Matching Items (13)
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- All Subjects: Computer vision
- All Subjects: Hemodynamics
- Creators: Frakes, David
- Creators: McDaniel, Troy
Description
Image understanding has been playing an increasingly crucial role in vision applications. Sparse models form an important component in image understanding, since the statistics of natural images reveal the presence of sparse structure. Sparse methods lead to parsimonious models, in addition to being efficient for large scale learning. In sparse modeling, data is represented as a sparse linear combination of atoms from a "dictionary" matrix. This dissertation focuses on understanding different aspects of sparse learning, thereby enhancing the use of sparse methods by incorporating tools from machine learning. With the growing need to adapt models for large scale data, it is important to design dictionaries that can model the entire data space and not just the samples considered. By exploiting the relation of dictionary learning to 1-D subspace clustering, a multilevel dictionary learning algorithm is developed, and it is shown to outperform conventional sparse models in compressed recovery, and image denoising. Theoretical aspects of learning such as algorithmic stability and generalization are considered, and ensemble learning is incorporated for effective large scale learning. In addition to building strategies for efficiently implementing 1-D subspace clustering, a discriminative clustering approach is designed to estimate the unknown mixing process in blind source separation. By exploiting the non-linear relation between the image descriptors, and allowing the use of multiple features, sparse methods can be made more effective in recognition problems. The idea of multiple kernel sparse representations is developed, and algorithms for learning dictionaries in the feature space are presented. Using object recognition experiments on standard datasets it is shown that the proposed approaches outperform other sparse coding-based recognition frameworks. Furthermore, a segmentation technique based on multiple kernel sparse representations is developed, and successfully applied for automated brain tumor identification. Using sparse codes to define the relation between data samples can lead to a more robust graph embedding for unsupervised clustering. By performing discriminative embedding using sparse coding-based graphs, an algorithm for measuring the glomerular number in kidney MRI images is developed. Finally, approaches to build dictionaries for local sparse coding of image descriptors are presented, and applied to object recognition and image retrieval.
ContributorsJayaraman Thiagarajan, Jayaraman (Author) / Spanias, Andreas (Thesis advisor) / Frakes, David (Committee member) / Tepedelenlioğlu, Cihan (Committee member) / Turaga, Pavan (Committee member) / Arizona State University (Publisher)
Created2013
Description
There is a growing interest for improved high-accuracy camera calibration methods due to the increasing demand for 3D visual media in commercial markets. Camera calibration is used widely in the fields of computer vision, robotics and 3D reconstruction. Camera calibration is the first step for extracting 3D data from a 2D image. It plays a crucial role in computer vision and 3D reconstruction due to the fact that the accuracy of the reconstruction and 3D coordinate determination relies on the accuracy of the camera calibration to a great extent. This thesis presents a novel camera calibration method using a circular calibration pattern. The disadvantages and issues with existing state-of-the-art methods are discussed and are overcome in this work. The implemented system consists of techniques of local adaptive segmentation, ellipse fitting, projection and optimization. Simulation results are presented to illustrate the performance of the proposed scheme. These results show that the proposed method reduces the error as compared to the state-of-the-art for high-resolution images, and that the proposed scheme is more robust to blur in the imaged calibration pattern.
ContributorsPrakash, Charan Dudda (Author) / Karam, Lina J (Thesis advisor) / Frakes, David (Committee member) / Papandreou-Suppappola, Antonia (Committee member) / Arizona State University (Publisher)
Created2012
Description
Diabetic retinopathy (DR) is a common cause of blindness occurring due to prolonged presence of diabetes. The risk of developing DR or having the disease progress is increasing over time. Despite advances in diabetes care over the years, DR remains a vision-threatening complication and one of the leading causes of blindness among American adults. Recent studies have shown that diagnosis based on digital retinal imaging has potential benefits over traditional face-to-face evaluation. Yet there is a dearth of computer-based systems that can match the level of performance achieved by ophthalmologists. This thesis takes a fresh perspective in developing a computer-based system aimed at improving diagnosis of DR images. These images are categorized into three classes according to their severity level. The proposed approach explores effective methods to classify new images and retrieve clinically-relevant images from a database with prior diagnosis information associated with them. Retrieval provides a novel way to utilize the vast knowledge in the archives of previously-diagnosed DR images and thereby improve a clinician's performance while classification can safely reduce the burden on DR screening programs and possibly achieve higher detection accuracy than human experts. To solve the three-class retrieval and classification problem, the approach uses a multi-class multiple-instance medical image retrieval framework that makes use of spectrally tuned color correlogram and steerable Gaussian filter response features. The results show better retrieval and classification performances than prior-art methods and are also observed to be of clinical and visual relevance.
