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(1 - 9 of 9)
- Title
- DESIGN AND ASSESSMENT OF COMPACT OPTICAL SYSTEMS TOWARDS SPECIAL EFFECTS IMAGING.
- Creator
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Chaoulov, Vesselin, Rolland, Jannick, University of Central Florida
- Abstract / Description
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A main challenge in the field of special effects is to create special effects in real time in a way that the user can preview the effect before taking the actual picture or movie sequence. There are many techniques currently used to create computer-simulated special effects, however current techniques in computer graphics do not provide the option for the creation of real-time texture synthesis. Thus, while computer graphics is a powerful tool in the field of special effects, it is neither...
Show moreA main challenge in the field of special effects is to create special effects in real time in a way that the user can preview the effect before taking the actual picture or movie sequence. There are many techniques currently used to create computer-simulated special effects, however current techniques in computer graphics do not provide the option for the creation of real-time texture synthesis. Thus, while computer graphics is a powerful tool in the field of special effects, it is neither portable nor does it provide work in real-time capabilities. Real-time special effects may, however, be created optically. Such approach will provide not only real-time image processing at the speed of light but also a preview option allowing the user or the artist to preview the effect on various parts of the object in order to optimize the outcome. The work presented in this dissertation was inspired by the idea of optically created special effects, such as painterly effects, encoded in images captured by photographic or motion picture cameras. As part of the presented work, compact relay optics was assessed, developed, and a working prototype was built. It was concluded that even though compact relay optics can be achieved, further push for compactness and cost-effectiveness was impossible in the paradigm of bulk macro-optics systems. Thus, a paradigm for imaging with multi-aperture micro-optics was proposed and demonstrated for the first time, which constitutes one of the key contributions of this work. This new paradigm was further extended to the most general case of magnifying multi-aperture micro-optical systems. Such paradigm allows an extreme reduction in size of the imaging optics by a factor of about 10 and a reduction in weight by a factor of about 500. Furthermore, an experimental quantification of the feasibility of optically created special effects was completed, and consequently raytracing software was developed, which was later commercialized by SmARTLens(TM). While the art forms created via raytracing were powerful, they did not predict all effects acquired experimentally. Thus, finally, as key contribution of this work, the principles of scalar diffraction theory were applied to optical imaging of extended objects under quasi-monochromatic incoherent illumination in order to provide a path to more accurately model the proposed optical imaging process for special effects obtained in the hardware. The existing theoretical framework was generalized to non-paraxial in- and out-of-focus imaging and results were obtained to verify the generalized framework. In the generalized non-paraxial framework, even the most complex linear systems, without any assumptions for shift invariance, can be modeled and analyzed.
Show less - Date Issued
- 2005
- Identifier
- CFE0000513, ucf:46447
- Format
- Document (PDF)
- PURL
- http://purl.flvc.org/ucf/fd/CFE0000513
- Title
- Learning Algorithms for Fat Quantification and Tumor Characterization.
- Creator
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Hussein, Sarfaraz, Bagci, Ulas, Shah, Mubarak, Heinrich, Mark, Pensky, Marianna, University of Central Florida
- Abstract / Description
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Obesity is one of the most prevalent health conditions. About 30% of the world's and over 70% of the United States' adult populations are either overweight or obese, causing an increased risk for cardiovascular diseases, diabetes, and certain types of cancer. Among all cancers, lung cancer is the leading cause of death, whereas pancreatic cancer has the poorest prognosis among all major cancers. Early diagnosis of these cancers can save lives. This dissertation contributes towards the...
