Diabetic retinopathy (DR) affects more than 4.4 million Americans age 40 and over. Automatic screening for DR has shown to be an efficient and cost-effective way to lower the burden on the healthcare system, by triaging diabetic patients and ensuring timely care for those presenting with DR. Several supervised algorithms have been developed to detect pathologies related to DR, but little work has been done in determining the size of the training set that optimizes an algorithm’s performance. In this paper we analyze the effect of the training sample size on the performance of a top-down DR screening algorithm for different types of statistical classifiers. Results are based on
partial least squares (PLS), support vector machines (SVM), k-nearest neighbor (kNN), and Naïve Bayes classifiers. Our dataset consisted of digital retinal images collected from a total of 745 cases (595 controls, 150 with DR). We varied the number of normal controls in the training set, while keeping the number of DR samples constant, and repeated the procedure 10 times using randomized training sets to avoid bias. Results show increasing performance in terms of area under the ROC curve (AUC) when the number of DR subjects in the training set increased, with similar trends for each of the classifiers. Of these, PLS and k-NN had the highest average AUC. Lower standard deviation and a flattening of the AUC curve gives evidence that there is a limit to the learning ability of the classifiers and an optimal number of cases to train on.
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.
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.
This paper presents an image processing technique for automatically categorize age-related macular degeneration
(AMD) phenotypes from retinal images. Ultimately, an automated approach will be much more precise and consistent in
phenotyping of retinal diseases, such as AMD. We have applied the automated phenotyping to retina images from a
cohort of mono- and dizygotic twins. The application of this technology will allow one to perform more quantitative
studies that will lead to a better understanding of the genetic and environmental factors associated with diseases such as
AMD. A method for classifying retinal images based on features derived from the application of amplitude-modulation
frequency-modulation (AM-FM) methods is presented. Retinal images from identical and fraternal twins who presented
with AMD were processed to determine whether AM-FM could be used to differentiate between the two types of twins.
Results of the automatic classifier agreed with the findings of other researchers in explaining the variation of the disease
between the related twins. AM-FM features classified 72% of the twins correctly. Visual grading found that genetics
could explain between 46% and 71% of the variance.
Diabetic retinopathy (DR) is one of the leading causes of blindness among adult Americans. Automatic
methods for detection of the disease have been developed in recent years, most of them addressing the
segmentation of bright and red lesions. In this paper we present an automatic DR screening system that does
approach the problem through the segmentation of features. The algorithm determines non-diseased retinal
images from those with pathology based on textural features obtained using multiscale Amplitude
Modulation-Frequency Modulation (AM-FM) decompositions. The decomposition is represented as features
that are the inputs to a classifier. The algorithm achieves 0.88 area under the ROC curve (AROC) for a set of
280 images from the MESSIDOR database. The algorithm is then used to analyze the effects of image
compression and degradation, which will be present in most actual clinical or screening environments.
Results show that the algorithm is insensitive to illumination variations, but high rates of compression and
large blurring effects degrade its performance.
Access to the requested content is limited to institutions that have purchased or subscribe to SPIE eBooks.
You are receiving this notice because your organization may not have SPIE eBooks access.*
*Shibboleth/Open Athens users─please
sign in
to access your institution's subscriptions.
To obtain this item, you may purchase the complete book in print or electronic format on
SPIE.org.
INSTITUTIONAL Select your institution to access the SPIE Digital Library.
PERSONAL Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.