Fully automated extraction of the fringe skeletons in dynamic electronic speckle pattern interferometry using a U-Net convolutional neural network

Li, Biyuan; Tang, Chen; Zheng, Tingyue; Lei, Zhenkun

doi:10.1117/1.oe.58.2.023105

Cited by 10 publications

(8 citation statements)

References 27 publications

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“…Almost all semantic segmentation networks have an encoder-decoder architecture to capture location information of an object. [24], [15]. The encoder reduces spatial resolution with pooling layers and the decoder recovers the spatial resolution.…”

Section: Semantic Segmentationmentioning

confidence: 99%

“…Accurate and robust lesion detection is a key component of an automated medical diagnosis system [1], [2], [3], [4], [5], [6], [7], [8]. Notable achievements in deep learning have benefited several research trials in medical image analysis, [9], [10], [11], [12], [13], [14], [15], [16] and the most recent major lesion detection algorithms are based on convolutional neural networks. In particular, semantic segmentation methods such as U-Net [17] allow for precise lesion detection with respect to intensity and shape variations.…”

Section: Introductionmentioning

confidence: 99%

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X-ray Image Segmentation using Multi-task Learning

Park¹,

Jeong²,

Moon³

2020

KSII TIIS

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The chest X-rays are a common way to diagnose lung cancer or pneumonia. In particular, the finding of a lung nodule is the most important problem in the early detection of lung cancer. Recently, a lot of automatic diagnosis algorithms have been studied to find the lung nodules missed by doctors. The algorithms are typically based on segmentation network like U-Net. However, the occurrence of false positives that similar to lung nodules present outside the lungs can severely degrade performance. In this study, we propose a multi-task learning method that simultaneously learns the lung region and nodule-labeled data based on the prior knowledge that lung nodules exist only in the lung. The proposed method significantly reduces false positives outside the lung and improves the recognition rate of lung nodules to 83.8 F1 score compared to 66.6 F1 score of single task learning with U-net model. The experimental results on the JSRT public dataset demonstrate the effectiveness of the proposed method compared with other baseline methods.

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Section: Semantic Segmentationmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

X-ray Image Segmentation using Multi-task Learning

Park¹,

Jeong²,

Moon³

2020

KSII TIIS

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“…In this paper, the noise model that was developed by [31,32] is adopted to quantitatively analyze the noise-induced phase error in phase-shifting algorithm. Usually, the noise is far less than the projected intensity.…”

Section: Gaussian Noise-induced Phase Errormentioning

confidence: 99%

“…At the same time, lots of hybrid methods based on PDEs had also been studied, such as fuzzy C-means [27,28], Hessian matrix [29], and shearlet transform [30]. Li [31] proposed a method for multi-frame fringe patterns processing based on convolutional neural network (CNN) in order to extract the fringe skeletons in ESPI. Partial differential equations, especially OPDEs and fourth-order OPDEs, have been demonstrated to be powerful in preserving the details of ESPI fringe patterns while filtering.…”

Section: Introductionmentioning

confidence: 99%

Fringe Phase-Shifting Field Based Fuzzy Quotient Space-Oriented Partial Differential Equations Filtering Method for Gaussian Noise-Induced Phase Error

Xue

et al. 2019

Sensors

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Traditional filtering methods only focused on improving the peak signal-to-noise ratio of the single fringe pattern, which ignore the filtering effect on phase extraction. Fringe phase-shifting field based fuzzy quotient space-oriented partial differential equations filtering method is proposed to reduce the phase error caused by Gaussian noise while filtering. First, the phase error distribution that is caused by Gaussian noise is analyzed. Furthermore, by introducing the fringe phase-shifting field and the theory of fuzzy quotient space, the modified filtering direction can be adaptively obtained, which transforms the traditional single image filtering into multi-image filtering. Finally, the improved fourth-order oriented partial differential equations with fidelity item filtering method is established. Experiments demonstrated that the proposed method achieves a higher signal-to-noise ratio and lower phase error caused by noise, while also retaining more edge details.

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“…To date, the machine learning method has not been widely used in the application related with InSAR technology. Li et al [36] proposed an intelligent method to achieve fully automated extraction of the fringe skeletons in Electronic Speckle Pattern Interferometry (ESPI) based on a U-Net convolutional neural network, which was used to reach the goal of intelligent fringe processing in optical interferometry. Anantrasirichai et al [37] first adopted machine learning approaches to detect volcanic deformation in large data sets.…”

Section: Introductionmentioning

confidence: 99%

Ground Deformation Analysis Using InSAR and Backpropagation Prediction with Influencing Factors in Erhai Region, China

Wang

Guo

et al. 2019

Sustainability

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The long continuity of Interferometric Synthetic Aperture Radar (InSAR) can provide high space and resolution data for ground deformation investigations. The ground deformation in this paper appeared in the city’s development, although it is close to the Erhai region, which is different from a water-deficient city. Therefore, the analysis and prediction of ground deformation using a new method is required. In this study, Sentinel-1 Synthetic Aperture Radar (SAR) images from 2015 to 2018 were used to study the characteristics of ground deformation in the Erhai region using the Small Baseline Subset Interferometric SAR (SBAS-InSAR) technique. The results were cross-validated using ascending and descending direction images to ensure the accuracy. In addition, the results showed that there was little ground deformation in the northern part of the Erhai region, while there was obvious ground deformation in the southern part. Four influencing factors—including the building area, water level, cumulative precipitation, and cumulative temperature of the southern Erhai region—were used together to predict the cumulative ground deformation using back-propagation (BP). The R of all the involved data was 0.966, and the root mean square errors (RMSEs) between the simulated values using BP and the true measured values were 3.063, 1.003, and 1.119, respectively. The results showed that BP has great potential in predicting the change tendency of ground deformation with high precision. The main reason for ground deformation is the continuous increase of building area; the water level followed. The cumulative precipitation and cumulative temperature are the reasons for the seasonal ground deformation. Some countermeasures and suggestions are given to face the challenge of serious ground deformation.

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Fully automated extraction of the fringe skeletons in dynamic electronic speckle pattern interferometry using a U-Net convolutional neural network

Cited by 10 publications

References 27 publications

X-ray Image Segmentation using Multi-task Learning

X-ray Image Segmentation using Multi-task Learning

Fringe Phase-Shifting Field Based Fuzzy Quotient Space-Oriented Partial Differential Equations Filtering Method for Gaussian Noise-Induced Phase Error

Ground Deformation Analysis Using InSAR and Backpropagation Prediction with Influencing Factors in Erhai Region, China

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