ContributorsChandakkar, Parag Shridhar (Author) / Li, Baoxin (Thesis advisor) / Turaga, Pavan (Committee member) / Frakes, David (Committee member) / Arizona State University (Publisher)
Created2012
Description
This paper presents the design and evaluation of a haptic interface for augmenting human-human interpersonal interactions by delivering facial expressions of an interaction partner to an individual who is blind using a visual-to-tactile mapping of facial action units and emotions. Pancake shaftless vibration motors are mounted on the back of a chair to provide vibrotactile stimulation in the context of a dyadic (one-on-one) interaction across a table. This work explores the design of spatiotemporal vibration patterns that can be used to convey the basic building blocks of facial movements according to the Facial Action Unit Coding System. A behavioral study was conducted to explore the factors that influence the naturalness of conveying affect using vibrotactile cues.
ContributorsBala, Shantanu (Author) / Panchanathan, Sethuraman (Thesis director) / McDaniel, Troy (Committee member) / Barrett, The Honors College (Contributor) / Computer Science and Engineering Program (Contributor) / Department of Psychology (Contributor)
Created2014-05
Description
Cerebral aneurysms, also known as intracranial aneurysms, are sac-like lesions in the arteries of the brain that can rupture to cause subarachnoid hemorrhaging, damaging and killing brain cells. Metal coil embolization has been traditionally used to occlude and treat cerebral aneurysms to limited success, but polymer embolization has been suggested, because it can provide a greater fraction of occlusion. One such polymer with low cytotoxicity is poly(propylene glycol)diacrylate (PPODA) crosslinked via Michael-type addition with pentaerythritol tetrakis(3-mercaptopropionate) (QT). This study was performed to examine the behavior of PPODA-QT gel in vitro under pulsatile flow emulating physiological conditions. An idealized cerebral aneurysm flow model was designed based on geometries associated with an increase in rupture risk. Pressure was monitored at the apex of the aneurysm dome for varied flow rates and polymer filling fractions of 32.4, 78.2, and 100%. The results indicate that the amount of PPODA-QT deployed into the aneurysm decreases the peak-to-peak oscillation in pressure at the aneurysm wall by an inverse proportion. The 32.4 and 78.2% treatments did not significantly decrease the mean pressure applied to the aneurysm dome, but the 100% treatment greatly reduced it by diverting flow. This study indicates that the maximum filling fraction after swelling of PPODA-QT polymer should be deployed into the aneurysmal sac for treatment.
