application, we utilize a texture analysis method based on the run-length matrix for identifying tissue abnormalities in prostate histology. A tissue sample was collected from a radical prostatectomy, H&E fixed, and assessed by a pathologist …

An Approach To Identifying The Biomechanical Differences Between Intercostal Cartilage In Subjects With Pectus Excavatum And Normals In Vivo: Reconstruction And Ct Registration, Zhenzhen Yan

Electrical & Computer Engineering Theses & Dissertations

Pectus excavatum (PE) is a congenital chest wall deformity affecting the ribs and sternum and exhibiting a concave appearance in the anterior chest wall. In this thesis, we describe a study to investigate in vivo differences in the pectus excavatum rib cage and outline steps using normals and pectus patients data in developing models and methods to be used in carrying out the study. We propose methods to develop reconstructed models in order to enable proper registration between data collection points (DCPs) on the 3D CT rib cage model and CT skin surface model and registration between CT surrogate models …

Least Squares Support Vector Machine Based Classification Of Abnormalities In Brain Mr Images, S. Thamarai Selvi, D. Selvathi, R. Ramkumar, Henry Selvaraj

Electrical & Computer Engineering Faculty Research

The manual interpretation of MRI slices based on visual examination by radiologist/physician may lead to missing diagnosis when a large number of MRIs are analyzed. To avoid the human error, an automated intelligent classification system is proposed. This research paper proposes an intelligent classification technique to the problem of classifying four types of brain abnormalities viz. Metastases, Meningiomas, Gliomas, and Astrocytomas. The abnormalities are classified based on Two/Three/ Four class classification using statistical and textural features. In this work, classification techniques based on Least Squares Support Vector Machine (LS-SVM) using textural features computed from the MR images of patient are …

An Unsupervised Fluoroscopic Analysis Of Knee Joint Kinematics, Charles Scott, Elisa H. Barney Smith

Electrical and Computer Engineering Faculty Publications and Presentations

Knowledge of the three dimensional positions of bones at a joint as a function of time is required to accurately model joint kinematics. 3-D bone geometry data from a static computer tomography (CT) images can be combined with time sequence information from 2-D video fluoroscopy images to produce 3-D position data over time. The process involves creating virtual X-rays from the CT image through digitally reconstructed radiograph (DRR) projections. Historically, the process of matching the 3-D and 2-D data has required human interaction. We have eliminated the need for manual initialization using a Monte Carlo technique with a variable search …