Show moreObesity is one of the most prevalent health conditions. About 30% of the world's and over 70% of the United States' adult populations are either overweight or obese, causing an increased risk for cardiovascular diseases, diabetes, and certain types of cancer. Among all cancers, lung cancer is the leading cause of death, whereas pancreatic cancer has the poorest prognosis among all major cancers. Early diagnosis of these cancers can save lives. This dissertation contributes towards the development of computer-aided diagnosis tools in order to aid clinicians in establishing the quantitative relationship between obesity and cancers. With respect to obesity and metabolism, in the first part of the dissertation, we specifically focus on the segmentation and quantification of white and brown adipose tissue. For cancer diagnosis, we perform analysis on two important cases: lung cancer and Intraductal Papillary Mucinous Neoplasm (IPMN), a precursor to pancreatic cancer. This dissertation proposes an automatic body region detection method trained with only a single example. Then a new fat quantification approach is proposed which is based on geometric and appearance characteristics. For the segmentation of brown fat, a PET-guided CT co-segmentation method is presented. With different variants of Convolutional Neural Networks (CNN), supervised learning strategies are proposed for the automatic diagnosis of lung nodules and IPMN. In order to address the unavailability of a large number of labeled examples required for training, unsupervised learning approaches for cancer diagnosis without explicit labeling are proposed. We evaluate our proposed approaches (both supervised and unsupervised) on two different tumor diagnosis challenges: lung and pancreas with 1018 CT and 171 MRI scans respectively. The proposed segmentation, quantification and diagnosis approaches explore the important adiposity-cancer association and help pave the way towards improved diagnostic decision making in routine clinical practice.
Show less - Date Issued
- 2018
- Identifier
- CFE0007196, ucf:52288
- Format
- Document (PDF)
- PURL
- http://purl.flvc.org/ucf/fd/CFE0007196
- Title
- OPTIMIZING THE PERFORMANCE OF AS-MANUFACTURED GRAZING INCIDENCE X-RAY TELESCOPES USING MOSAIC DETECTOR ARRAYS.
- Creator
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Atanassova, Martina, Harvey, James, University of Central Florida
- Abstract / Description
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The field of X-ray astronomy is only forty (43) years old, and grazing incidence X-ray telescopes have only been conceived and designed for a little over fifty (50) years. The Wolter Type I design is particularly well suited for stellar astronomical telescopes (very small field-of-view). The first orbiting X-ray observatory, HEAO-1 was launched in 1977, a mere twenty-eight (28) years ago. Since that time large nested Wolter Type I X-ray telescopes have been designed, build, and launched by...
Show moreThe field of X-ray astronomy is only forty (43) years old, and grazing incidence X-ray telescopes have only been conceived and designed for a little over fifty (50) years. The Wolter Type I design is particularly well suited for stellar astronomical telescopes (very small field-of-view). The first orbiting X-ray observatory, HEAO-1 was launched in 1977, a mere twenty-eight (28) years ago. Since that time large nested Wolter Type I X-ray telescopes have been designed, build, and launched by the European Space Agency (ROSAT) and NASA (the Chandra Observatory). Several smaller grazing incidence telescopes have been launched for making solar observations (SOHO, HESP, SXI). These grazing incidence designs tend to suffer from severe aberrations and at these very short wavelengths scattering effects from residual optical fabrication errors are another major source of image degradation. The fabrication of precision optical surfaces for grazing incidence X-ray telescopes thus poses a great technological challenge. Both the residual "figure" errors and the residual microroughness or "finish" of the manufactured mirrors must be precisely measured, and the image degradation due to these fabrication errors must be accurately modeled in order to predict the final optical performance of the as manufactured telescope. The fabrication process thus consists of a series of polishing and testing cycles with the predictions from the metrology data of each cycle indicating the strategy for the next polishing cycle. Most commercially available optical design and analysis software analyzes the image degradation effects of diffraction and aberrations, but does not adequately model the image degradation effects of surface scatter or the effects of state-of-the-art mosaic detectors. The work presented in this dissertation is in support of the Solar X-ray Imager (SXI) program. We have developed a rigorous procedure by which to analyze detector effects in systems which exhibit severe field-dependent aberrations (conventional transfer function analysis is not applicable). Furthermore, we developed a technique to balance detector effects with geometrical aberrations, during the design process, for wide-field applications. We then included these detector effects in a complete systems engineering analysis (including the effects of diffraction, geometrical aberrations, surface scatter effects, the mirror manufacturer error budget tree, and detector effects) of image quality for the five SXI telescopes being fabricated for NOAA's next generation GOES weather satellites. In addition we have re-optimized the remaining optical design parameters after the grazing incidence SXI mirrors have been imperfectly fabricated. This ability depends critically upon the adoption of an image quality criterion, or merit function, appropriate for the specific application. In particular, we discuss in detail how the focal plane position can be adjusted to optimize the optical performance of the telescope to best compensate for optical figure and/or finish errors resulting from the optical fabrication process. Our systems engineering analysis was then used to predict the increase in performance achieved by the re-optimization procedure. The image quality predictions are also compared with real X-ray test data from the SXI program to experimentally validate our system engineering analysis capability.
