Slide-free histological imaging by microscopy with ultraviolet surface excitation using speckle illumination

Wong, Hei Man; Zhang, Yan; Chen, Zhenghui; Kang, Lei; Wong, Terence T. W.

doi:10.1364/prj.440935

Cited by 4 publications

(5 citation statements)

References 31 publications

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“…The results of searching for a match with a sample of the 108 th person are as shown in Fig. (9). As is visible, the maximum response is obtained at the 108 th sample i.e.…”

Section: Outputmentioning

confidence: 89%

“…Nguyen [8] examined a number of pre-trained CNN models, such as DenseNet, AlexNet, VGGNet, InceptionNet, and ResNet, in order to extract readily available CNN characteristics for precise iris identification and retina recognition. He Wong et al [9] suggested using ResNet, or the deep residual network, to recognize images. Training the smaller iris datasets can benefit greatly from this architecture's ability to reduce overfitting and improve accuracy.…”

Section: Methodsmentioning

confidence: 99%

“…(8) (9) This process is also repeated to the full 8-bit images (of corrected gamma) in order to yield more such parameters.…”

Section: Statistical Parametersmentioning

confidence: 99%

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Iris Recognition based on Statistically Bound Spatial Domain Zero Crossing and Neural Networks

Sudheesh,

Puttegowda,

Patil

et al. 2024

TOBIOIJ

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Purpose The iris pattern is an important biological feature of the human body. The recognition of an individual based on an iris pattern is gaining more popularity due to the uniqueness of the pattern among the people. Iris recognition systems have received attention very much due to their rich iris texture which gives robust standards for identifying individuals. Notwithstanding this, there are several challenges in unrestricted recognition environments. Methods This article discusses a highly error-resistant technique to implement a biometric recognition system based on the iris portion of the human eye. All iris recognition algorithms of the current day face a major problem of localization errors and the enormous time involved in this localization process. Spatial domain zero crossing may be the simplest and least complex method for localization. Yet, it has not been used due to its high sensitivity to erroneous edges, as a consequence of which more complex and time-consuming algorithms have taken its place. Appropriate statistical bounds imposed on this process help this method to be the least erroneous and time-consuming. Errors were reduced to 0.022% using this approach on the CASIA v1 & v2 datasets. Time consumption in this stage was the least compared to other algorithms. At the comparison stage, most algorithms use multiple comparisons to account for translation and rotation errors. This is time-consuming and very resource-hungry. Results The current approach discusses a robust method based on a single comparison, which works with a correct recognition of over 99.78% which is clearly demonstrated by tests. Conclusions The technique is to use a neural network trained to recognize special statistical and regional parameters unique to every person’s iris. The algorithm also gives sufficient attention to consider illumination errors, elliptical pupils, excess eyelash errors and bad contrast.

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“…The results of searching for a match with a sample of the 108 th person are as shown in Fig. (9). As is visible, the maximum response is obtained at the 108 th sample i.e.…”

Section: Outputmentioning

confidence: 89%

Section: Methodsmentioning

confidence: 99%

See 1 more Smart Citation

Iris Recognition based on Statistically Bound Spatial Domain Zero Crossing and Neural Networks

Sudheesh,

Puttegowda,

Patil

et al. 2024

TOBIOIJ

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“…DUV-excited fluorescence microscopy, also known as MUSE, has been celebrated for its simplicity and effectiveness in concept -the combination of oblique DUV illumination and widefield detection. However, prior published illustrations of real-world implementations [14,15,35,[56][57][58] showed inevitably large and complex setups, with the notable exception of Pocket MUSE, which is generally incompatible with large surgical or mesoscale samples [16]. Also notable in the MUSE literature was the conspicuous omission of imaging capabilities at >40X magnification, a mainstay of biomedical inspection and unusually difficult in MUSE due to the classically awkward implementation of oblique illumination.…”

Section: Discussionmentioning

confidence: 99%

Multi-scale tissue fluorescence mapping with fibre optic ultraviolet excitation and generative modelling

