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Articles 1 - 14 of 14
Full-Text Articles in Biomedical
Domain Adaptive Federated Learning For Multi-Institution Molecular Mutation Prediction And Bias Identification, W. Farzana, M. A. Witherow, I. Longoria, M. S. Sadique, A. Temtam, K. M. Iftekharuddin
Domain Adaptive Federated Learning For Multi-Institution Molecular Mutation Prediction And Bias Identification, W. Farzana, M. A. Witherow, I. Longoria, M. S. Sadique, A. Temtam, K. M. Iftekharuddin
Electrical & Computer Engineering Faculty Publications
Deep learning models have shown potential in medical image analysis tasks. However, training a generalized deep learning model requires huge amounts of patient data that is usually gathered from multiple institutions which may raise privacy concerns. Federated learning (FL) provides an alternative to sharing data across institutions. Nonetheless, FL is susceptible to a few challenges including inversion attacks on model weights, heterogenous data distributions, and bias. This study addresses heterogeneity and bias issues for multi-institution patient data by proposing domain adaptive FL modeling using several radiomics (volume, fractal, texture) features for O6-methylguanine-DNA methyltransferase (MGMT) classification across multiple institutions. The proposed …
Ultrasensitive Tapered Optical Fiber Refractive Index, Erem Ujah, Meimei Lai, Gymama Slaughter
Ultrasensitive Tapered Optical Fiber Refractive Index, Erem Ujah, Meimei Lai, Gymama Slaughter
Electrical & Computer Engineering Faculty Publications
Refractive index (RI) sensors are of great interest for label-free optical biosensing. A tapered optical fiber (TOF) RI sensor with micron-sized waist diameters can dramatically enhance sensor sensitivity by reducing the mode volume over a long distance. Here, a simple and fast method is used to fabricate highly sensitive refractive index sensors based on localized surface plasmon resonance (LSPR). Two TOFs (l = 5 mm) with waist diameters of 5 µm and 12 µm demonstrated sensitivity enhancement at λ = 1559 nm for glucose sensing (5-45 wt%) at room temperature. The optical power transmission decreased with increasing glucose concentration due …
Model-Based Approach For Diffuse Glioma Classification, Grading, And Patient Survival Prediction, Zeina A. Shboul
Model-Based Approach For Diffuse Glioma Classification, Grading, And Patient Survival Prediction, Zeina A. Shboul
Electrical & Computer Engineering Theses & Dissertations
The work in this dissertation proposes model-based approaches for molecular mutations classification of gliomas, grading based on radiomics features and genomics, and prediction of diffuse gliomas clinical outcome in overall patient survival. Diffuse gliomas are types of Central Nervous System (CNS) brain tumors that account for 25.5% of primary brain and CNS tumors and originate from the supportive glial cells. In the 2016 World Health Organization’s (WHO) criteria for CNS brain tumor, a major reclassification of the diffuse gliomas is presented based on gliomas molecular mutations and the growth behavior. Currently, the status of molecular mutations is determined by obtaining …
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
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 …
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.)
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
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 …
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
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.)
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 …
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
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 …
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.)
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.)
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 …
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
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 …
Wavelet Analysis In Virtual Colonoscopy, Sharon Greenblum, Jiang Li, Adam Huang, Ronald M. Summers, Armando Manduca (Ed.), Amir A. Amini (Ed.)
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
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.