Matching Items (27)
Description
Despite significant advances in digital pathology and automation sciences, current diagnostic practice for cancer detection primarily relies on a qualitative manual inspection of tissue architecture and cell and nuclear morphology in stained biopsies using low-magnification, two-dimensional (2D) brightfield microscopy. The efficacy of this process is limited by inter-operator variations in sample preparation and imaging, and by inter-observer variability in assessment. Over the past few decades, the predictive value quantitative morphology measurements derived from computerized analysis of micrographs has been compromised by the inability of 2D microscopy to capture information in the third dimension, and by the anisotropic spatial resolution inherent to conventional microscopy techniques that generate volumetric images by stacking 2D optical sections to approximate 3D. To gain insight into the analytical 3D nature of cells, this dissertation explores the application of a new technology for single-cell optical computed tomography (optical cell CT) that is a promising 3D tomographic imaging technique which uses visible light absorption to image stained cells individually with sub-micron, isotropic spatial resolution. This dissertation provides a scalable analytical framework to perform fully-automated 3D morphological analysis from transmission-mode optical cell CT images of hematoxylin-stained cells. The developed framework performs rapid and accurate quantification of 3D cell and nuclear morphology, facilitates assessment of morphological heterogeneity, and generates shape- and texture-based biosignatures predictive of the cell state. Custom 3D image segmentation methods were developed to precisely delineate volumes of interest (VOIs) from reconstructed cell images. Comparison with user-defined ground truth assessments yielded an average agreement (DICE coefficient) of 94% for the cell and its nucleus. Seventy nine biologically relevant morphological descriptors (features) were computed from the segmented VOIs, and statistical classification methods were implemented to determine the subset of features that best predicted cell health. The efficacy of our proposed framework was demonstrated on an in vitro model of multistep carcinogenesis in human Barrett's esophagus (BE) and classifier performance using our 3D morphometric analysis was compared against computerized analysis of 2D image slices that reflected conventional cytological observation. Our results enable sensitive and specific nuclear grade classification for early cancer diagnosis and underline the value of the approach as an objective adjunctive tool to better understand morphological changes associated with malignant transformation.
ContributorsNandakumar, Vivek (Author) / Meldrum, Deirdre R (Thesis advisor) / Nelson, Alan C. (Committee member) / Karam, Lina J (Committee member) / Ye, Jieping (Committee member) / Johnson, Roger H (Committee member) / Bussey, Kimberly J (Committee member) / Arizona State University (Publisher)
Created2013
Description
Cell-cell interactions in a microenvironment under stress conditions play a critical role in pathogenesis and pre-malignant progression. Hypoxia is a central factor in carcinogenesis, which induces selective pressure in this process. Understanding the role of intercellular communications and cellular adaptation to hypoxia can help discover new cancer biosignatures and more effective diagnostic and therapeutic strategies. This dissertation presents a study on transcriptomic and metabolic profiling of pre-malignant progression of Barrett's esophagus. It encompasses two methodology developments and experimental findings of two related studies. To integrate phenotype and genotype measurements, a minimally invasive method was developed for selectively retrieving single adherent cells from cell cultures. Selected single cells can be harvested by a combination of mechanical force and biochemical treatment after phenotype measurements and used for end-point assays. Furthermore, a method was developed for analyzing expression levels of ten genes in individual mammalian cells with high sensitivity and reproducibility without the need of pre-amplifying cDNA. It is inexpensive and compatible with most of commercially available RT-qPCR systems, which warrants a wide applicability of the method to gene expression analysis in single cells. In the first study, the effect of intercellular interactions was investigated between normal esophageal epithelial and dysplastic Barrett's esophagus cells on gene expression levels and cellular functions. As a result, gene expression levels in dysplastic cells were found to be affected to a significantly larger extent than in the normal esophageal epithelial cells. These differentially expressed genes are enriched in cellular movement, TGFβ and EGF signaling networks. Heterotypic interactions between normal and dysplastic cells can change cellular motility and inhibit proliferation in both normal and dysplastic cells. In the second study, alterations in gene transcription levels and metabolic phenotypes between hypoxia-adapted cells and age-matched normoxic controls representing four different stages of pre-malignant progression in Barrett's esophagus were investigated. Through differential gene expression analysis and mitochondrial membrane potential measurements, evidence of clonal evolution induced by hypoxia selection pressure in metaplastic and high-grade dysplastic cells was found. These discoveries on cell-cell interactions and hypoxia adaptations provide a deeper insight into the dynamic evolutionary process in pre-malignant progression of Barrett's esophagus.
