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Biometric Technologies Based on Optical Coherence Tomography (OCT)

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A special issue of Sensors (ISSN 1424-8220). This special issue belongs to the section "Biosensors".

Deadline for manuscript submissions: closed (31 January 2023) | Viewed by 23397

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Dr. Tomasz Marciniak

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Guest Editor

Division of Signal Processing and Electronic Systems, Institute of Automation and Robotics, Poznan University of Technology, 60-965 Poznań, Poland
Interests: digital signal processing; biometrics; signal processors

Prof. Dr. Adam Dabrowski

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Guest Editor

Special Issue Information

Dear Colleagues,

Optical coherence tomography (OCT) is one of the newest and most important optical non-invasive methods for the investigation and testing of various materials (e.g., tissues) that are at least partly transparent to infrared light. This method is ideal for eye diagnostics, especially of the anterior segment or the retina. That is why commercially available OCT devices have revolutionized the clinical practice of ophthalmology. However, OCT may also be a very interesting technology for biometrics, including the biometrics of the human eye.

Data acquisition with the use of OCT devices is constantly being improved and becoming more and more popular, as exemplified by, for instance, OCTA (optical coherence tomography angiography) solutions, which allow to observe the retinal vessels. OCT is also applicable in other fields beyond ophthalmology.

OCT allows also for successful examination of other various materials and their structures. OCT can, among others, be used for the measurement of material thickness, testing of thin silicon wafers, structural analysis of polymer composites, as well as to examine the structure of artwork. OCT data processing requires the use of advanced IT methods, including machine learning algorithms.

This Special Issue of the Sensors Journal deals with the research of OCT technologies and techniques in biometric applications. The research should concern both hardware and software aspects of the application of OCT measurements of people, animals, and plants.

Dr. Tomasz Marciniak
Prof. Dr. Adam Dabrowski
Guest Editors

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Keywords

OCT acquisition
OCT biometrics
OCT image denoising
Real time processing
Machine learning algorithms
Visualization of OCT data
Medical diagnostics

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Published Papers (7 papers)

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Editorial

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3 pages, 167 KiB

Open AccessEditorial

Biometric Technologies Based on Optical Coherence Tomography

by Tomasz Marciniak

Sensors 2023, 23(7), 3753; https://doi.org/10.3390/s23073753 - 5 Apr 2023

Cited by 3 | Viewed by 1505

Abstract

Optical coherence tomography (OCT) is one of the newest and most important optical non-invasive methods for the investigation and testing of various materials (e [...] Full article

(This article belongs to the Special Issue Biometric Technologies Based on Optical Coherence Tomography (OCT))

Research

Jump to: Editorial

17 pages, 18540 KiB

Open AccessArticle

Robust Identification and Segmentation of the Outer Skin Layers in Volumetric Fingerprint Data

by Alexander Kirfel, Tobias Scheer, Norbert Jung and Christoph Busch

Sensors 2022, 22(21), 8229; https://doi.org/10.3390/s22218229 - 27 Oct 2022

Cited by 5 | Viewed by 2644

Abstract

Despite the long history of fingerprint biometrics and its use to authenticate individuals, there are still some unsolved challenges with fingerprint acquisition and presentation attack detection (PAD). Currently available commercial fingerprint capture devices struggle with non-ideal skin conditions, including soft skin in infants. They are also susceptible to presentation attacks, which limits their applicability in unsupervised scenarios such as border control. Optical coherence tomography (OCT) could be a promising solution to these problems. In this work, we propose a digital signal processing chain for segmenting two complementary fingerprints from the same OCT fingertip scan: One fingerprint is captured as usual from the epidermis (“outer fingerprint”), whereas the other is taken from inside the skin, at the junction between the epidermis and the underlying dermis (“inner fingerprint”). The resulting 3D fingerprints are then converted to a conventional 2D grayscale representation from which minutiae points can be extracted using existing methods. Our approach is device-independent and has been proven to work with two different time domain OCT scanners. Using efficient GPGPU computing, it took less than a second to process an entire gigabyte of OCT data. To validate the results, we captured OCT fingerprints of 130 individual fingers and compared them with conventional 2D fingerprints of the same fingers. We found that both the outer and inner OCT fingerprints were backward compatible with conventional 2D fingerprints, with the inner fingerprint generally being less damaged and, therefore, more reliable. Full article