Show less - Date Issued
- 2005
- Identifier
- CFE0000428, ucf:46387
- Format
- Document (PDF)
- PURL
- http://purl.flvc.org/ucf/fd/CFE0000428
- Title
- A COMPARATIVE ANALYSIS OF COLLEGE STUDENT SPRING BREAK DESTINATIONS: AN EMPIRICAL STUDY OF TOURISM DESTINATION ATTRIBUTES.
- Creator
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Tang, Tricia, Choi, Youngsoo, University of Central Florida
- Abstract / Description
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The tourism industry has become one of the fastest growing sectors in the world's economy, contributing 9.1% of world GDP and more than 260 million jobs worldwide (World Travel & Tourism Council, 2011). The U.S college student market has emerged as major segment within this sector, generating approximately $15 billion on annual domestic and international travel. Among the various travel patterns of college students, they are most highly motivated for spring break travel, with more than two...
Show moreThe tourism industry has become one of the fastest growing sectors in the world's economy, contributing 9.1% of world GDP and more than 260 million jobs worldwide (World Travel & Tourism Council, 2011). The U.S college student market has emerged as major segment within this sector, generating approximately $15 billion on annual domestic and international travel. Among the various travel patterns of college students, they are most highly motivated for spring break travel, with more than two million students traveling per season (Bai et al., 2004; Borgerding, 2001; Reynolds, 2004). This research, through surveying college students majoring in hospitality and tourism management, analyzed the significance of college student perceptions of key spring break destination attributes. A total of 281 usable responses were subjected to the Principal Component Analysis that generated six dimensions: Breaking Away, Sun and Beach, Safety and Hygiene, Psychological Distance, Price and Value, and Social Exploration, comprised of 24 key attributes that influence a college spring breaker's destination selection decision. An Importance-Performance Analysis (Martilla & James, 1977) was conducted based on the respondents' assessment of attributes on five of the six dimensions. The results of the IPA allowed comparison of the top four most visited destinations identified by the respondents: Daytona Beach, South Beach Miami, Panama City Beach, and Clearwater Beach/ Tampa. The study findings may provide valuable implications for destination service providers to improve their destination's appeal in this highly competitive and lucrative market. Future research on college spring break groups located in different geographic locations within the country is highly encouraged to better understand the general characteristics of this market.
Show less - Date Issued
- 2012
- Identifier
- CFH0004193, ucf:44837
- Format
- Document (PDF)
- PURL
- http://purl.flvc.org/ucf/fd/CFH0004193
- Title
- The Mechanical Response and Parametric Optimization of Ankle-Foot Devices.
- Creator
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Smith, Kevin, Gordon, Ali, Kassab, Alain, Bai, Yuanli, Pabian, Patrick, University of Central Florida
- Abstract / Description
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To improve the mobility of lower limb amputees, many modern prosthetic ankle-foot devices utilize a so called energy storing and return (ESAR) design. This allows for elastically stored energy to be returned to the gait cycle as forward propulsion. While ESAR type feet have been well accepted by the prosthetic community, the design and selection of a prosthetic device for a specific individual is often based on clinical feedback rather than engineering design. This is due to an incomplete...