ContributorsZeng, Jia (Author) / Meldrum, Deirdre R (Thesis advisor) / Kelbauskas, Laimonas (Committee member) / Barrett, Michael T (Committee member) / Bussey, Kimberly J (Committee member) / Zhang, Weiwen (Committee member) / Arizona State University (Publisher)
Created2014
Description
Within the last decade there has been remarkable interest in single-cell metabolic analysis as a key technology for understanding cellular heterogeneity, disease initiation, progression, and drug resistance. Technologies have been developed for oxygen consumption rate (OCR) measurements using various configurations of microfluidic devices. The technical challenges of current approaches include: (1) deposition of multiple sensors for multi-parameter metabolic measurements, e.g. oxygen, pH, etc.; (2) tedious and labor-intensive microwell array fabrication processes; (3) low yield of hermetic sealing between two rigid fused silica parts, even with a compliance layer of PDMS or Parylene-C. In this thesis, several improved microfabrication technologies are developed and demonstrated for analyzing multiple metabolic parameters from single cells, including (1) a modified "lid-on-top" configuration with a multiple sensor trapping (MST) lid which spatially confines multiple sensors to micro-pockets enclosed by lips for hermetic sealing of wells; (2) a multiple step photo-polymerization method for patterning three optical sensors (oxygen, pH and reference) on fused silica and on a polyethylene terephthalate (PET) surface; (3) a photo-polymerization method for patterning tri-color (oxygen, pH and reference) optical sensors on both fused silica and on the PET surface; (4) improved KMPR/SU-8 microfabrication protocols for fabricating microwell arrays that can withstand cell culture conditions. Implementation of these improved microfabrication methods should address the aforementioned challenges and provide a high throughput and multi-parameter single cell metabolic analysis platform.
ContributorsSong, Ganquan (Author) / Meldrum, Deirdre R (Thesis advisor) / Goryll, Michael (Committee member) / Wang, Hong (Committee member) / Tian, Yanqing (Committee member) / Arizona State University (Publisher)
Created2014
Description
Texture analysis plays an important role in applications like automated pattern inspection, image and video compression, content-based image retrieval, remote-sensing, medical imaging and document processing, to name a few. Texture Structure Analysis is the process of studying the structure present in the textures. This structure can be expressed in terms of perceived regularity. Our human visual system (HVS) uses the perceived regularity as one of the important pre-attentive cues in low-level image understanding. Similar to the HVS, image processing and computer vision systems can make fast and efficient decisions if they can quantify this regularity automatically. In this work, the problem of quantifying the degree of perceived regularity when looking at an arbitrary texture is introduced and addressed. One key contribution of this work is in proposing an objective no-reference perceptual texture regularity metric based on visual saliency. Other key contributions include an adaptive texture synthesis method based on texture regularity, and a low-complexity reduced-reference visual quality metric for assessing the quality of synthesized textures. In order to use the best performing visual attention model on textures, the performance of the most popular visual attention models to predict the visual saliency on textures is evaluated. Since there is no publicly available database with ground-truth saliency maps on images with exclusive texture content, a new eye-tracking database is systematically built. Using the Visual Saliency Map (VSM) generated by the best visual attention model, the proposed texture regularity metric is computed. The proposed metric is based on the observation that VSM characteristics differ between textures of differing regularity. The proposed texture regularity metric is based on two texture regularity scores, namely a textural similarity score and a spatial distribution score. In order to evaluate the performance of the proposed regularity metric, a texture regularity database called RegTEX, is built as a part of this work. It is shown through subjective testing that the proposed