In this paper we present improvements to a software application, named ROPtool, that aids in the timely and
accurate detection and diagnosis of retinopathy of prematurity (ROP).
ROP occurs in 68% of infants less than 1251 grams at birth, and it is a leading cause of blindness for
prematurely born infants. The standard of care for its diagnosis is the subjective assessment of retinal vessel
dilation and tortuosity. There is significant inter-observer variation in those assessments.
ROPtool analyzes retinal images, extracts user-selected blood vessels from those images, and quantifies the
tortuosity of those vessels. The presence of ROP is then gauged by comparing the tortuosity of an infant's retinal
vessels with measures made from a clinical-standard image of severely tortuous retinal vessels. The presence of
such tortuous retinal vessels is referred to as 'plus disease'.
In this paper, a novel metric of tortuosity is proposed. From the ophthalmologist's point of view, the new
metric is an improvement from our previously published algorithm, since it uses smooth curves instead of straight
lines to simulate 'normal vessels'.
Another advantage of the new ROPtool is that minimal user interactions are required. ROPtool utilizes
a ridge traversal algorithm to extract retinal vessels. The algorithm reconstructs connectivity along a vessel
This paper supports its claims by reporting ROC curves from a pilot study involving 20 retinal images. The
areas under two ROC curves, from two experts in ROP, using the new metric to diagnose 'tortuosity sufficient
for plus disease', varied from 0.86 to 0.91.
This paper describes a novel framework to build 3D Point Distribution Model (PDM) from a set of segmented volumetric images. This method is based on a deformable model algorithm. Each training sample deforms to approximate all other training shapes. The training sample with best approximation results is then chosen as the template. Finally, the poor approximation results from this template are improved by the "bridge over" scheme, which deforms the template to approximate intermediate training shapes and then deforms the approximations to outliers. The method is applied to construct a 3D PDM of 20 human brain ventricles. The results show that the algorithm leads to more accurate representation than traditional framework. Also, the performance of the PDM of soft tissue is comparable with the PDM of bone structures by a previous method. The traditional framework of deformable model based approach selects the template arbitrarily and deforms the template to approximate training shapes directly. The limitation of the traditional framework is that the representation accuracy of the PDM entirely depends on the direct approximation. Moreover, the arbitrary template selection deteriorates the accuracy of the approximation. Our framework that features template selection and indirect approximation solves the shortcomings and improves the PDM representation accuracy. Furthermore, the "bridge over" framework could be used with any deformable model algorithm. In this sense, the method is a generic framework open to future investigation.