Show moreTo improve the mobility of lower limb amputees, many modern prosthetic ankle-foot devices utilize a so called energy storing and return (ESAR) design. This allows for elastically stored energy to be returned to the gait cycle as forward propulsion. While ESAR type feet have been well accepted by the prosthetic community, the design and selection of a prosthetic device for a specific individual is often based on clinical feedback rather than engineering design. This is due to an incomplete understanding of the role of prosthetic design characteristics (e.g. stiffness, roll-over shape, etc.) have on the gait pattern of an individual. Therefore, the focus of this work has been to establish a better understanding of the design characteristics of existing prosthetic devices through mechanical testing and the development of a prototype prosthetic foot that has been numerically optimized for a specific gait pattern. The component stiffness, viscous properties, and energy return of commonly prescribed carbon fiber ESAR type feet were evaluated through compression testing with digital image correlation at select loading angles following the idealized gait from the ISO 22675 standard for fatigue testing. A representative model was developed to predict the stress within each of the tested components during loading and to optimize the design for a target loading response through parametric finite element analysis. This design optimization approach, along with rapid prototyping technologies, will allow clinicians to better identify the role the design characteristics of the foot have on an amputee's biomechanics during future gait analysis.
Show less - Date Issued
- 2016
- Identifier
- CFE0006397, ucf:51502
- Format
- Document (PDF)
- PURL
- http://purl.flvc.org/ucf/fd/CFE0006397
- Title
- Automatic Detection of Brain Functional Disorder Using Imaging Data.
- Creator
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Dey, Soumyabrata, Shah, Mubarak, Jha, Sumit, Hu, Haiyan, Weeks, Arthur, Rao, Ravishankar, University of Central Florida
- Abstract / Description
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Recently, Attention Deficit Hyperactive Disorder (ADHD) is getting a lot of attention mainly for two reasons. First, it is one of the most commonly found childhood behavioral disorders. Around 5-10% of the children all over the world are diagnosed with ADHD. Second, the root cause of the problem is still unknown and therefore no biological measure exists to diagnose ADHD. Instead, doctors need to diagnose it based on the clinical symptoms, such as inattention, impulsivity and hyperactivity,...
Show moreRecently, Attention Deficit Hyperactive Disorder (ADHD) is getting a lot of attention mainly for two reasons. First, it is one of the most commonly found childhood behavioral disorders. Around 5-10% of the children all over the world are diagnosed with ADHD. Second, the root cause of the problem is still unknown and therefore no biological measure exists to diagnose ADHD. Instead, doctors need to diagnose it based on the clinical symptoms, such as inattention, impulsivity and hyperactivity, which are all subjective.Functional Magnetic Resonance Imaging (fMRI) data has become a popular tool to understand the functioning of the brain such as identifying the brain regions responsible for different cognitive tasks or analyzing the statistical differences of the brain functioning between the diseased and control subjects. ADHD is also being studied using the fMRI data. In this dissertation we aim to solve the problem of automatic diagnosis of the ADHD subjects using their resting state fMRI (rs-fMRI) data.As a core step of our approach, we model the functions of a brain as a connectivity network, which is expected to capture the information about how synchronous different brain regions are in terms of their functional activities. The network is constructed by representing different brain regions as the nodes where any two nodes of the network are connected by an edge if the correlation of the activity patterns of the two nodes is higher than some threshold. The brain regions, represented as the nodes of the network, can be selected at different granularities e.g. single voxels or cluster of functionally homogeneous voxels. The topological differences of the constructed networks of the ADHD and control group of subjects are then exploited in the classification approach.We have developed a simple method employing the Bag-of-Words (BoW) framework for the classification of the ADHD subjects. We represent each node in the network by a 4-D feature vector: node degree and 3-D location. The 4-D vectors of all the network nodes of the training data are then grouped in a number of clusters using K-means; where each such cluster is termed as a word. Finally, each subject is represented by a histogram (bag) of such words. The Support Vector Machine (SVM) classifier is used for the detection of the ADHD subjects using their histogram representation. The method is able to achieve 64% classification accuracy.The above simple approach has several shortcomings. First, there is a loss of spatial information while constructing the histogram because it only counts the occurrences of words ignoring the spatial positions. Second, features from the whole brain are used for classification, but