Human iris three-dimensional imaging at micron resolution by a micro-plenoptic camera

Chen, Hao; Woodward, Maria A.; Burke, David T.; Jeganathan, V Swetha E; Demirci, Hakan; Sick, Volker

doi:10.1364/boe.8.004514

Cited by 10 publications

(6 citation statements)

References 23 publications

(25 reference statements)

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“…More recently a plenoptic sensor has been used to successfully characterize the topography of the healthy and diseased human iris in vivo. 13 In this proceeding we present the design principle of a plenoptic fundus camera and an example of the implementation of such a system equipped with a commercial 3D light-field camera. We show that with a single light-field snapshot we can digitally refocus between the retina and a tool located in front of the retina or display an extended depth of field image where everything is in focus.…”

Section: Conjugatementioning

confidence: 99%

Retinal fundus imaging with a plenoptic sensor

Thurin

Bloch

Nousias

et al. 2018

Ophthalmic Technologies XXVIII

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Vitreoretinal surgery is moving towards 3D visualization of the surgical field. This require acquisition system capable of recording such 3D information. We propose a proof of concept imaging system based on a light-field camera where an array of micro-lenses is placed in front of a conventional sensor. With a single snapshot, a stack of images focused at different depth are produced on the fly, which provides enhanced depth perception for the surgeon. Difficulty in depth localization of features and frequent focus-change during surgery are making current vitreoretinal heads-up surgical imaging systems cumbersome to use. To improve the depth perception and eliminate the need to manually refocus on the instruments during the surgery, we designed and implemented a proof-of-concept ophthalmoscope equipped with a commercial light-field camera. The sensor of our camera is composed of an array of micro-lenses which are projecting an array of overlapped micro-images. We show that with a single light-field snapshot we can digitally refocus between the retina and a tool located in front of the retina or display an extended depth-of-field image where everything is in focus. The design and system performances of the plenoptic fundus camera are detailed. We will conclude by showing in vivo data recorded with our device.

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Section: Conjugatementioning

confidence: 99%

Retinal fundus imaging with a plenoptic sensor

Thurin

Bloch

Nousias

et al. 2018

Ophthalmic Technologies XXVIII

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“…Its accuracy can reach the micron level. Considering its dynamic measurement capabilities, Chen et al [ 92 ] used a light field camera for human iris measurements to enhance the measurement of the iris as a dynamic tissue compared to the commonly used methods of 2D cross-sectional optical coherence tomography (OCT) or ultrasound images. A depth resolution of 10 µm can be achieved within a depth of field of 2.4 mm.…”

Section: Measurement Application Of Light Field Cameramentioning

confidence: 99%

Measurement Technologies of Light Field Camera: An Overview

Hu,

Li,

Miao

et al. 2023

Sensors

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Visual measurement methods are extensively used in various fields, such as aerospace, biomedicine, agricultural production, and social life, owing to their advantages of high speed, high accuracy, and non-contact. However, traditional camera-based measurement systems, relying on the pinhole imaging model, face challenges in achieving three-dimensional measurements using a single camera by one shot. Moreover, traditional visual systems struggle to meet the requirements of high precision, efficiency, and compact size simultaneously. With the development of light field theory, the light field camera has garnered significant attention as a novel measurement method. Due to its special structure, the light field camera enables high-precision three-dimensional measurements with a single camera through only one shot. This paper presents a comprehensive overview of light field camera measurement technologies, including the imaging principles, calibration methods, reconstruction algorithms, and measurement applications. Additionally, we explored future research directions and the potential application prospects of the light field camera.

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“…There are various methods for obtaining an iris 3D model. The method in the work of Chen et al 12 uses a microplenoptic camera to capture the three‐dimensional (3D) topography of the anterior iris surface. This method provides 3D resolution of about 10‐80 μm capture of the ridges and folds of the iris.…”

Section: D Iris Synthesismentioning

confidence: 99%

3D iris model and reader for iris identification

Cohen

Sowmithran

2020

Concurrency and Computation

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In the presence of eyelids and eyelashes movement, pupil dilation, poor lighting, blur due to movement during iris image acquisition, and factors that collectively cause distortion in the iris image, 2D image-based iris identification techniques become limited and might lead to iris misclassification due to the dependency on the image appearance (texture) on the possibly distorted image. To alleviate this problem, we introduce a new 3D iris model and reader based upon which iris identification is performed. Using a set of at least two 2D images taken from different views, a small set of reliable and corresponding salient fiducial points (corner points taken from crypts, corona, and serpentine rings of iris pattern) in the two images are extracted, from which a set of 3D iris salient points are obtained using triangulation. Corresponding salient points in the 2D images are found using the Random Sampling Consensus (RANSAC) algorithm, which is robust in identifying the inlier points that correspond to each other in different views of the iris. From this small and reliable salient point set, a denser (high-resolution) set of extra salient feature points is constructed at minimum cost using a loop subdivision method that yields corresponding extra salient points in the two images from which a high-resolution 3D iris model is obtained. The points associated with the 3D high-resolution model are tessellated to form a high-resolution triangular mesh with the appearance of a triangular patch in the image imported onto the iris personalized 3D model rendering it a 3D iris surface with appearance. This 3D model construction method allows for a 3D classification of a test iris to one of many possible irises stored in the database. The classification is based on a geometric 3D point cloud error. For cross-validation, the geometric-based classification can be augmented by considering a mean squared error (MSE) based on the appearance of corresponding points on the test and base models to further disambiguate between irises with close geometric error values.

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Human iris three-dimensional imaging at micron resolution by a micro-plenoptic camera

Cited by 10 publications

References 23 publications

Retinal fundus imaging with a plenoptic sensor

Retinal fundus imaging with a plenoptic sensor

Measurement Technologies of Light Field Camera: An Overview

3D iris model and reader for iris identification

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