(This article belongs to the Special Issue Biometric Technologies Based on Optical Coherence Tomography (OCT))

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20 pages, 7379 KiB

Open AccessArticle

Spatial-Temporal Speckle Variance in the En-Face View as a Contrast for Optical Coherence Tomography Angiography (OCTA)

by Jonathan D. Luisi, Jonathan L. Lin, Bill T. Ameredes and Massoud Motamedi

Sensors 2022, 22(7), 2447; https://doi.org/10.3390/s22072447 - 22 Mar 2022

Cited by 9 | Viewed by 2739

Abstract

Optical Coherence Tomography (OCT) is an adaptable depth-resolved imaging modality capable of creating a non-invasive ‘digital biopsy’ of the eye. One of the latest advances in OCT is optical coherence tomography angiography (OCTA), which uses the speckle variance or phase change in the signal to differentiate static tissue from blood flow. Unlike fluorescein angiography (FA), OCTA is contrast free and depth resolved. By combining high-density scan patterns and image processing algorithms, both morphometric and functional data can be extracted into a depth-resolved vascular map of the retina. The algorithm that we explored takes advantage of the temporal-spatial relationship of the speckle variance to improve the contrast of the vessels in the en-face OCT with a single frame. It also does not require the computationally inefficient decorrelation of multiple A-scans to detect vasculature, as used in conventional OCTA analysis. Furthermore, the spatial temporal OCTA (ST-OCTA) methodology tested offers the potential for post hoc analysis to improve the depth-resolved contrast of specific ocular structures, such as blood vessels, with the capability of using only a single frame for efficient screening of large sample volumes, and additional enhancement by processing with choice of frame averaging methods. Applications of this method in pre-clinical studies suggest that the OCTA algorithm and spatial temporal methodology reported here can be employed to investigate microvascularization and blood flow in the retina, and possibly other compartments of the eye. Full article

(This article belongs to the Special Issue Biometric Technologies Based on Optical Coherence Tomography (OCT))

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21 pages, 2196 KiB

Open AccessArticle

OCT Retinal and Choroidal Layer Instance Segmentation Using Mask R-CNN

by Ignacio A. Viedma, David Alonso-Caneiro, Scott A. Read and Michael J. Collins

Sensors 2022, 22(5), 2016; https://doi.org/10.3390/s22052016 - 4 Mar 2022

Cited by 19 | Viewed by 4375

Abstract

Optical coherence tomography (OCT) of the posterior segment of the eye provides high-resolution cross-sectional images that allow visualization of individual layers of the posterior eye tissue (the retina and choroid), facilitating the diagnosis and monitoring of ocular diseases and abnormalities. The manual analysis of retinal OCT images is a time-consuming task; therefore, the development of automatic image analysis methods is important for both research and clinical applications. In recent years, deep learning methods have emerged as an alternative method to perform this segmentation task. A large number of the proposed segmentation methods in the literature focus on the use of encoder–decoder architectures, such as U-Net, while other architectural modalities have not received as much attention. In this study, the application of an instance segmentation method based on region proposal architecture, called the Mask R-CNN, is explored in depth in the context of retinal OCT image segmentation. The importance of adequate hyper-parameter selection is examined, and the performance is compared with commonly used techniques. The Mask R-CNN provides a suitable method for the segmentation of OCT images with low segmentation boundary errors and high Dice coefficients, with segmentation performance comparable with the commonly used U-Net method. The Mask R-CNN has the advantage of a simpler extraction of the boundary positions, especially avoiding the need for a time-consuming graph search method to extract boundaries, which reduces the inference time by 2.5 times compared to U-Net, while segmenting seven retinal layers. Full article

(This article belongs to the Special Issue Biometric Technologies Based on Optical Coherence Tomography (OCT))

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14 pages, 4507 KiB

Open AccessArticle

A Tool for High-Resolution Volumetric Optical Coherence Tomography by Compounding Radial-and Linear Acquired B-Scans Using Registration

by Christian M. Bosch, Carmen Baumann, Shervin Dehghani, Michael Sommersperger, Navid Johannigmann-Malek, Katharina Kirchmair, Mathias Maier and Mohammad Ali Nasseri