metric has a strong correlation with the Mean Opinion Score (MOS) for the perceived regularity of textures. The proposed method is also shown to be robust to geometric and photometric transformations and outperforms some of the popular texture regularity metrics in predicting the perceived regularity. The impact of the proposed metric to improve the performance of many image-processing applications is also presented. The influence of the perceived texture regularity on the perceptual quality of synthesized textures is demonstrated through building a synthesized textures database named SynTEX. It is shown through subjective testing that textures with different degrees of perceived regularities exhibit different degrees of vulnerability to artifacts resulting from different texture synthesis approaches. This work also proposes an algorithm for adaptively selecting the appropriate texture synthesis method based on the perceived regularity of the original texture. A reduced-reference texture quality metric for texture synthesis is also proposed as part of this work. The metric is based on the change in perceived regularity and the change in perceived granularity between the original and the synthesized textures. The perceived granularity is quantified through a new granularity metric that is proposed in this work. It is shown through subjective testing that the proposed quality metric, using just 2 parameters, has a strong correlation with the MOS for the fidelity of synthesized textures and outperforms the state-of-the-art full-reference quality metrics on 3 different texture databases. Finally, the ability of the proposed regularity metric in predicting the perceived degradation of textures due to compression and blur artifacts is also established.
ContributorsVaradarajan, Srenivas (Author) / Karam, Lina J (Thesis advisor) / Chakrabarti, Chaitali (Committee member) / Li, Baoxin (Committee member) / Tepedelenlioğlu, Cihan (Committee member) / Arizona State University (Publisher)
Created2014
Description
The ocean is vital to the health of our planet but remains virtually unexplored. Many researchers seek to understand a wide range of geological and biological phenomena by developing technologies which enable exploration of the deep-sea. The task of developing a technology which can withstand extreme pressure and temperature gradients in the deep ocean is not trivial. Of these technologies, underwater vehicles were developed to study the deep ocean, but remain large and expensive to manufacture. I am proposing the development of cost efficient miniaturized underwater vehicle (mUV) with propulsion systems to carry small measurement devices and enable deep-sea exploration. These mUV's overall size is optimized based on the vehicle parameters such as energy density, desired velocity, swimming time and propulsion performance. However, there are limitations associated with the size of the mUV which leads to certain challenges. For example, 2000 m below the sea level, the pressure is as high as 3000 psi. Therefore, certain underwater vehicle modules, such as the propulsion system, will require pressure housing to ensure the functionality of the thrust generation. In the case of a mUV swimming against the deep-sea current, a thrust magnitude is required to enable the vehicle to overcome the ocean current speed and move forward. Therefore, the size of the mUV is limited by the energy density and the propeller size. An equation is derived to miniaturize underwater vehicle while performing with a certain specifications. An inrunner three-phase permanent magnet brushless DC motor is designed and fabricated with a specific size to fit inside the mUV's core. The motor is composed of stator winding in a pressure housing and an open to water ring-propeller rotor magnet. Several ring-propellers are 3D printed and tested experimentally to determine their performances and efficiencies. A planer motion optimal trajectory for the mUV is determined to minimize the energy usage. Those studies enable the design of size optimized underwater vehicle with propulsion to carry small measurement sensors and enable underwater exploration. Developing mUV's will enable ocean exploration that can lead to significant scientific discoveries and breakthroughs that will solve current world health and environmental problems.
