Image Segmentation-Registration Cooperative Techniques Applied to Biomedicine and Remote Sensing

Okorie, Azubuike ProQuest Dissertations & Theses Delaware State Uni 2022 해외박사(DDOD)

소속기관이 구독 중이 아닌 경우 오후 4시부터 익일 오전 9시까지 원문보기가 가능합니다.

The topic of this dissertation is the development of segmentation–registration and registration–segmentation cooperative techniques for applications in remote sensing and biomedical imaging domains. On one hand, image segmentation is the process of partitioning digital images into meaningful regions, while on the other hand, image registration which is the process of establishing a correspondence for the alignment of two images of the same scene portraying viewpoint, temporal or sensor variations. Image registration and segmentation are useful in many domains of application in computer vision and biomedicine. In biomedical applications, accurate region delineation is useful for tissue identification, tissue quantification and cell tracking. In addition, correct alignment of images may be critical for detection of tumors, changes in body tissues, the effect of aging on the bone structure, or diseases in the body. Hence aiding treatment planning, clinical diagnosis, and clinical trials. In satellite–remote sensing applications, segmentation may be used for target recogniction, and object detection, while a precise alignment of images may be important for target and change detection, urban planning, and many other applications. In the part of this work that relates to automated registration via segmentation, we propose a joint region feature set and a matching cost function for registration of remote sensing/satellite images exhibiting multi-sensor, multi-spectral, and multi-view characteristics. Our joint region descriptor is a combination of Fourier, intensity, and shape features. Our work first delineates the input and reference images into regions by segmentation. Next, it extract joint intensity and shape features from the regions. It computes region similarity measures for feature matching. Finally, it estimate the geometric transform by the maximum-likelihood sampling consensus (MLESAC) technique. Our results are promising, showing potentials for sub-pixel accuracy. In addition, our algorithm outperformed most state-of-the-art automated feature-based registration algorithms incorporating feature detection and extraction algorithms such as SURF-SURF, BRISK-SURF, HARRIS-FREAK, FAST-FREAK, minimum eigenvalue-FREAK, and KAZE-KAZE.Furthermore, this work proposes an unsupervised subspace learning-based disaster mapping (SLDM) technique using pre- and post-disaster satellite imagery. It first finds the geometric transformation for automatic image registration by matching regions represented by shape and intensity descriptors as described above. It produces piece-wise constant approximations of the two images using the delineated regions. It performs subspace learning in the joint regional space to produce a change map and identify the damaged regions by probabilistic subspace distances between test points and the subspace model.We tested our method on seven disaster datasets including four wildfire events, two flooding events and one tsunami/earthquake event. Comparative analysis of our method with state-of-the-art techniques, such as Gabor Two-Level Clustering (G-TLC), image differencing and differenced-spectiral indices (NDVI, NBR, and NDWI) indicate that the proposed SLDM framework and especially the multi-band variant SLDM-SF-MS-MB produced more accurate disaster maps than the compared method, overall. Quantitative and qualitative evaluations of the proposed SLDM method confirm its capacity for disaster mapping, and its applicability to different categories of disaster events. In the segmentation approach using deformable registration models, our work first introduces a statistical shape modeling technique for atlas generation and multi-atlas-based image segmentation (MAIS) techniques for the identification of soft and hard tissues in biomedical imaging data. To calculate the deformation fields, it employed multi-grid free-form deformation (FFD) models with B-splines, symmetric log-domain extension of diffeomorphic demons (SDD) or Symmetric Normalization (SyN) from the Advanced Normalization Tools (ANTs) library. It then applies majority voting, or Simultaneous Truth And Performance Level Estimation (STAPLE) for label fusion. It compared the results of our MAIS methodology for each deformable registration model and each label fusion method, using Dice similarity coefficient scores (DSC) against manually segmented tissue label maps. It applied MAIS methods: FFD-STPL, SDD-STPL, and SyN-STPL, to two datasets: 2D peripheral quantitative computed tomography (pQCT) scans in the lower leg and 3D thigh magnetic resonance images (MRIs), where STPL stands for simultaneous truth and performance level estimation or STAPLE. Our results are encouraging for both applications, even for pQCT scans with considerable quality degradations – such as motion artifacts, or when boundaries between tissues are ambiguous because of physiological reasons. SDD-STPL outperformed FFD-STPL and SyN-STPL on both datasets. Furthermore, our method applied the BM3D algorithm to enhance the statistical atlas generated for the 3D thigh MRI dataset, and generated silver truth with our MAIS model. Statistical enhancement using BM3D improved our MAIS result better than other image enhancement techniques, including Wiener filtering, unsharp-masking, histogram equalization and histogram matching.