ContributorsWorkman, Christopher David (Author) / Vernon, Brent (Thesis director) / Frakes, David (Committee member) / Barrett, The Honors College (Contributor) / Department of Chemistry and Biochemistry (Contributor) / Harrington Bioengineering Program (Contributor)
Created2014-05
Description
Intracranial aneurysms are blood \u2014filled sacs along the blood vessels in the brain. These aneurysms can be particularly dangerous due to difficulty in detection and potential lifethreatening outcome. When these aneurysms are detected, there are few treatment options to prevent rupture, one of which is endovascular stents. By placing a stent across the parent vessel, blood flow can be diverted from the aneurysm. Reduced flow reduces the chance of rupture and promotes clotting within the aneurysm. In this study, hemodynamics in idealized basilar tip aneurysm models were investigated at three flow rates using particle imaging velocimetry (PIV). Two models were created with increasing dome size (4mm vs 6mm), and constant dome-to-neck ratio (3:2) and parent vessel contact angle to represent growing aneurysm. With the pulsatile flow, data is acquired at three separate points in the cardiac cycle. Both of the models were studied untreated, treated with Enterprise stent and treated with Pipeline stent. Enterprise stent was developed mainly for structural support while the Pipeline stent was developed as a flow diverter. Due to target functions of the stents, Enterprise stent is more porous than the Pipeline stent. Hemodynamics were studied using a stereo particle image velocimetry technique. The flow in models was characterized by neck and aneurysmal RMS velocity, neck and aneurysm kinetic energy, cross neck flow. It was found that both of the stents are capable diverting flow. Enterprise reduced aneurysmal RMS velocity in model 1 by 38.7% and in model 2 by 76.2%. Pipeline stent reduced aneurysmal RMS velocity in model 1 by 71.4% and in model 2 by 88.1%. Both reductions are data for 3ml/s at peak systole pulsatile flow. Data shows that the Pipeline stent is better than Enterprise stent at reducing flow to the aneurysm.
ContributorsChung, Hanseung (Author) / Frakes, David (Thesis director) / Caplan, Michael (Committee member) / Babiker, Haithem (Committee member) / Barrett, The Honors College (Contributor) / Economics Program in CLAS (Contributor) / Harrington Bioengineering Program (Contributor)
Created2014-05
Description
Cerebral aneurysms are pathological balloonings of blood vessels in the brain, commonly found in the arterial network at the base of the brain. Cerebral aneurysm rupture can lead to a dangerous medical condition, subarachnoid hemorrhage, that is associated with high rates of morbidity and mortality. Effective evaluation and management of cerebral aneurysms is therefore essential to public health. The goal of treating an aneurysm is to isolate the aneurysm from its surrounding circulation, thereby preventing further growth and rupture. Endovascular treatment for cerebral aneurysms has gained popularity over traditional surgical techniques due to its minimally invasive nature and shorter associated recovery time. The hemodynamic modifications that the treatment effects can promote thrombus formation within the aneurysm leading to eventual isolation. However, different treatment devices can effect very different hemodynamic outcomes in aneurysms with different geometries.
Currently, cerebral aneurysm risk evaluation and treatment planning in clinical practice is largely based on geometric features of the aneurysm including the dome size, dome-to-neck ratio, and parent vessel geometry. Hemodynamics, on the other hand, although known to be deeply involved in cerebral aneurysm initiation and progression, are considered to a lesser degree. Previous work in the field of biofluid mechanics has demonstrated that geometry is a driving factor behind aneurysmal hemodynamics.
The goal of this research is to develop a more combined geometric/hemodynamic basis for informing clinical decisions. Geometric main effects were analyzed to quantify contributions made by geometric factors that describe cerebral aneurysms (i.e., dome size, dome-to-neck ratio, and inflow angle) to clinically relevant hemodynamic responses (i.e., wall shear stress, root mean square velocity magnitude and cross-neck flow). Computational templates of idealized bifurcation and sidewall aneurysms were created to satisfy a two-level full factorial design, and examined using computational fluid dynamics. A subset of the computational bifurcation templates was also translated into physical models for experimental validation using particle image velocimetry. The effects of geometry on treatment were analyzed by virtually treating the aneurysm templates with endovascular devices. The statistical relationships between geometry, treatment, and flow that emerged have the potential to play a valuable role in clinical practice.
Currently, cerebral aneurysm risk evaluation and treatment planning in clinical practice is largely based on geometric features of the aneurysm including the dome size, dome-to-neck ratio, and parent vessel geometry. Hemodynamics, on the other hand, although known to be deeply involved in cerebral aneurysm initiation and progression, are considered to a lesser degree. Previous work in the field of biofluid mechanics has demonstrated that geometry is a driving factor behind aneurysmal hemodynamics.