some of the brain regions may not contain any useful information and may only increase the feature dimensions and noise of the system. Third, in our study we used only one network feature, the degree of a node which measures the connectivity of the node, while other complex network features may be useful for solving the proposed problem.In order to address the above shortcomings, we hypothesize that only a subset of the nodes of the network possesses important information for the classification of the ADHD subjects. To identify the important nodes of the network we have developed a novel algorithm. The algorithm generates different random subset of nodes each time extracting the features from a subset to compute the feature vector and perform classification. The subsets are then ranked based on the classification accuracy and the occurrences of each node in the top ranked subsets are measured. Our algorithm selects the highly occurring nodes for the final classification. Furthermore, along with the node degree, we employ three more node features: network cycles, the varying distance degree and the edge weight sum. We concatenate the features of the selected nodes in a fixed order to preserve the relative spatial information. Experimental validation suggests that the use of the features from the nodes selected using our algorithm indeed help to improve the classification accuracy. Also, our finding is in concordance with the existing literature as the brain regions identified by our algorithms are independently found by many other studies on the ADHD. We achieved a classification accuracy of 69.59% using this approach. However, since this method represents each voxel as a node of the network which makes the number of nodes of the network several thousands. As a result, the network construction step becomes computationally very expensive. Another limitation of the approach is that the network features, which are computed for each node of the network, captures only the local structures while ignore the global structure of the network.Next, in order to capture the global structure of the networks, we use the Multi-Dimensional Scaling (MDS) technique to project all the subjects from an unknown network-space to a low dimensional space based on their inter-network distance measures. For the purpose of computing distance between two networks, we represent each node by a set of attributes such as the node degree, the average power, the physical location, the neighbor node degrees, and the average powers of the neighbor nodes. The nodes of the two networks are then mapped in such a way that for all pair of nodes, the sum of the attribute distances, which is the inter-network distance, is minimized. To reduce the network computation cost, we enforce that the maximum relevant information is preserved with minimum redundancy. To achieve this, the nodes of the network are constructed with clusters of highly active voxels while the activity levels of the voxels are measured based on the average power of their corresponding fMRI time-series. Our method shows promise as we achieve impressive classification accuracies (73.55%) on the ADHD-200 data set. Our results also reveal that the detection rates are higher when classification is performed separately on the male and female groups of subjects.So far, we have only used the fMRI data for solving the ADHD diagnosis problem. Finally, we investigated the answers of the following questions. Do the structural brain images contain useful information related to the ADHD diagnosis problem? Can the classification accuracy of the automatic diagnosis system be improved combining the information of the structural and functional brain data? Towards that end, we developed a new method to combine the information of structural and functional brain images in a late fusion framework. For structural data we input the gray matter (GM) brain images to a Convolutional Neural Network (CNN). The output of the CNN is a feature vector per subject which is used to train the SVM classifier. For the functional data we compute the average power of each voxel based on its fMRI time series. The average power of the fMRI time series of a voxel measures the activity level of the voxel. We found significant differences in the voxel power distribution patterns of the ADHD and control groups of subjects. The Local binary pattern (LBP) texture feature is used on the voxel power map to capture these differences. We achieved 74.23% accuracy using GM features, 77.30% using LBP features and 79.14% using combined information.In summary this dissertation demonstrated that the structural and functional brain imaging data are useful for the automatic detection of the ADHD subjects as we achieve impressive classification accuracies on the ADHD-200 data set. Our study also helps to identify the brain regions which are useful for ADHD subject classification. These findings can help in understanding the pathophysiology of the problem. Finally, we expect that our approaches will contribute towards the development of a biological measure for the diagnosis of the ADHD subjects.
Show less - Date Issued
- 2014
- Identifier
- CFE0005786, ucf:50060
- Format
- Document (PDF)
- PURL
- http://purl.flvc.org/ucf/fd/CFE0005786
- Title
- Specialty Fiber Lasers and Novel Fiber Devices.