Yong

Tan

et al. 2022

Preprint

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Rapidly resolving microstructure and function from thick biological samples is a critical requirement from basic research to clinical diagnosis. The need for physical sectioning leads to limitations in the speed, cost, or ease of sample preparation and imaging. We introduce a fluorescence microscope with deep ultraviolet illumination via omnidirectional fibre optics, based on the concept of microscopy with ultraviolet surface excitation (MUSE). We further exploited precise control of directional illumination to produce 3D reconstructions of surface microtopography from 2D images. Our technology is applicable to diverse sample types, ranging from single cells and 3D cultures to large tissue samples while utilising conventional, widely available objective lenses in a compact and portable implementation. We demonstrated the use of our microscope with important fluorescent assays through quantitative viability studies in single cells to whole organs, organ-level studies comparable to standard H&E histology, and multiplexed immunohistochemistry staining. We also explored the potential for microtopography to provide additional insight into vascular and tissue texture and heterogeneity studies. This technology fills long-standing technological gaps in both the laboratory and clinic for a general-purpose microscope that is simple, portable, and cheap that can provide near real-time biological insights.

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“…Recently, deep learning techniques have been widely used in several research fields, such as photonics research [ 9 , 10 , 11 ], biological imaging [ 12 , 13 ], material science [ 14 ], and image super-resolution [ 15 , 16 ], all of which demonstrate the advantages of these techniques. Similarly, the strategy based on deep learning has achieved excellent performance in a series of biometric technology problems, such as emotion recognition [ 17 , 18 ], gait recognition [ 19 ], fingerprint recognition [ 20 ], and voice signal recognition [ 21 ].…”

Section: Introductionmentioning

confidence: 99%

Iris Recognition Method Based on Parallel Iris Localization Algorithm and Deep Learning Iris Verification

Wei

Zhang

Zeng

et al. 2022

Sensors

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Biometric recognition technology has been widely used in various fields of society. Iris recognition technology, as a stable and convenient biometric recognition technology, has been widely used in security applications. However, the iris images collected in the actual non-cooperative environment have various noises. Although mainstream iris recognition methods based on deep learning have achieved good recognition accuracy, the intention is to increase the complexity of the model. On the other hand, what the actual optical system collects is the original iris image that is not normalized. The mainstream iris recognition scheme based on deep learning does not consider the iris localization stage. In order to solve the above problems, this paper proposes an effective iris recognition scheme consisting of the iris localization and iris verification stages. For the iris localization stage, we used the parallel Hough circle to extract the inner circle of the iris and the Daugman algorithm to extract the outer circle of the iris, and for the iris verification stage, we developed a new lightweight convolutional neural network. The architecture consists of a deep residual network module and a residual pooling layer which is introduced to effectively improve the accuracy of iris verification. Iris localization experiments were conducted on 400 iris images collected under a non-cooperative environment. Compared with its processing time on a graphics processing unit with a central processing unit architecture, the experimental results revealed that the speed was increased by 26, 32, 36, and 21 times at 4 different iris datasets, respectively, and the effective iris localization accuracy is achieved. Furthermore, we chose four representative iris datasets collected under a non-cooperative environment for the iris verification experiments. The experimental results demonstrated that the network structure could achieve high-precision iris verification with fewer parameters, and the equal error rates are 1.08%, 1.01%, 1.71%, and 1.11% on 4 test databases, respectively.

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Slide-free histological imaging by microscopy with ultraviolet surface excitation using speckle illumination

Cited by 4 publications

References 31 publications

Iris Recognition based on Statistically Bound Spatial Domain Zero Crossing and Neural Networks

Iris Recognition based on Statistically Bound Spatial Domain Zero Crossing and Neural Networks

Multi-scale tissue fluorescence mapping with fibre optic ultraviolet excitation and generative modelling

Iris Recognition Method Based on Parallel Iris Localization Algorithm and Deep Learning Iris Verification

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