The goal of this research is to develop a more combined geometric/hemodynamic basis for informing clinical decisions. Geometric main effects were analyzed to quantify contributions made by geometric factors that describe cerebral aneurysms (i.e., dome size, dome-to-neck ratio, and inflow angle) to clinically relevant hemodynamic responses (i.e., wall shear stress, root mean square velocity magnitude and cross-neck flow). Computational templates of idealized bifurcation and sidewall aneurysms were created to satisfy a two-level full factorial design, and examined using computational fluid dynamics. A subset of the computational bifurcation templates was also translated into physical models for experimental validation using particle image velocimetry. The effects of geometry on treatment were analyzed by virtually treating the aneurysm templates with endovascular devices. The statistical relationships between geometry, treatment, and flow that emerged have the potential to play a valuable role in clinical practice.
ContributorsNair, Priya (Author) / Frakes, David (Thesis advisor) / Vernon, Brent (Committee member) / Chong, Brian (Committee member) / Pizziconi, Vincent (Committee member) / Adrian, Ronald (Committee member) / Arizona State University (Publisher)
Created2016
Description
Intracranial aneurysms, which form in the blood vessels of the brain, are particularly dangerous because of the importance and fragility of the human brain. When an intracranial aneurysm gets large it poses a significant risk of bursting and causing subarachnoid hemorrhaging (SAH), a possibly fatal condition. One possible treatment involves placing a stent in the vessel to act as a flow diverter. In this study we look at the hemodynamics of two geometries of idealized basilar tip aneurysms, at 2,3, and 4 ml/s pulsatile flow, at three different points in the cardiac cycle. The smaller model had neck and dome diameters of 2.67 mm and 4 mm respectively, while the larger aneurysm had neck and dome diameters of 3 mm and 6 mm respectively. Both diameters and the dome to neck ratio increased in the second model, representing growth over time. Flow was analyzed using stereoscopic particle image velocimetry (PIV) for both geometries in untreated models, as well as after treatment with a high porosity Enterprise stent (Codman and Shurtleff Inc.). Flow in the models was characterized by root mean square velocity in the aneurysm and neck plane, cross neck flow, max aneurysm vorticity, and total aneurysm kinetic energy. It was found that in the smaller aneurysm model (model 1), Enterprise stent treatment reduced all flow parameters substantially. The smallest reduction was in max vorticity, at 42.48%, and the largest in total kinetic energy, at 75.69%. In the larger model (model 2) there was a 52.18% reduction in cross neck flow, but a 167.28% increase in aneurysm vorticity. The other three parameters experienced little change. These results, along with observed velocity vector fields, indicate a noticeable diversion of flow away from the aneurysm in the stent treated model 1. Treatment in model 2 had a small flow diversion effect, but also altered flow in unpredictable ways, in some cases having a detrimental effect on aneurysm hemodynamics. The results of this study indicate that Enterprise stent treatment is only effective in small, relatively undeveloped aneurysm geometries, and waiting until an aneurysm has grown too large can eliminate this treatment option altogether.
ContributorsLindsay, James Bryan (Author) / Frakes, David (Thesis director) / LaBelle, Jeffrey (Committee member) / Nair, Priya (Committee member) / Barrett, The Honors College (Contributor) / School of Humanities, Arts, and Cultural Studies (Contributor)
Created2013-05
Description
The application of novel visualization and modeling methods to the study of cardiovascular disease is vital to the development of innovative diagnostic techniques, including those that may aid in the early detection and prevention of cardiovascular disorders. This dissertation focuses on the application of particle image velocimetry (PIV) to the study of intracardiac hemodynamics. This is accomplished primarily though the use of ultrasound based PIV, which allows for in vivo visualization of intracardiac flow without the requirement for optical access, as is required with traditional camera-based PIV methods.
The fundamentals of ultrasound PIV are introduced, including experimental methods for its implementation as well as a discussion on estimating and mitigating measurement error. Ultrasound PIV is then compared to optical PIV; this is a highly developed technique with proven accuracy; through rigorous examination it has become the “gold standard” of two-dimensional flow visualization. Results show good agreement between the two methods.