ContributorsMerza, Saeed A (Author) / Meldrum, Deirdre R (Thesis advisor) / Chao, Shih-hui (Committee member) / Shankar, Praveen (Committee member) / Saripalli, Srikanth (Committee member) / Berman, Spring Melody (Committee member) / Arizona State University (Publisher)
Created2014
Description
The production of monomer compounds for synthesizing plastics has to date been largely restricted to the petroleum-based chemical industry and sugar-based microbial fermentation, limiting its sustainability and economic feasibility. Cyanobacteria have, however, become attractive microbial factories to produce renewable fuels and chemicals directly from sunlight and CO2. To explore the feasibility of photosynthetic production of (S)- and (R)-3-hydroxybutyrate (3HB), building-block monomers for synthesizing the biodegradable plastics polyhydroxyalkanoates and precursors to fine chemicals, synthetic metabolic pathways have been constructed, characterized and optimized in the cyanobacterium Synechocystis sp. PCC 6803 (hereafter Synechocystis 6803). Both types of 3HB molecules were produced and readily secreted from Synechocystis cells without over-expression of transporters. Additional inactivation of the competing PHB biosynthesis pathway further promoted the 3HB production. Analysis of the intracellular acetyl-CoA and anion concentrations in the culture media indicated that the phosphate consumption during the photoautotrophic growth and the concomitant elevated acetyl-CoA pool acted as a key driving force for 3HB biosynthesis in Synechocystis. Fine-tuning of the gene expression levels via strategies, including tuning gene copy numbers, promoter engineering and ribosome binding site optimization, proved critical to mitigating metabolic bottlenecks and thus improving the 3HB production. One of the engineered Synechocystis strains, namely R168, was able to produce (R)-3HB to a cumulative titer of ~1600 mg/L, with a peak daily productivity of ~200 mg/L, using light and CO2 as the sole energy and carbon sources, respectively. Additionally, in order to establish a high-efficiency transformation protocol in cyanobacterium Synechocystis 6803, methyltransferase-encoding genes were cloned and expressed to pre-methylate the exogenous DNA before Synechocystis transformation. Eventually, the transformation efficiency was increased by two orders of magnitude in Synechocystis. This research has demonstrated the use of cyanobacteria as cell factories to produce 3HB directly from light and CO2, and developed new synthetic biology tools for cyanobacteria.
ContributorsWang, Bo (Author) / Meldrum, Deirdre R (Thesis advisor) / Zhang, Weiwen (Committee member) / Sandrin, Todd R. (Committee member) / Nielsen, David R (Committee member) / Arizona State University (Publisher)
Created2014
Description
The NLR family, pyrin domain-containing 3 (NLRP3) inflammasome is essential for the innate immune response to danger signals. Importantly, the NLRP3 inflammasome responds to structurally and functionally dissimilar stimuli. It is currently unknown how the NLRP3 inflammasome responds to such diverse triggers. This dissertation investigates the role of ion flux in regulating the NLRP3 inflammasome. Project 1 explores the relationship between potassium efflux and Syk tyrosine kinase. The results reveal that Syk activity is upstream of mitochondrial oxidative signaling and is crucial for inflammasome assembly, pro-inflammatory cytokine processing, and caspase-1-dependent pyroptotic cell death. Dynamic potassium imaging and molecular analysis revealed that Syk is downstream of, and regulated by, potassium efflux. Project 1 reveals the first identified intermediate regulator of inflammasome activity regulated by potassium efflux. Project 2 focuses on P2X7 purinergic receptor-dependent ion flux in regulating the inflammasome. Dynamic potassium imaging revealed an ATP dose-dependent efflux of potassium driven by P2X7. Surprisingly, ATP induced mitochondrial potassium mobilization, suggesting a mitochondrial detection of purinergic ion flux. ATP-induced potassium and calcium flux was found to regulate mitochondrial oxidative signaling upstream of inflammasome assembly. First-ever multiplexed imaging of potassium and calcium dynamics revealed that potassium efflux is necessary for calcium influx. These results suggest that ATP-induced potassium efflux regulates the inflammasome by calcium influx-dependent mitochondrial oxidative signaling. Project 2 defines a coordinated cation flux dependent on the efflux of potassium and upstream of mitochondrial oxidative signaling in inflammasome regulation. Lastly, this dissertation contributes two methods that will be useful for investigating inflammasome biology: an optimized pipeline for single cell transcriptional analysis, and a mouse macrophage cell line expressing a genetically encoded intracellular ATP sensor. This dissertation contributes to understanding the fundamental role of ion flux in regulation of the NLRP3 inflammasome and identifies potassium flux and Syk as potential targets to modulate inflammation.
