Bioimaging and Biomedical Optics Commons^™

Development And Validation Of A Three-Dimensional Optical Imaging System For Chest Wall Deformity Measurement, Nahom Kidane

Electrical & Computer Engineering Theses & Dissertations

Congenital chest wall deformities (CWD) are malformations of the thoracic cage that become more pronounced during early adolescence. Pectus excavatum (PE) is the most common CWD, characterized by an inward depression of the sternum and adjacent costal cartilage. A cross-sectional computed tomography (CT) image is mainly used to calculate the chest thoracic indices. Physicians use the indices to quantify PE deformity, prescribe surgical or non-surgical therapies, and evaluate treatment outcomes. However, the use of CT is increasingly causing physicians to be concerned about the radiation doses administered to young patients. Furthermore, radiographic indices are an unsafe and expensive method of …

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 …

Using Feature Extraction From Deep Convolutional Neural Networks For Pathological Image Analysis And Its Visual Interpretability, Wei-Wen Hsu

Electrical & Computer Engineering Theses & Dissertations

This dissertation presents a computer-aided diagnosis (CAD) system using deep learning approaches for lesion detection and classification on whole-slide images (WSIs) with breast cancer. The deep features being distinguishing in classification from the convolutional neural networks (CNN) are demonstrated in this study to provide comprehensive interpretability for the proposed CAD system using the domain knowledge in pathology. In the experiment, a total of 186 slides of WSIs were collected and classified into three categories: Non-Carcinoma, Ductal Carcinoma in Situ (DCIS), and Invasive Ductal Carcinoma (IDC). Instead of conducting pixel-wise classification (segmentation) into three classes directly, a hierarchical framework with the …

Computational Modeling For Abnormal Brain Tissue Segmentation, Brain Tumor Tracking, And Grading, Syed Mohammad Shamin Reza

Electrical & Computer Engineering Theses & Dissertations

This dissertation proposes novel texture feature-based computational models for quantitative analysis of abnormal tissues in two neurological disorders: brain tumor and stroke. Brain tumors are the cells with uncontrolled growth in the brain tissues and one of the major causes of death due to cancer. On the other hand, brain strokes occur due to the sudden interruption of the blood supply which damages the normal brain tissues and frequently causes death or persistent disability. Clinical management of these brain tumors and stroke lesions critically depends on robust quantitative analysis using different imaging modalities including Magnetic Resonance (MR) and Digital Pathology …

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 …

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 …

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 …

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 …

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 …