Development of an ultrasound microscope combined with optical microscope for multiparametric characterization of a single cell

Arakawa, Mototaka; Shikama, Joe; Yoshida, Koki; Nagaoka, Ryo; Kobayashi, Kazuto; Saijo, Yoshifumi

doi:10.1109/tuffc.2014.006865

Cited by 11 publications

(3 citation statements)

References 33 publications

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“…Arakawa et al used the combination of ultrasound microscopy and optical microscopy for the multiparameter characterization of single cells. They believe that this fusion has further enhanced people's understanding of cell biomechanics [2]. Schultheiss and Denil analyzed the history of microscopy and the development of microsurgery in their research, calling it a revolution in reproductive tract surgery [3].…”

Section: Introductionmentioning

confidence: 99%

Focusing Algorithm of Automatic Control Microscope Based on Digital Image Processing

Zhang

2021

Journal of Sensors

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When performing digital image processing, the most critical technology that affects its use effect is the autofocus technology. With the advancement of science and the development of computer technology, autofocus technology has become more and more widely used in various fields. Autofocus technology is a key technology in robot vision and digital video systems. In order to allow digital image processing technology to better serve humans, it is necessary to further improve the focus evaluation function algorithm. This article focuses on the imaging principle of defocused images, using different evaluation functions to analyze and process the experimental images to observe the changes in image clarity. Through the introduction and analysis of the existing evaluation function, it can be known that the focus evaluation function will directly affect the quality of digital image processing. Therefore, it is best to choose unimodality, unbiasedness, low noise sensitivity, wide coverage, and a small amount of calculation. For the evaluation function, the Laplacian gradient function is an ideal choice. However, because the current digital image processing technology is not perfect enough, the focus function is still prone to multiextreme problems when the image is severely defocused and the high-frequency components in the image are missing; the balance between image processing speed and focus accuracy also still needs to be improved. Therefore, this paper studies the autocontrol microscope focus algorithm based on digital image processing, analyzes the principle of visual image imaging, and makes some improvements to the microscope focus algorithm. Through experiments, it can be seen that the real-time data of the original Laplace function in the edge-obvious target is 76.9, and it reaches 77.6 after improvement. The improved algorithm can better maintain the single-peak state during the focusing process, which improves the image processing efficiency while ensuring the measurement accuracy.

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Section: Introductionmentioning

confidence: 99%

Focusing Algorithm of Automatic Control Microscope Based on Digital Image Processing

Zhang

2021

Journal of Sensors

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“…Meanwhile, it is difficult to complete large‐area and real‐time control with only a few microscopes in the field. Apart from this, this method relies too much on human experience . But it is undeniable that the microscope observation method is a convenient and practical method for detecting rice diseases in daily life .…”

Section: Introductionmentioning

confidence: 99%

“…Apart from this, this method relies too much on human experience. 13 But it is undeniable that the microscope observation method is a convenient and practical method for detecting rice diseases in daily life. 14 The image recognition method, based on morphological features and machine learning, can complete identification while capturing a large number of microparticle images.…”

Section: Introductionmentioning

confidence: 99%

A rapid rice blast detection and identification method based on crop disease spores' diffraction fingerprint texture

Yang

Wang

et al. 2020

J Sci Food Agric

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BACKGROUND Rice blast fungus is a worldwide disease, and it is one of the most serious rice diseases in the north and south rice fields in China. The initial symptoms of rice blast are not obvious, and the speed of transmission is fast. Manual identification is time‐consuming and laborious. At present, it is a great challenge to realize rapid and accurate early identification of rice blast. RESULTS In this paper, an identification method based on crop disease spores' diffraction fingerprint texture for rice blast was studied; this method utilizes the light field and texture features of diffraction images. To verify the reliability of the model that we proposed, we selected two methods of manual identification and machine recognition to compare and detect rice blast spores. The experimental results show that the identification of light diffraction characteristics is not only higher than the traditional manual recognition by microscope (increased by more than 0.3%), but also faster after neural network training (increased by more than 90%). The diffraction recognition method used in this study, based on crop disease spores' diffraction fingerprint texture, can be completed in a few seconds, and its test accuracy is 97.18%. CONCLUSION The proposed method, a rapid rice blast detection and identification method based on crop disease spores' diffraction fingerprint texture, has certain advantages compared with the existing manual identification by microscope. This method can be applied to the recognition of rice blast in agricultural research. © 2020 Society of Chemical Industry

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Beyond Conventional Medical Ultrasound Imaging

Saijo,

Hermawan,

Ikeda

et al. 2023

Mathematics for Industry

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Development of an ultrasound microscope combined with optical microscope for multiparametric characterization of a single cell

Cited by 11 publications

References 33 publications

Focusing Algorithm of Automatic Control Microscope Based on Digital Image Processing

Focusing Algorithm of Automatic Control Microscope Based on Digital Image Processing

A rapid rice blast detection and identification method based on crop disease spores' diffraction fingerprint texture

Beyond Conventional Medical Ultrasound Imaging

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