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Micromachines
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Laser and Optics in Micromachines for Biomedical Applications
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Laser and Optics in Micromachines for Biomedical Applications

A special issue of Micromachines (ISSN 2072-666X). This special issue belongs to the section "B：Biology and Biomedicine".

Deadline for manuscript submissions: closed (15 July 2023) | Viewed by 4271

Special Issue Editors

Dr. Hayde Peregrina-Barreto

E-Mail Website
Guest Editor
Instituto Nacional de Astrofisica Optica y Electronica, Puebla 72840, Mexico
Interests: image processing and analysis; image and signal classification; biomedical applications; multispectral images
Special Issues, Collections and Topics in MDPI journals
Dr. Julio César Ramı́rez-San-Juan

E-Mail Website
Guest Editor
Instituto Nacional de Astrofisica Optica y Electronica, Puebla 72840, Mexico
Interests: speckle contrast; blood flow; partial coherence
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

In recent years, the development and improvement in laser and optics devices and the techniques used for analyzing information have significantly advanced. Moreover, laser and optics have significantly impacted growing research possibilities and technology development in areas such as spectroscopy, instrumentation, and sensing. These advances open a wide field of opportunity for applications in biomedicine.

This Special Issue of Micromachines entitled “Laser and Optics in Micromachines for Biomedical Applications” covers topics involving optical instrumentation and sensing, development and application of lasers, and imaging technology, among others. This Special Issue is focused on gathering novel ideas related to advances and solutions for biomedical applications and encouraging multidisciplinary research. Research in other related areas is also welcome.

Dr. Hayde Peregrina-Barreto
Dr. Julio César Ramı́rez-San-Juan
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Micromachines is an international peer-reviewed open access monthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2600 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • optoelectronics
  • optics and laser instrumentation
  • optical sensing
  • optical imaging
  • spectroscopy
  • biomedical optical devices
  • biomedical signal and image processing
  • biosensors
  • image and information fusion
  • computational methods in optical imaging

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Published Papers (2 papers)

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14 pages, 6256 KiB  
Article
Non-Local Mean Denoising Algorithm Based on Fractional Compact Finite Difference Scheme Effectively Reduces Speckle Noise in Optical Coherence Tomography Images
by Huaiguang Chen and Jing Gao
Micromachines 2022, 13(12), 2039; https://doi.org/10.3390/mi13122039 - 22 Nov 2022
Cited by 1 | Viewed by 1527
Abstract
Optical coherence tomography (OCT) is used in various fields such, as medical diagnosis and material inspection, as a non-invasive and high-resolution optical imaging modality. However, an OCT image is damaged by speckle noise during its generation, thus reducing the image quality. To address [...] Read more.
Optical coherence tomography (OCT) is used in various fields such, as medical diagnosis and material inspection, as a non-invasive and high-resolution optical imaging modality. However, an OCT image is damaged by speckle noise during its generation, thus reducing the image quality. To address this problem, a non-local means (NLM) algorithm based on the fractional compact finite difference scheme (FCFDS) is proposed to remove the speckle noise in OCT images. FCFDS uses more local pixel information when compared to integer-order difference operators. The FCFDS operator is introduced into the NLM algorithm to construct a high-precision weight calculation so that the proposed algorithm can effectively reduce the speckle noise in the OCT images. Experiments on simulations and real OCT images show that the proposed method is comparable to other state-of-the-art despeckling methods and can substantially reduce noise and preserve image details such as edges and structures. Speckle noise removal can further promote the application of the proposed algorithm in medical diagnosis and industrial detection, as it has key research value. Full article
(This article belongs to the Special Issue Laser and Optics in Micromachines for Biomedical Applications)
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14 pages, 3241 KiB  
Article
Adaptive Feature Extraction for Blood Vessel Segmentation and Contrast Recalculation in Laser Speckle Contrast Imaging
by Eduardo Morales-Vargas, Juan Pablo Padilla-Martinez, Hayde Peregrina-Barreto, Wendy Argelia Garcia-Suastegui and Julio Cesar Ramirez-San-Juan
Micromachines 2022, 13(10), 1788; https://doi.org/10.3390/mi13101788 - 20 Oct 2022
Cited by 2 | Viewed by 2028
Abstract
Microvasculature analysis in biomedical images is essential in the medical area to evaluate diseases by extracting properties of blood vessels, such as relative blood flow or morphological measurements such as diameter. Given the advantages of Laser Speckle Contrast Imaging (LSCI), several studies have [...] Read more.
Microvasculature analysis in biomedical images is essential in the medical area to evaluate diseases by extracting properties of blood vessels, such as relative blood flow or morphological measurements such as diameter. Given the advantages of Laser Speckle Contrast Imaging (LSCI), several studies have aimed to reduce inherent noise to distinguish between tissue and blood vessels at higher depths. These studies have shown that computing Contrast Images (CIs) with Analysis Windows (AWs) larger than standard sizes obtains better statistical estimators. The main issue is that larger samples combine pixels of microvasculature with tissue regions, reducing the spatial resolution of the CI. This work proposes using adaptive AWs of variable size and shape to calculate the features required to train a segmentation model that discriminates between blood vessels and tissue in LSCI. The obtained results show that it is possible to improve segmentation rates of blood vessels up to 45% in high depths (≈900 μm) by extracting features adaptively. The main contribution of this work is the experimentation with LSCI images under different depths and exposure times through adaptive processing methods, furthering the understanding the performance of the different approaches under these conditions. Results also suggest that it is possible to train a segmentation model to discriminate between pixels belonging to blood vessels and those belonging to tissue. Therefore, an adaptive feature extraction method may improve the quality of the features and thus increase the classification rates of blood vessels in LSCI. Full article
(This article belongs to the Special Issue Laser and Optics in Micromachines for Biomedical Applications)
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