Sensitive Detection of Atmospheric Methane and Nitrous Oxide Using Higher Harmonic Wavelength Modulation Spectroscopy

Hlaing, May Hnin ProQuest Dissertations & Theses Delaware State Uni 2020 해외박사(DDOD)

소속기관이 구독 중이 아닌 경우 오후 4시부터 익일 오전 9시까지 원문보기가 가능합니다.

Atmospheric methane (CH4), and nitrous oxide (N2O) are two of the most potent greenhouse gases with relatively significant radiative forcing, contributing to global warming and climate change. In addition, due to their atmospheric residence time, both CH4 and N2O have much higher global warming potentials than the most prevalent greenhouse gas, carbon dioxide (CO2). Over the last several decades, the concentrations of CH4 and N2O have rapidly increased due to diverse natural and anthropogenic sources and sinks resulting in significant uncertainty in their emission budget. Currently, there are several commercial and academic research technologies and field instruments (earth, aerial, and geostationary satellite-based) to precisely quantify and profile atmospheric CH4 and N2O in real-time. However, there are limited arrays of technologies that can simultaneously and synchronously sample these two species on a local and global scale. Simultaneous detection of CH4 and N2O is of great interest in discriminating and correlating carbon and nitrogen emissions from biogenic and abiogenic sources, for instance, in measurements of soil flux and atmospheric monitoring near the surface and in the atmospheric boundary layer region.Semiconductor laser-based sensing techniques have been widely in use to develop precise sensor systems for detecting and monitoring trace gases in the atmosphere. Among many sensing techniques, wavelength modulation spectroscopy (WMS) is a non-intrusive and highly sensitive technique for probing atmospheric broadened rotational-vibrational molecular transitions. By employing the principles of modulation technique, WMS higher harmonic detection (WMS-HHD) not only can simultaneously measure concentrations of trace species but also can improve detection sensitivity and spectral line resolution. In addition, semiconductor mid-infrared laser sources integrated with optical multipass cells in an open-path configuration, and optomechanical components with WMS methodology provide a simple, compact, low-power, and low-cost sensing technology suitable for deployment in diverse platforms, including mobile, ground-based and airborne systems.This dissertation focuses on the design and development of a precise, non-intrusive, and ultra-sensitive sensor system to detect atmospheric CH4 and N2O in the 7.8 μm spectral region. The system is built upon a room-temperature thermo-electrically cooled mid-infrared quantum-cascade laser source, and Heriot design optical multipass cell implemented with WMS-HHD sensing technique. We show a central aspect of WMS-HHD where the structure of the higher harmonic signal and optimal WMS detection order is employed to, (i) accurately quantity laser tuning properties and frequency modulation response, and (ii) resolve overlapping rotational, vibrational molecular transition of multiple (CH4, N2O, and H2O) line-transitions of different oscillator strengths. Finally, we quantify the performance of the sensor system with detection limits of 52 parts per billion by volume (ppbv) for N2O and 162 ppbv for CH4. The laboratory precisions are 0.59%–1.12% for CH4 and 0.82%–1.85% for N2O, which translate to uncertainties of 10–22 ppbv for CH4 and 3 ppbv for N2O over the background of 1800 ppbv CH4 and 352 ppbv N2O, respectively.

The Impact of a Two Generational Approach on the Academic Outcomes of Low-Income Children - Case Study Analysis

Williams, Whitney J ProQuest Dissertations & Theses Delaware State Uni 2019 해외박사(DDOD)

소속기관이 구독 중이 아닌 경우 오후 4시부터 익일 오전 9시까지 원문보기가 가능합니다.

Low-income parents face a number of challenges that affect the well-being and academic outcomes of their children. Two-generational approaches, which simultaneously address the needs of low-income parents and children, may provide a comprehensive and effective strategy for improving academic achievement among children from low-income families. Although growing interest in two-generational programs has prompted the development of a number of programs, a comprehensive analysis of the best practices related to these programs was lacking. The purpose of this project was to synthesize existing research on the two-generational approaches used with parents and children from low-income families to develop a model that leaders and policy makers may follow. This project employed a case study analysis approach. Five existing case studies were used to explore research-based best practices for two-generational programs aimed at improving academic outcomes for pre-kindergarten and elementary (pre-K–5) school students from low-income families. The study was guided by the framework for two-generational models, developed by Scott, Popkin, and Simington (2016). Qualitative analysis revealed three strategies that were most successful across the cases, including improving parent education, fostering parenting skills, and facilitating parental involvement. From these results, a best practices model was created that may inform the development of future two-generational programs operating with limited resources. Two-generational programs are a wonderful way for ensuring that resources and assistance are not provided to low-income parents and children in silos, but that interventions for this group are comprehensive and dynamic. The current analysis provides valuable guidance for the development of future two-generational programs. The implications of study suggested an effective way to leverage the four-element framework in the development of two-generational programs may be to start with the outcomes element and work backwards.