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Multi-Focus Image Fusion Using Focal Area Extraction in a Large Quantity of Microscopic Images
The non-invasive examination of conjunctival goblet cells using a microscope is a novel procedure for the diagnosis of ocular surface diseases. However, it is difficult to generate an all-in-focus image due to the curvature of the eyes and the limited focal depth of the microscope. The microscope ac...
Autores principales: | , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2021
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8586970/ https://www.ncbi.nlm.nih.gov/pubmed/34770677 http://dx.doi.org/10.3390/s21217371 |
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author | Lee, Jiyoung Jang, Seunghyun Lee, Jungbin Kim, Taehan Kim, Seonghan Seo, Jongbum Kim, Ki Hean Yang, Sejung |
author_facet | Lee, Jiyoung Jang, Seunghyun Lee, Jungbin Kim, Taehan Kim, Seonghan Seo, Jongbum Kim, Ki Hean Yang, Sejung |
author_sort | Lee, Jiyoung |
collection | PubMed |
description | The non-invasive examination of conjunctival goblet cells using a microscope is a novel procedure for the diagnosis of ocular surface diseases. However, it is difficult to generate an all-in-focus image due to the curvature of the eyes and the limited focal depth of the microscope. The microscope acquires multiple images with the axial translation of focus, and the image stack must be processed. Thus, we propose a multi-focus image fusion method to generate an all-in-focus image from multiple microscopic images. First, a bandpass filter is applied to the source images and the focus areas are extracted using Laplacian transformation and thresholding with a morphological operation. Next, a self-adjusting guided filter is applied for the natural connections between local focus images. A window-size-updating method is adopted in the guided filter to reduce the number of parameters. This paper presents a novel algorithm that can operate for a large quantity of images (10 or more) and obtain an all-in-focus image. To quantitatively evaluate the proposed method, two different types of evaluation metrics are used: “full-reference” and “no-reference”. The experimental results demonstrate that this algorithm is robust to noise and capable of preserving local focus information through focal area extraction. Additionally, the proposed method outperforms state-of-the-art approaches in terms of both visual effects and image quality assessments. |
format | Online Article Text |
id | pubmed-8586970 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2021 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
spelling | pubmed-85869702021-11-13 Multi-Focus Image Fusion Using Focal Area Extraction in a Large Quantity of Microscopic Images Lee, Jiyoung Jang, Seunghyun Lee, Jungbin Kim, Taehan Kim, Seonghan Seo, Jongbum Kim, Ki Hean Yang, Sejung Sensors (Basel) Communication The non-invasive examination of conjunctival goblet cells using a microscope is a novel procedure for the diagnosis of ocular surface diseases. However, it is difficult to generate an all-in-focus image due to the curvature of the eyes and the limited focal depth of the microscope. The microscope acquires multiple images with the axial translation of focus, and the image stack must be processed. Thus, we propose a multi-focus image fusion method to generate an all-in-focus image from multiple microscopic images. First, a bandpass filter is applied to the source images and the focus areas are extracted using Laplacian transformation and thresholding with a morphological operation. Next, a self-adjusting guided filter is applied for the natural connections between local focus images. A window-size-updating method is adopted in the guided filter to reduce the number of parameters. This paper presents a novel algorithm that can operate for a large quantity of images (10 or more) and obtain an all-in-focus image. To quantitatively evaluate the proposed method, two different types of evaluation metrics are used: “full-reference” and “no-reference”. The experimental results demonstrate that this algorithm is robust to noise and capable of preserving local focus information through focal area extraction. Additionally, the proposed method outperforms state-of-the-art approaches in terms of both visual effects and image quality assessments. MDPI 2021-11-05 /pmc/articles/PMC8586970/ /pubmed/34770677 http://dx.doi.org/10.3390/s21217371 Text en © 2021 by the authors. https://creativecommons.org/licenses/by/4.0/Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/). |
spellingShingle | Communication Lee, Jiyoung Jang, Seunghyun Lee, Jungbin Kim, Taehan Kim, Seonghan Seo, Jongbum Kim, Ki Hean Yang, Sejung Multi-Focus Image Fusion Using Focal Area Extraction in a Large Quantity of Microscopic Images |
title | Multi-Focus Image Fusion Using Focal Area Extraction in a Large Quantity of Microscopic Images |
title_full | Multi-Focus Image Fusion Using Focal Area Extraction in a Large Quantity of Microscopic Images |
title_fullStr | Multi-Focus Image Fusion Using Focal Area Extraction in a Large Quantity of Microscopic Images |
title_full_unstemmed | Multi-Focus Image Fusion Using Focal Area Extraction in a Large Quantity of Microscopic Images |
title_short | Multi-Focus Image Fusion Using Focal Area Extraction in a Large Quantity of Microscopic Images |
title_sort | multi-focus image fusion using focal area extraction in a large quantity of microscopic images |
topic | Communication |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8586970/ https://www.ncbi.nlm.nih.gov/pubmed/34770677 http://dx.doi.org/10.3390/s21217371 |
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