Dr. Zheen Zhao Profile

Dr. Zheen Zhao

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Proceedings Article | 1 April 2008 Paper

A tool for computer-aided diagnosis of retinopathy of prematurity

Zheen Zhao, David Wallace, Sharon Freedman, Stephen Aylward

Proceedings Volume 6915, 69152W (2008) https://doi.org/10.1117/12.769030

KEYWORDS: Blood vessels, Photography, Computer aided diagnosis and therapy, Diagnostics, Eye, Image analysis, Standards development, Computer simulations, Reconstruction algorithms, Retina

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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 automatically. 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.

Proceedings Article | 29 April 2005 Paper

A novel framework for automated 3D PDM construction using deformable models

Zheen Zhao, Eam Khwang Teoh

Proceedings Volume 5747, (2005) https://doi.org/10.1117/12.594736

KEYWORDS: 3D modeling, Bridges, Image segmentation, 3D image processing, Binary data, Brain, Data modeling, Bone, Statistical modeling, Tissues

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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.

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