Sensors 2022, 22(3), 1135; https://doi.org/10.3390/s22031135 - 2 Feb 2022

Cited by 5 | Viewed by 3361

Abstract

Optical coherence tomography (OCT) is a medical imaging modality that is commonly used to diagnose retinal diseases. In recent years, linear and radial scanning patterns have been proposed to acquire three-dimensional OCT data. These patterns show differences in A-scan acquisition density across the generated volumes, and thus differ in their suitability for the diagnosis of retinal diseases. While radial OCT volumes exhibit a higher A-scan sampling rate around the scan center, linear scans contain more information in the peripheral scan areas. In this paper, we propose a method to combine a linearly and radially acquired OCT volume to generate a single compound volume, which merges the advantages of both scanning patterns to increase the information that can be gained from the three-dimensional OCT data. We initially generate 3D point clouds of the linearly and radially acquired OCT volumes and use an Iterative Closest Point (ICP) variant to register both volumes. After registration, the compound volume is created by selectively exploiting linear and radial scanning data, depending on the A-scan density of the individual scans. Fusing regions from both volumes with respect to their local A-scan sampling density, we achieve improved overall anatomical OCT information in a high-resolution compound volume. We demonstrate our method on linear and radial OCT volumes for the visualization and analysis of macular holes and the surrounding anatomical structures. Full article

(This article belongs to the Special Issue Biometric Technologies Based on Optical Coherence Tomography (OCT))

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26 pages, 4922 KiB

Open AccessArticle

Segmentation of Preretinal Space in Optical Coherence Tomography Images Using Deep Neural Networks

by Agnieszka Stankiewicz, Tomasz Marciniak, Adam Dabrowski, Marcin Stopa, Elzbieta Marciniak and Boguslaw Obara

Sensors 2021, 21(22), 7521; https://doi.org/10.3390/s21227521 - 12 Nov 2021

Cited by 11 | Viewed by 3016

Abstract

This paper proposes an efficient segmentation of the preretinal area between the inner limiting membrane (ILM) and posterior cortical vitreous (PCV) of the human eye in an image obtained with the use of optical coherence tomography (OCT). The research was carried out using a database of three-dimensional OCT imaging scans obtained with the Optovue RTVue XR Avanti device. Various types of neural networks (UNet, Attention UNet, ReLayNet, LFUNet) were tested for semantic segmentation, their effectiveness was assessed using the Dice coefficient and compared to the graph theory techniques. Improvement in segmentation efficiency was achieved through the use of relative distance maps. We also show that selecting a larger kernel size for convolutional layers can improve segmentation quality depending on the neural network model. In the case of PVC, we obtain the effectiveness reaching up to

96.35 %

. The proposed solution can be widely used to diagnose vitreomacular traction changes, which is not yet available in scientific or commercial OCT imaging solutions. Full article

(This article belongs to the Special Issue Biometric Technologies Based on Optical Coherence Tomography (OCT))

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19 pages, 9985 KiB

Open AccessArticle

Automatic Quantification of Anterior Lamina Cribrosa Structures in Optical Coherence Tomography Using a Two-Stage CNN Framework

by Md Habibur Rahman, Hyeon Woo Jeong, Na Rae Kim and Dae Yu Kim

Sensors 2021, 21(16), 5383; https://doi.org/10.3390/s21165383 - 9 Aug 2021

Cited by 5 | Viewed by 3706

Abstract

In this study, we propose a new intelligent system to automatically quantify the morphological parameters of the lamina cribrosa (LC) of the optical coherence tomography (OCT), including depth, curve depth, and curve index from OCT images. The proposed system consisted of a two-stage deep learning (DL) model, which was composed of the detection and the segmentation models as well as a quantification process with a post-processing scheme. The models were used to solve the class imbalance problem and obtain Bruch’s membrane opening (BMO) as well as anterior LC information. The detection model was implemented by using YOLOv3 to acquire the BMO and LC position information. The Attention U-Net segmentation model is used to compute accurate locations of the BMO and LC curve information. In addition, post-processing is applied using polynomial regression to attain the anterior LC curve boundary information. Finally, the numerical values of morphological parameters are quantified from BMO and LC curve information using an image processing algorithm. The average precision values in the detection performances of BMO and LC information were 99.92% and 99.18%, respectively, which is very accurate. A highly correlated performance of R² = 0.96 between the predicted and ground-truth values was obtained, which was very close to 1 and satisfied the quantification results. The proposed system was performed accurately by fully automatic quantification of BMO and LC morphological parameters using a DL model. Full article

(This article belongs to the Special Issue Biometric Technologies Based on Optical Coherence Tomography (OCT))

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