ContributorsYaron, Jordan Robin (Author) / Meldrum, Deirdre R (Thesis advisor) / Blattman, Joseph N (Committee member) / Glenn, Honor L (Committee member) / Arizona State University (Publisher)
Created2015
Description
Thousands of high-resolution images are generated each day. Detecting and analyzing variations in these images are key steps in image understanding. This work focuses on spatial and multitemporal
visual change detection and its applications in multi-temporal synthetic aperture radar (SAR) images.
The Canny edge detector is one of the most widely-used edge detection algorithms due to its superior performance in terms of SNR and edge localization and only one response to a single edge. In this work, we propose a mechanism to implement the Canny algorithm at the block level without any loss in edge detection performance as compared to the original frame-level Canny algorithm. The resulting block-based algorithm has significantly reduced memory requirements and can achieve a significantly reduced latency. Furthermore, the proposed algorithm can be easily integrated with other block-based image processing systems. In addition, quantitative evaluations and subjective tests show that the edge detection performance of the proposed algorithm is better than the original frame-based algorithm, especially when noise is present in the images.
In the context of multi-temporal SAR images for earth monitoring applications, one critical issue is the detection of changes occurring after a natural or anthropic disaster. In this work, we propose a novel similarity measure for automatic change detection using a pair of SAR images
acquired at different times and apply it in both the spatial and wavelet domains. This measure is based on the evolution of the local statistics of the image between two dates. The local statistics are modeled as a Gaussian Mixture Model (GMM), which is more suitable and flexible to approximate the local distribution of the SAR image with distinct land-cover typologies. Tests on real datasets show that the proposed detectors outperform existing methods in terms of the quality of the similarity maps, which are assessed using the receiver operating characteristic (ROC) curves, and in terms of the total error rates of the final change detection maps. Furthermore, we proposed a new
similarity measure for automatic change detection based on a divisive normalization transform in order to reduce the computation complexity. Tests show that our proposed DNT-based change detector
exhibits competitive detection performance while achieving lower computational complexity as compared to previously suggested methods.
visual change detection and its applications in multi-temporal synthetic aperture radar (SAR) images.
The Canny edge detector is one of the most widely-used edge detection algorithms due to its superior performance in terms of SNR and edge localization and only one response to a single edge. In this work, we propose a mechanism to implement the Canny algorithm at the block level without any loss in edge detection performance as compared to the original frame-level Canny algorithm. The resulting block-based algorithm has significantly reduced memory requirements and can achieve a significantly reduced latency. Furthermore, the proposed algorithm can be easily integrated with other block-based image processing systems. In addition, quantitative evaluations and subjective tests show that the edge detection performance of the proposed algorithm is better than the original frame-based algorithm, especially when noise is present in the images.
In the context of multi-temporal SAR images for earth monitoring applications, one critical issue is the detection of changes occurring after a natural or anthropic disaster. In this work, we propose a novel similarity measure for automatic change detection using a pair of SAR images
acquired at different times and apply it in both the spatial and wavelet domains. This measure is based on the evolution of the local statistics of the image between two dates. The local statistics are modeled as a Gaussian Mixture Model (GMM), which is more suitable and flexible to approximate the local distribution of the SAR image with distinct land-cover typologies. Tests on real datasets show that the proposed detectors outperform existing methods in terms of the quality of the similarity maps, which are assessed using the receiver operating characteristic (ROC) curves, and in terms of the total error rates of the final change detection maps. Furthermore, we proposed a new
similarity measure for automatic change detection based on a divisive normalization transform in order to reduce the computation complexity. Tests show that our proposed DNT-based change detector
exhibits competitive detection performance while achieving lower computational complexity as compared to previously suggested methods.
