An estimated 82 million American adults have one or more type of cardiovascular diseases (CVD). CVD is the leading
cause of death (1 of every 3 deaths) in the United States. When considered separately from other CVDs, stroke ranks
third among all causes of death behind diseases of the heart and cancer. Stroke accounts for 1 out of every 18 deaths and
is the leading cause of serious long-term disability in the United States.
Motion estimation of ultrasound videos (US) of carotid artery (CA) plaques provides important information regarding
plaque deformation that should be considered for distinguishing between symptomatic and asymptomatic plaques. In this
paper, we present the development of verifiable methods for the estimation of plaque motion. Our methodology is tested
on a set of 34 (5 symptomatic and 29 asymptomatic) ultrasound videos of carotid artery plaques.
Plaque and wall motion analysis provides information about plaque instability and is used in an attempt to differentiate
between symptomatic and asymptomatic cases. The final goal for motion estimation and analysis is to identify
pathological conditions that can be detected from motion changes due to changes in tissue stiffness.
The lurking epidemic of eye diseases caused by diabetes and aging will put more than 130 million Americans at risk of
blindness by 2020. Screening has been touted as a means to prevent blindness by identifying those individuals at risk.
However, the cost of most of today's commercial retinal imaging devices makes their use economically impractical for
mass screening. Thus, low cost devices are needed. With these devices, low cost often comes at the expense of image
quality with high levels of noise and distortion hindering the clinical evaluation of those retinas.
A software-based super resolution (SR) reconstruction methodology that produces images with improved resolution and
quality from multiple low resolution (LR) observations is introduced. The LR images are taken with a low-cost Scanning
Laser Ophthalmoscope (SLO). The non-redundant information of these LR images is combined to produce a single
image in an implementation that also removes noise and imaging distortions while preserving fine blood vessels and
small lesions.
The feasibility of using the resulting SR images for screening of eye diseases was tested using quantitative and
qualitative assessments. Qualitatively, expert image readers evaluated their ability of detecting clinically significant
features on the SR images and compared their findings with those obtained from matching images of the same eyes
taken with commercially available high-end cameras. Quantitatively, measures of image quality were calculated from
SR images and compared to subject-matched images from a commercial fundus imager. Our results show that the SR
images have indeed enough quality and spatial detail for screening purposes.
In the United States and most of the western world, the leading causes of vision impairment and blindness are age-related
macular degeneration (AMD), diabetic retinopathy (DR), and glaucoma. In the last decade, research in automatic
detection of retinal lesions associated with eye diseases has produced several automatic systems for detection and
screening of AMD, DR, and glaucoma. However. advanced, sight-threatening stages of DR and AMD can present with
lesions not commonly addressed by current approaches to automatic screening. In this paper we present an automatic eye
screening system based on multiscale Amplitude Modulation-Frequency Modulation (AM-FM) decompositions that
addresses not only the early stages, but also advanced stages of retinal and optic nerve disease. Ten different experiments
were performed in which abnormal features such as neovascularization, drusen, exudates, pigmentation abnormalities,
geographic atrophy (GA), and glaucoma were classified. The algorithm achieved an accuracy detection range of [0.77 to
0.98] area under the ROC curve for a set of 810 images. When set to a specificity value of 0.60, the sensitivity of the
algorithm to the detection of abnormal features ranged between 0.88 and 1.00. Our system demonstrates that, given an
appropriate training set, it is possible to use a unique algorithm to detect a broad range of eye diseases.
Motion estimation from digital video is an ill-posed problem that requires a regularization approach. Regularization
introduces a smoothness constraint that can reduce the resolution of the velocity estimates. The problem is further
complicated for ultrasound videos (US), where speckle noise levels can be significant. Motion estimation using optical
flow models requires the modification of several parameters to satisfy the optical flow constraint as well as the level of
imposed smoothness. Furthermore, except in simulations or mostly unrealistic cases, there is no ground truth to use for
validating the velocity estimates. This problem is present in all real video sequences that are used as input to motion
estimation algorithms. It is also an open problem in biomedical applications like motion analysis of US of carotid artery
(CA) plaques. In this paper, we study the problem of obtaining reliable ultrasound video motion estimates for
atherosclerotic plaques for use in clinical diagnosis.
A global optimization framework for motion parameter optimization is presented. This framework uses actual carotid
artery motions to provide optimal parameter values for a variety of motions and is tested on ten different US videos
using two different motion estimation techniques.
Researchers have sought to gain greater insight into the mechanisms of the retina and the optic disc at high spatial
resolutions that would enable the visualization of small structures such as photoreceptors and nerve fiber bundles. The
sources of retinal image quality degradation are aberrations within the human eye, which limit the achievable resolution
and the contrast of small image details. To overcome these fundamental limitations, researchers have been applying
adaptive optics (AO) techniques to correct for the aberrations. Today, deformable mirror based adaptive optics devices
have been developed to overcome the limitations of standard fundus cameras, but at prices that are typically unaffordable
for most clinics. In this paper we demonstrate a clinically viable fundus camera with auto-focus and astigmatism
correction that is easy to use and has improved resolution. We have shown that removal of low-order aberrations results
in significantly better resolution and quality images. Additionally, through the application of image restoration and
super-resolution techniques, the images present considerably improved quality. The improvements lead to enhanced
visualization of retinal structures associated with pathology.
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