Using Convolutional Neural Networks to Detect and Extract Retinal Blood Vessels in Fundoscopic Images

Standfield, Benjamin; Chen, Wei-Bang; Wang, Yujuan; Lu, Yue; Abdelzaher, Ahmed F.; Wang, Xiaoliang; Yang, Xin-Guang

doi:10.1109/mipr.2019.00047

Cited by 4 publications

(1 citation statement)

References 21 publications

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“…ContainingmuchlessparametersthanAlexNetandVGG16,theInceptionarchitecture ofGoogLeNetperformswellevenunderstrictconstraintsonmemoryandcomputational budget (Szegedy, 2015;Szegedy, Vanhoucke, Ioffe, Shlens, & Wojna, 2016). The savingofcomputationalcostisachievedbyintroducingchannelconcatenationand1 ×1convolution"bottleneck"layers.ThetotalnumberofparametersofGoogLeNet is only 5 million, greatly reduced from AlexNet and VGG16.…”

Section: Inception Netmentioning

confidence: 99%

A Fully Automated Porosity Measure for Thermal Barrier Coating Images

Chen

Standfield

Gao

et al. 2018

International Journal of Multimedia Data Engineering and Management

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Thermal barrier coating (TBC), a widely used advanced manufacturing technique in various industries, provides thermal insulation and surface protection to a substrate by spraying melted coating materials on to the surface of the substrate. This article is an extended version of a previously published work. To quantify microstructures in the TBC, the authors introduce a fully automated image analysis-based TBC porosity measure (TBCPM) framework which includes 1) top coat layer (TCL) detection module, and 2) microstructure recognition and porosity measure module. The first module is designed to automatically identify the TCL in a TBC image using a histogram-based approach. The second module recognizes the microstructures in the TCL using a local thresholding-based method. This article extends the previous work by introducing convolutional neural networks (CNNs) to enhance the performance of the second module. The experimental results show that the CNN-based methods outperform local thresholding-based methods, and results of the proposed porosity measure are comparable to that of the domain experts.

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Section: Inception Netmentioning

confidence: 99%