- Creator
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Jollivet, Clemence, Schulzgen, Axel, Moharam, Jim, Richardson, Martin, Mafi, Arash, University of Central Florida
- Abstract / Description
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At the Dawn of the 21st century, the field of specialty optical fibers experienced a scientific revolution with the introduction of the stack-and-draw technique, a multi-steps and advanced fiber fabrication method, which enabled the creation of well-controlled micro-structured designs. Since then, an extremely wide variety of finely tuned fiber structures have been demonstrated including novel materials and novel designs. As the complexity of the fiber design increased, highly-controlled...
Show moreAt the Dawn of the 21st century, the field of specialty optical fibers experienced a scientific revolution with the introduction of the stack-and-draw technique, a multi-steps and advanced fiber fabrication method, which enabled the creation of well-controlled micro-structured designs. Since then, an extremely wide variety of finely tuned fiber structures have been demonstrated including novel materials and novel designs. As the complexity of the fiber design increased, highly-controlled fabrication processes became critical. To determine the ability of a novel fiber design to deliver light with properties tailored according to a specific application, several mode analysis techniques were reported, addressing the recurring needs for in-depth fiber characterization. The first part of this dissertation details a novel experiment that was demonstrated to achieve modal decomposition with extended capabilities, reaching beyond the limits set by the existing mode analysis techniques. As a result, individual transverse modes carrying between ~0.01% and ~30% of the total light were resolved with unmatched accuracy. Furthermore, this approach was employed to decompose the light guided in Large-Mode Area (LMA) fiber, Photonic Crystal Fiber (PCF) and Leakage Channel Fiber (LCF). The single-mode performances were evaluated and compared. As a result, the suitability of each specialty fiber design to be implemented for power-scaling applications of fiber laser systems was experimentally determined.The second part of this dissertation is dedicated to novel specialty fiber laser systems. First, challenges related to the monolithic integration of novel and complex specialty fiber designs in all-fiber systems were addressed. The poor design and size compatibility between specialty fibers and conventional fiber-based components limits their monolithic integration due to high coupling loss and unstable performances. Here, novel all-fiber Mode-Field Adapter (MFA) devices made of selected segments of Graded Index Multimode Fiber (GIMF) were implemented to mitigate the coupling losses between a LMA PCF and a conventional Single-Mode Fiber (SMF), presenting an initial 18-fold mode-field area mismatch. It was experimentally demonstrated that the overall transmission in the mode-matched fiber chain was increased by more than 11 dB (the MFA was a 250 ?m piece of 50 ?m core diameter GIMF). This approach was further employed to assemble monolithic fiber laser cavities combining an active LMA PCF and fiber Bragg gratings (FBG) in conventional SMF. It was demonstrated that intra-cavity mode-matching results in an efficient (60%) and narrow-linewidth (200 pm) laser emission at the FBG wavelength.In the last section of this dissertation, monolithic Multi-Core Fiber (MCF) laser cavities were reported for the first time. Compared to existing MCF lasers, renown for high-brightness beam delivery after selection of the in-phase supermode, the present new generation of 7-coupled-cores Yb-doped fiber laser uses the gain from several supermodes simultaneously. In order to uncover mode competition mechanisms during amplification and the complex dynamics of multi-supermode lasing, novel diagnostic approaches were demonstrated. After characterizing the laser behavior, the first observations of self-mode-locking in linear MCF laser cavities were discovered.
Show less - Date Issued
- 2014
- Identifier
- CFE0005354, ucf:50491
- Format
- Document (PDF)
- PURL
- http://purl.flvc.org/ucf/fd/CFE0005354
- Title
- Sampling and Subspace Methods for Learning Sparse Group Structures in Computer Vision.
- Creator
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Jaberi, Maryam, Foroosh, Hassan, Pensky, Marianna, Gong, Boqing, Qi, GuoJun, Pensky, Marianna, University of Central Florida
- Abstract / Description
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The unprecedented growth of data in volume and dimension has led to an increased number of computationally-demanding and data-driven decision-making methods in many disciplines, such as computer vision, genomics, finance, etc. Research on big data aims to understand and describe trends in massive volumes of high-dimensional data. High volume and dimension are the determining factors in both computational and time complexity of algorithms. The challenge grows when the data are formed of the...
