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Applying t-SNE to Estimate Image Sharpness of Low-cost Nailfold Capillaroscopy

Hung-Hsiang Wang1, Chih-Ping Chen2,*

1 Department of Industrial Design, National Taipei University of Technology, Taipei, Taiwan
2 College of Design, National Taipei University of Technology, Taipei, Taiwan

* Corresponding Author: Chih-Ping Chen. Email:

(This article belongs to this Special Issue: Soft Computing Methods for Intelligent Automation Systems)

Intelligent Automation & Soft Computing 2022, 32(1), 237-254.


Machine learning can classify the image clarity of low-cost nailfold capillaroscopy (NC) and can be applied to the design verification for other medical devices. The method can be beneficial for systems that require a large number of image datasets. This investigation covers the design, integration, image sharpness estimation, and deconvolution sharpening of the NC. The study applies this device to record two videos and extract 600 photos, including blurry and sharp images. It then uses the Laplace operator method for blur detection of the pictures. Statistics are recorded for each image’s Laplace score and the distribution of clear photos in NC. In this investigation, an algorithm called t-distributed stochastic neighbor embedding (t-SNE) is introduced into the NC image sharpness estimation issue. The t-SNE is employed as a data visualization tool to determine the cluster distribution of sharp images. The result shows the t-SNE method to classify sharp images of NC is close to the method combine statistic and Laplace operator for sharp image distribution. And the image captured from the low-cost NC that applying deconvolution can sharpen the image. It shows that the NC equipment combines with low-cost hardware and software computing advantages has broader, powerful, and complex applications. For long-term purposes, it is conducive for detecting people who have Raynaud’s phenomenon in rural areas of developing countries to realize the vision of health equity.


Cite This Article

H. Wang and C. Chen, "Applying t-sne to estimate image sharpness of low-cost nailfold capillaroscopy," Intelligent Automation & Soft Computing, vol. 32, no.1, pp. 237–254, 2022.

This work is licensed under a Creative Commons Attribution 4.0 International License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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