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OIPAV:眼科图像处理、分析和可视化的集成软件系统。

OIPAV: an Integrated Software System for Ophthalmic Image Processing, Analysis, and Visualization.

机构信息

School of Electronics and Information Engineering, Soochow University, No.1 Shizi Street, Suzhou, Jiangsu Province, 215006, China.

出版信息

J Digit Imaging. 2019 Feb;32(1):183-197. doi: 10.1007/s10278-017-0047-6.

Abstract

Ophthalmic medical images, such as optical coherence tomography (OCT) images and color photo of fundus, provide valuable information for clinical diagnosis and treatment of ophthalmic diseases. In this paper, we introduce a software system specially oriented to ophthalmic images processing, analysis, and visualization (OIPAV) to assist users. OIPAV is a cross-platform system built on a set of powerful and widely used toolkit libraries. Based on the plugin mechanism, the system has an extensible framework. It provides rich functionalities including data I/O, image processing, interaction, ophthalmic diseases detection, data analysis, and visualization. By using OIPAV, users can easily access to the ophthalmic image data manufactured from different imaging devices, facilitate workflows of processing ophthalmic images, and improve quantitative evaluations. With a satisfying function scalability and expandability, the software is applicable for both ophthalmic researchers and clinicians.

摘要

眼科医学图像,如光学相干断层扫描(OCT)图像和眼底彩色照片,为眼科疾病的临床诊断和治疗提供了有价值的信息。在本文中,我们介绍了一个专门面向眼科图像处理、分析和可视化(OIPAV)的软件系统,以帮助用户。OIPAV 是一个跨平台系统,建立在一组强大且广泛使用的工具包库之上。基于插件机制,该系统具有可扩展的框架。它提供了丰富的功能,包括数据输入/输出、图像处理、交互、眼科疾病检测、数据分析和可视化。通过使用 OIPAV,用户可以轻松访问来自不同成像设备的眼科图像数据,方便眼科图像的处理工作流程,并提高定量评估的效果。该软件具有令人满意的功能可扩展性和可扩展性,适用于眼科研究人员和临床医生。

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本文引用的文献

1
Quantitative analysis of retinal layers on three-dimensional spectral-domain optical coherence tomography for pituitary adenoma.
PLoS One. 2017 Jun 19;12(6):e0179532. doi: 10.1371/journal.pone.0179532. eCollection 2017.
2
Choroid Neovascularization Growth Prediction With Treatment Based on Reaction-Diffusion Model in 3-D OCT Images.
IEEE J Biomed Health Inform. 2017 Nov;21(6):1667-1674. doi: 10.1109/JBHI.2017.2702603. Epub 2017 May 16.
3
A Framework for Classification and Segmentation of Branch Retinal Artery Occlusion in SD-OCT.
IEEE Trans Image Process. 2017 Jul;26(7):3518-3527. doi: 10.1109/TIP.2017.2697762. Epub 2017 Apr 25.
5
Automatic detection of microaneurysms in retinal fundus images.
Comput Med Imaging Graph. 2017 Jan;55:106-112. doi: 10.1016/j.compmedimag.2016.08.001. Epub 2016 Aug 4.
6
Enhanced low-rank + sparsity decomposition for speckle reduction in optical coherence tomography.
J Biomed Opt. 2016 Jul 1;21(7):76008. doi: 10.1117/1.JBO.21.7.076008.
9
Profile and Determinants of Retinal Optical Intensity in Normal Eyes with Spectral Domain Optical Coherence Tomography.
PLoS One. 2016 Feb 10;11(2):e0148183. doi: 10.1371/journal.pone.0148183. eCollection 2016.
10
Comparison of Retinal Thickness Measurements between the Topcon Algorithm and a Graph-Based Algorithm in Normal and Glaucoma Eyes.
PLoS One. 2015 Jun 4;10(6):e0128925. doi: 10.1371/journal.pone.0128925. eCollection 2015.

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