Using a mechanical left heart model, a multi-plane ultrasound PIV technique is introduced and applied to quantify a complex, three-dimensional flow that is analogous to the left intraventricular flow. Changes in ventricular flow dynamics due to the rotational orientation of mechanical heart valves are studied; the results demonstrate the importance of multi-plane imaging techniques when trying to assess the strongly three-dimensional intraventricular flow.
The potential use of ultrasound PIV as an early diagnosis technique is demonstrated through the development of a novel elasticity estimation technique. A finite element analysis routine is couple with an ensemble Kalman filter to allow for the estimation of material elasticity using forcing and displacement data derived from PIV. Results demonstrate that it is possible to estimate elasticity using forcing data derived from a PIV vector field, provided vector density is sufficient.
The fundamentals of ultrasound PIV are introduced, including experimental methods for its implementation as well as a discussion on estimating and mitigating measurement error. Ultrasound PIV is then compared to optical PIV; this is a highly developed technique with proven accuracy; through rigorous examination it has become the “gold standard” of two-dimensional flow visualization. Results show good agreement between the two methods.
Using a mechanical left heart model, a multi-plane ultrasound PIV technique is introduced and applied to quantify a complex, three-dimensional flow that is analogous to the left intraventricular flow. Changes in ventricular flow dynamics due to the rotational orientation of mechanical heart valves are studied; the results demonstrate the importance of multi-plane imaging techniques when trying to assess the strongly three-dimensional intraventricular flow.
The potential use of ultrasound PIV as an early diagnosis technique is demonstrated through the development of a novel elasticity estimation technique. A finite element analysis routine is couple with an ensemble Kalman filter to allow for the estimation of material elasticity using forcing and displacement data derived from PIV. Results demonstrate that it is possible to estimate elasticity using forcing data derived from a PIV vector field, provided vector density is sufficient.
ContributorsWesterdale, John Curtis (Author) / Adrian, Ronald (Thesis advisor) / Belohlavek, Marek (Committee member) / Squires, Kyle (Committee member) / Trimble, Steve (Committee member) / Frakes, David (Committee member) / Arizona State University (Publisher)
Created2015
Description
Endowing machines with the ability to understand digital images is a critical task for a host of high-impact applications, including pathology detection in radiographic imaging, autonomous vehicles, and assistive technology for the visually impaired. Computer vision systems rely on large corpora of annotated data in order to train task-specific visual recognition models. Despite significant advances made over the past decade, the fact remains collecting and annotating the data needed to successfully train a model is a prohibitively expensive endeavor. Moreover, these models are prone to rapid performance degradation when applied to data sampled from a different domain. Recent works in the development of deep adaptation networks seek to overcome these challenges by facilitating transfer learning between source and target domains. In parallel, the unification of dominant semi-supervised learning techniques has illustrated unprecedented potential for utilizing unlabeled data to train classification models in defiance of discouragingly meager sets of annotated data.
In this thesis, a novel domain adaptation algorithm -- Domain Adaptive Fusion (DAF) -- is proposed, which encourages a domain-invariant linear relationship between the pixel-space of different domains and the prediction-space while being trained under a domain adversarial signal. The thoughtful combination of key components in unsupervised domain adaptation and semi-supervised learning enable DAF to effectively bridge the gap between source and target domains. Experiments performed on computer vision benchmark datasets for domain adaptation endorse the efficacy of this hybrid approach, outperforming all of the baseline architectures on most of the transfer tasks.
In this thesis, a novel domain adaptation algorithm -- Domain Adaptive Fusion (DAF) -- is proposed, which encourages a domain-invariant linear relationship between the pixel-space of different domains and the prediction-space while being trained under a domain adversarial signal. The thoughtful combination of key components in unsupervised domain adaptation and semi-supervised learning enable DAF to effectively bridge the gap between source and target domains. Experiments performed on computer vision benchmark datasets for domain adaptation endorse the efficacy of this hybrid approach, outperforming all of the baseline architectures on most of the transfer tasks.
ContributorsDudley, Andrew, M.S (Author) / Panchanathan, Sethuraman (Thesis advisor) / Venkateswara, Hemanth (Committee member) / McDaniel, Troy (Committee member) / Arizona State University (Publisher)
Created2019