ContributorsXu, Qian (Author) / Karam, Lina J (Thesis advisor) / Chakrabarti, Chaitali (Committee member) / Bliss, Daniel (Committee member) / Tepedelenlioğlu, Cihan (Committee member) / Arizona State University (Publisher)
Created2014
Description
Super-Resolution (SR) techniques are widely developed to increase image resolution by fusing several Low-Resolution (LR) images of the same scene to overcome sensor hardware limitations and reduce media impairments in a cost-effective manner. When choosing a solution for the SR problem, there is always a trade-off between computational efficiency and High-Resolution (HR) image quality. Existing SR approaches suffer from extremely high computational requirements due to the high number of unknowns to be estimated in the solution of the SR inverse problem. This thesis proposes efficient iterative SR techniques based on Visual Attention (VA) and perceptual modeling of the human visual system. In the first part of this thesis, an efficient ATtentive-SELective Perceptual-based (AT-SELP) SR framework is presented, where only a subset of perceptually significant active pixels is selected for processing by the SR algorithm based on a local contrast sensitivity threshold model and a proposed low complexity saliency detector. The proposed saliency detector utilizes a probability of detection rule inspired by concepts of luminance masking and visual attention. The second part of this thesis further enhances on the efficiency of selective SR approaches by presenting an ATtentive (AT) SR framework that is completely driven by VA region detectors. Additionally, different VA techniques that combine several low-level features, such as center-surround differences in intensity and orientation, patch luminance and contrast, bandpass outputs of patch luminance and contrast, and difference of Gaussians of luminance intensity are integrated and analyzed to illustrate the effectiveness of the proposed selective SR frameworks. The proposed AT-SELP SR and AT-SR frameworks proved to be flexible by integrating a Maximum A Posteriori (MAP)-based SR algorithm as well as a fast two-stage Fusion-Restoration (FR) SR estimator. By adopting the proposed selective SR frameworks, simulation results show significant reduction on average in computational complexity with comparable visual quality in terms of quantitative metrics such as PSNR, SNR or MAE gains, and subjective assessment. The third part of this thesis proposes a Perceptually Weighted (WP) SR technique that incorporates unequal weighting parameters in the cost function of iterative SR problems. The proposed approach is inspired by the unequal processing of the Human Visual System (HVS) to different local image features in an image. Simulation results show an enhanced reconstruction quality and faster convergence rates when applied to the MAP-based and FR-based SR schemes.
ContributorsSadaka, Nabil (Author) / Karam, Lina J (Thesis advisor) / Spanias, Andreas S (Committee member) / Papandreou-Suppappola, Antonia (Committee member) / Abousleman, Glen P (Committee member) / Goryll, Michael (Committee member) / Arizona State University (Publisher)
Created2011
Description
Due to heterogeneity at the cellular level, single cell analysis (SCA) has become a necessity to study cellomics for the early detection of diseases like cancer. Development of single cell manipulation systems is very critical for performing SCA. In this thesis, electrorotation (ROT) chips to trap and rotate single cells using electrokinetic forces have been developed. The ROT chip mainly consists of a set of closely spaced metal electrodes (60µm interspacing between opposite electrodes) that forms a closed electric field cage (electrocage) when driven with high frequency AC voltages. Cells were flowed through a microchannel to the electrocage where they could be precisely trapped, levitated and rotated in 3-D along the axis of interest. The dielectrophoresis based ROT chip design and relevant electrokinetic effects have been simulated using COMSOL 3.4 to optimize the design parameters. Also, various semiconductor technology fabrication process steps have been developed and optimized for better yield and repeatability in the manufacture of the ROT chip. The ROT chip thus fabricated was used to characterize rotation of single cells with respect to the control parameters namely excitation voltage, frequency and cell line. The longevity of cell rotation under electric fields has been probed. Also, the Joule heating inside the ROT chip due to applied voltage has been characterized to know the thermal stress on the cells. The major advantages of the ROT chip developed are precise electrorotation of cells, simple design and straight forward fabrication process.
ContributorsSoundappa Elango, Iniyan (Author) / Meldrum, Deirdre R (Thesis advisor) / Christen, Jennifer Blain (Committee member) / Johnson, Roger H (Committee member) / Arizona State University (Publisher)
Created2012