Optical Imaging Techniques Targeting Biological Applications

A special issue of Photonics (ISSN 2304-6732). This special issue belongs to the section "New Applications Enabled by Photonics Technologies and Systems".

Deadline for manuscript submissions: closed (31 October 2023) | Viewed by 1595

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School of Computer Science and Engineering, University of New South Wales, Sydney, Australia
Interests: image segmentation; image classification; medical image analysis; resource constraint neural networks; deep neural networks
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Special Issue Information

Dear Colleagues,

Human health monitoring technologies have recently been developing at a rapid and exciting rate. Technological improvements in hardware and software systems have enabled new approaches for detecting, monitoring, and tracing health-related biomarkers in clinical, pre-clinical, and at-home settings. These changes in the available technology have improved healthcare efficacy through allowing earlier disease identification, novel treatment options, and quantified self-monitoring.

The recent rise in computer power and optical imaging technology has enabled the creation of new analysis approaches that make use of well-established modalities from several biomedical applications. Machine learning and image-processing algorithms, for example, have grown in prevalence across various parts of biological imaging for the recognition of complicated patterns. Similarly, developments in hardware have aided in the development of health-monitoring devices in difficult circumstances, such as ambulatory monitoring. In the aim of bringing together various aspects of health monitoring in this Special Issue, authors are invited to submit papers describing novel imaging and/or sensing methods with pre-clinical/clinical applications.  The core themes of this topic include, but are not limited to, the following:

  • Advances in image analysis for disease detection and/or monitoring, including, but not limited to, Fundus, CT, X-ray, MRI, and ultrasound;
  • Machine learning methods for biomedical image analysis;
  • Machine learning methods for biomedical image segmentation;
  • Advances in multimodal biomedical systems for diagnosis, treatment, and/or prevention;
  • Advances in the investigation of biological tissues, including non-computational innovations and developments, such as photoacoustic, OCT, fluorescence microscopy, elastography, etc.

Dr. Tariq Khan
Guest Editor

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Published Papers (1 paper)

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Research

15 pages, 2712 KiB  
Article
Automated Segmentation and Morphometry of Zebrafish Anterior Chamber OCT Scans
by Oscar Ramos-Soto, Hang Chan Jo, Robert J. Zawadzki, Dae Yu Kim and Sandra E. Balderas-Mata
Photonics 2023, 10(9), 957; https://doi.org/10.3390/photonics10090957 - 22 Aug 2023
Viewed by 1216
Abstract
Zebrafish (Danio rerio) eyes are widely used in modeling studies of human ophthalmic diseases, including glaucoma and myopia. These pathologies cause morphological variations in the anterior chamber elements, which can be quantitatively measured using morphometric parameters, such as the corneal curvature, central corneal [...] Read more.
Zebrafish (Danio rerio) eyes are widely used in modeling studies of human ophthalmic diseases, including glaucoma and myopia. These pathologies cause morphological variations in the anterior chamber elements, which can be quantitatively measured using morphometric parameters, such as the corneal curvature, central corneal thickness, and anterior chamber angle. In the present work, an automated method is presented for iris and corneal segmentation, as well as the determination of the above-mentioned morphometry from optical coherence tomography (OCT) scans of zebrafish. The proposed method consists of four stages; namely, preprocessing, segmentation, postprocessing, and extraction of morphometric parameters. The first stage is composed of a combination of wavelet and Fourier transforms as well as gamma correction for artifact removal/reduction. The segmentation step is achieved using the U-net convolutional neural network. The postprocessing stage is composed of multilevel thresholding and morphological operations. Finally, three algorithms are proposed for automated morphological extraction in the last step. The morphology obtained using our automated framework is compared against manual measurements to assess the effectiveness of the method. The obtained results show that our scheme allows reliable determination of the morphometric parameters, thereby allowing efficient assessment for massive studies on zebrafish anterior chamber morphology using OCT scans. Full article
(This article belongs to the Special Issue Optical Imaging Techniques Targeting Biological Applications)
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