Bioimaging and Biomedical Optics Commons^™

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 …

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 …

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 …

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 …

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 …

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 …

Wavelet Analysis In Virtual Colonoscopy, Sharon Greenblum, Jiang Li, Adam Huang, Ronald M. Summers, Armando Manduca (Ed.), Amir A. Amini (Ed.)

Electrical & Computer Engineering Faculty Publications

The computed tomographic colonography (CTC) computer aided detection (CAD) program is a new method in development to detect colon polyps in virtual colonoscopy. While high sensitivity is consistently achieved, additional features are desired to increase specificity. In this paper, a wavelet analysis was applied to CTCCAD outputs in an attempt to filter out false positive detections. 52 CTCCAD detection images were obtained using a screen capture application. 26 of these images were real polyps, confirmed by optical colonoscopy and 26 were false positive detections. A discrete wavelet transform of each image was computed with the MATLAB wavelet toolbox using the …

Implementation Of Gy-Eq For Deterministic Effects Limitation In Shield Design, John W. Wilson, Myung-Hee Y. Kim, Giovanni De Angelis, Francis A. Cucinotta, Nobuaki Yoshizawa, Francis F. Badavi

Mathematics & Statistics Faculty Publications

The NCRP has recently defined RBE values and a new quantity (Gy-Eq) for use in estimation of deterministic effects in space shielding and operations. The NCRP's RBE for neutrons is left ambiguous and not fully defined. In the present report we will suggest a complete definition of neutron RBE consistent with the NCRP recommendations and evaluate attenuation properties of deterministic effects (Gy-Eq) in comparison with other dosimetric quantities.