FocusALL: Focal Stacking of Microscopic Images Using Modified Harris Corner Response Measure

Sigdel, Madhu S.; Sigdel, Madhav; Dinç, Semih; Dinc, Imren; Pusey, Marc L.; Aygün, Ramazan

doi:10.1109/tcbb.2015.2459685

Cited by 31 publications

(13 citation statements)

References 23 publications

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“…TDOA is a technology derived from TOA. Its main advantage is to overcome the problem of electronic clock time synchronization, that is, it does not require a strictly synchronized electronic clock [21,22]. Assuming there are two signals with different propagation speeds, the calculation formula of distance R is as follows:…”

Section: Fusion Modelmentioning

confidence: 99%

[Retracted] Multimode Intelligent Control Based on Multidata Fusion Filtering in High‐Speed Train Traffic Signal and Control

Huang

2021

Journal of Sensors

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As the speed of high-speed trains continues to increase, the intelligent monitoring of high-speed trains has become a concern of people. This research mainly discusses the application of multimode intelligent control of multidata fusion filtering in high-speed train traffic signal and control. In multimodal intelligent control, BangBang, PI control, adaptive fuzzy PID control, and expert monitoring control under special circumstances can be used, respectively, according to the error and the rate of change of the error, which can achieve the best control effect under safe conditions. Take the allowable speed of ATP as the target speed of the control system, and combine the operation process, operation requirements, traction characteristics, braking characteristics of high-speed trains, and meet the two conditions for improving the operating efficiency of high-speed trains. According to the dynamic expected speed value of high-speed trains, dynamically adjust the switching threshold. This study uses a pulse signal generator to simulate the speed data of the vehicle speed sensor (all pulse data), and then read the speed (pulse) signal data through the pulse signal acquisition card, and display the simulated speed data under the Kingview software. The monitoring computer is used to collect train speed information, display speed information, manage speed information, and output speed information. Then, through OPC technology, the simulation speed data is transmitted to MATLAB software for multidata fusion filtering processing and multimodal control simulation. In the simulation process, the train adopts a multimodal intelligent control response scheme, with a total time of 2183.7 s, which is shortened by 214.5 s and improved by nearly 10%. The multimode intelligent control scheme of multidata fusion filtering proposed in this study can better meet the control of high-speed train traffic signals.

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Section: Fusion Modelmentioning

confidence: 99%

[Retracted] Multimode Intelligent Control Based on Multidata Fusion Filtering in High‐Speed Train Traffic Signal and Control

Huang

2021

Journal of Sensors

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“…Particularly, all these approaches can be classified into three groups: spatial domain (based on pixel value), transform domain (based on frequency components), and deep learning methods. Spatial domain image fusion methods select pixels or regions from the focused parts in the spatial domain to compose the final image [ 10 , 11 ]. Different transform domain methods are used for image resolution enhancement through the transformation of the source image into different scales, which then are composed into one fused image [ 12 ].…”

Section: Related Workmentioning

confidence: 99%

“…All these methods can be used to obtain the best focus image from a set of captured microscopic images, but the performance time is the essential factor for the embryo selection task. Speed of microscopic image processing by focus stacking is highlighted by many researchers [ 11 , 20 ], but not less relevant are image spectral information, features to be retrieved or similarity based metrics. For our problem, the latter metrics are important as far as it can affect the correct embryo identification regarding the number of cells.…”

Section: Related Workmentioning

confidence: 99%

Fast Multi-Focus Fusion Based on Deep Learning for Early-Stage Embryo Image Enhancement

Raudonis

Paulauskaitė-Tarasevičienė

Šutienė

2021

Sensors

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Background: Cell detection and counting is of essential importance in evaluating the quality of early-stage embryo. Full automation of this process remains a challenging task due to different cell size, shape, the presence of incomplete cell boundaries, partially or fully overlapping cells. Moreover, the algorithm to be developed should process a large number of image data of different quality in a reasonable amount of time. Methods: Multi-focus image fusion approach based on deep learning U-Net architecture is proposed in the paper, which allows reducing the amount of data up to 7 times without losing spectral information required for embryo enhancement in the microscopic image. Results: The experiment includes the visual and quantitative analysis by estimating the image similarity metrics and processing times, which is compared to the results achieved by two wellknown techniques—Inverse Laplacian Pyramid Transform and Enhanced Correlation Coefficient Maximization. Conclusion: Comparatively, the image fusion time is substantially improved for different image resolutions, whilst ensuring the high quality of the fused image.

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“…Usually, crystal images are expected to have distinguishable features such as high intensity, sharp clear edges, and proper geometric shapes. However, in some cases, these features may not be dominant due to focusing or reflection problems even if there is a protein crystal in the image [4]. Therefore, a single type of thresholding technique may not provide an informative binary image for classifying images.…”

Section: Introductionmentioning

confidence: 99%

Super-Thresholding: Supervised Thresholding of Protein Crystal Images

Dinc

Dinç

Sigdel

et al. 2017

IEEE/ACM Trans. Comput. Biol. and Bioinf.

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In general, a single thresholding technique is developed or enhanced to separate foreground objects from background for a domain of images. This idea may not generate satisfactory results for all images in a dataset, since different images may require different types of thresholding methods for proper binarization or segmentation. To overcome this limitation, in this study, we propose a novel approach called “super-thresholding” that utilizes a supervised classifier to decide an appropriate thresholding method for a specific image. This method provides a generic framework that allows selection of the best thresholding method among different thresholding techniques that are beneficial for the problem domain. A classifier model is built using features extracted priori from the original image only or posteriori by analyzing the outputs of thresholding methods and the original image. This model is applied to identify the thresholding method for new images of the domain. We performed our method on protein crystallization images, and then we compared our results with 6 thresholding techniques. Numerical results are provided using 4 different correctness measurements. Super-thresholding outperforms the best single thresholding method around 10%, and it gives the best performance for protein crystallization dataset in our experiments.

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FocusALL: Focal Stacking of Microscopic Images Using Modified Harris Corner Response Measure

Cited by 31 publications

References 23 publications

[Retracted] Multimode Intelligent Control Based on Multidata Fusion Filtering in High‐Speed Train Traffic Signal and Control

[Retracted] Multimode Intelligent Control Based on Multidata Fusion Filtering in High‐Speed Train Traffic Signal and Control

Fast Multi-Focus Fusion Based on Deep Learning for Early-Stage Embryo Image Enhancement

Super-Thresholding: Supervised Thresholding of Protein Crystal Images

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