Show moreThe unprecedented growth of data in volume and dimension has led to an increased number of computationally-demanding and data-driven decision-making methods in many disciplines, such as computer vision, genomics, finance, etc. Research on big data aims to understand and describe trends in massive volumes of high-dimensional data. High volume and dimension are the determining factors in both computational and time complexity of algorithms. The challenge grows when the data are formed of the union of group-structures of different dimensions embedded in a high-dimensional ambient space.To address the problem of high volume, we propose a sampling method referred to as the Sparse Withdrawal of Inliers in a First Trial (SWIFT), which determines the smallest sample size in one grab so that all group-structures are adequately represented and discovered with high probability. The key features of SWIFT are: (i) sparsity, which is independent of the population size; (ii) no prior knowledge of the distribution of data, or the number of underlying group-structures; and (iii) robustness in the presence of an overwhelming number of outliers. We report a comprehensive study of the proposed sampling method in terms of accuracy, functionality, and effectiveness in reducing the computational cost in various applications of computer vision. In the second part of this dissertation, we study dimensionality reduction for multi-structural data. We propose a probabilistic subspace clustering method that unifies soft- and hard-clustering in a single framework. This is achieved by introducing a delayed association of uncertain points to subspaces of lower dimensions based on a confidence measure. Delayed association yields higher accuracy in clustering subspaces that have ambiguities, i.e. due to intersections and high-level of outliers/noise, and hence leads to more accurate self-representation of underlying subspaces. Altogether, this dissertation addresses the key theoretical and practically issues of size and dimension in big data analysis.
Show less - Date Issued
- 2018
- Identifier
- CFE0007017, ucf:52039
- Format
- Document (PDF)
- PURL
- http://purl.flvc.org/ucf/fd/CFE0007017
- Title
- The Identification and Segmentation of Astrocytoma Prior to Critical Mass, by means of a Volumetric/Subregion Regression Analysis of Normal and Neoplastic Brain Tissue.
- Creator
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Higgins, Lyn, Hughes, Charles, Morrow, Patricia Bockelman, Bagci, Ulas, Lisle, Curtis, University of Central Florida
- Abstract / Description
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As the underlying cause of Glioblastoma Multiforme (GBM) is presently unclear, this research implements a new approach to identifying and segmenting plausible instances of GBM prior to critical mass. Grade-IV Astrocytoma, or GBM, is an aggressive and malignant cancer arising from star-shaped glial cells, or astrocytes, where the astrocytes, functionally, assist in the support and protection of neurons within the central nervous system and spinal cord. Subsequently, our motivation for...
Show moreAs the underlying cause of Glioblastoma Multiforme (GBM) is presently unclear, this research implements a new approach to identifying and segmenting plausible instances of GBM prior to critical mass. Grade-IV Astrocytoma, or GBM, is an aggressive and malignant cancer arising from star-shaped glial cells, or astrocytes, where the astrocytes, functionally, assist in the support and protection of neurons within the central nervous system and spinal cord. Subsequently, our motivation for researching the ability to recognize GBM is that the underlying cause of the mutation is presently unclear, leading to the operative that GBM is only detectable through a combination of MRI and CT brain scans, cooperatively, along with a resection biopsy. Since astrocytoma only becomes evident at critical mass, when the cellular structure of the neoplasm becomes visible within the image, this research seeks to achieve earlier identification and segmentation of the neoplasm by evaluating the malignant area via a volumetric voxel approach to removing noise artifacts and analyzing voxel differentials. In order to investigate neoplasm continuity, a differential approach has been implemented utilizing a multi-polynomial/multi-domain regression algorithm, thus, ultimately, providing a graphical and mathematical analysis of the differentials within critical mass and non-critical mass images. Given these augmentations to MRI and CT image rectifications, we theorize that our approach will improve on astrocytoma recognition and segmentation, along with achieving greater accuracy in diagnostic evaluations of the malignant area.
Show less - Date Issued
- 2018
- Identifier
- CFE0007336, ucf:52111
- Format
- Document (PDF)
- PURL
- http://purl.flvc.org/ucf/fd/CFE0007336
