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Image Processing and Analysis for Biotechnology and Bioprocess Engineering

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A special issue of Applied Sciences (ISSN 2076-3417). This special issue belongs to the section "Applied Biosciences and Bioengineering".

Deadline for manuscript submissions: closed (20 September 2023) | Viewed by 8777

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Special Issue Editor

Dr. Sang-Kyu Jung

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Guest Editor

Biological and Chemical Engineering Department, Hongik University, Sejong 2639, Republic of Korea
Interests: remote sensing; imaging; image analysis; high-throughput analysis; toxicity assay; smart system; deep learning; machine learning; IoT; ICT; Arduino
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Thanks to the development of various imaging devices and high-speed computing hardware, image analysis technology is increasingly being used in various fields. In particular, in the fields of biotechnology and bioprocessing engineering, image analysis technology is changing existing qualitative analysis methods, which rely on human vision, into quantitative analysis methods. For example, protein size and concentration can be quantitatively analyzed more accurately through an image analysis of the protein bands shown on Western blot images. Although many biotechnology researchers who are familiar with wet experiments may have difficulties using image analysis techniques in their studies, the advantages of image analysis techniques are obvious. Image analysis enables large-scale quantitative analysis that has been difficult for researchers to perform manually and allows real-time automatic analysis and control, saving manpower and time. The recent rapid progress of deep learning and machine learning technology is expected to further increase the possibility and level of application of image analysis technology in the field of biotechnology as it exists now. The final endpoint has mainly been obtained mainly by the sequential application of individual image processing algorithms, but hybrid image analysis combining traditional image processing algorithms and machine learning technology will be used more in the future.

In this Special Issue, we invite submissions exploring advanced research and recent advances in the development and application of various image analysis techniques in the fields of biotechnology and bioprocessing engineering. Any research on the development and application of image analysis technology in the fields of biotechnology and bioprocess engineering is welcome. For example, the development and utilization of imaging devices, image processing and software development, image analysis applications in wet/dry experiments, high-throughput analysis of cells or animals, or image-based machine learning studies are well-suited to the topic of the Special Issue. In addition, comprehensive reviews and survey papers as well as experimental studies are also welcome.

Image analysis algorithm and software development;
Image analysis application;
High-throughput image analysis;
Imaging devices and imaging methods;
Machine learning; deep learning;
Other keywords related to image analysis: 2D/3D reconstruction; animal behavior analysis; biomass analysis; bioprocess monitoring; cell culture monitoring (or control); cell morphology analysis; chemical analysis; classification; crystallization processes; densitometric analysis; food processing; fluorescence; healthcare; hyperspectral analysis; (near) infrared camera; medical image analysis; (electron) microscopy; morphology analysis; non-destructive analysis; object tracking; plant phenotyping; photobioreactor; process control; phenotype; soft sensors; thermal imaging; toxicity assay; wastewater treatment processes.

Dr. Sang-Kyu Jung
Guest Editor

Manuscript Submission Information

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Keywords

image analysis
image processing algorithms
image analysis applications
high-throughput image analysis
quantitative analysis
imaging devices

Published Papers (6 papers)

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Editorial

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6 pages, 194 KiB

Open AccessEditorial

Image Processing and Analysis for Biotechnology and Bioprocess Engineering

by Sang-Kyu Jung

Appl. Sci. 2024, 14(2), 711; https://doi.org/10.3390/app14020711 - 14 Jan 2024

Viewed by 986

Abstract

The development of high-performance computing hardware and image processing algorithms has led to the widespread application of image analysis in various fields [...] Full article

(This article belongs to the Special Issue Image Processing and Analysis for Biotechnology and Bioprocess Engineering)

Research

Jump to: Editorial

17 pages, 4331 KiB

Open AccessArticle

Learning to Segment Blob-like Objects by Image-Level Counting

by Konstantin Wüstefeld, Robin Ebbinghaus and Frank Weichert

Appl. Sci. 2023, 13(22), 12219; https://doi.org/10.3390/app132212219 - 10 Nov 2023

Viewed by 799

Abstract

There is a high demand for manually annotated data in many of the segmentation tasks based on neural networks. Selecting objects pixel by pixel not only takes much time, but it can also lead to inattentiveness and to inconsistencies due to changing annotators for different datasets and monotonous work. This is especially, but not exclusively, the case with sensor data such as microscopy imaging, where many blob-like objects need to be annotated. In addressing these problems, we present a weakly supervised training method that uses object counts at the image level to learn a segmentation implicitly instead of relying on a pixelwise annotation. Our method uses a given segmentation network and extends it with a counting head to enable training by counting. As part of the method, we introduce two specialized losses, contrast loss and morphological loss, which allow for a blob-like output with high contrast to be extracted from the last convolutional layer of the network before the actual counting. We show that similar high F1-scores can be achieved with weakly supervised learning methods as with strongly supervised training; in addition, we address the limitations of the presented method. Full article

(This article belongs to the Special Issue Image Processing and Analysis for Biotechnology and Bioprocess Engineering)

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11 pages, 2398 KiB

Open AccessArticle

AniWellTracker: Image Analysis of Small Animal Locomotion in Multiwell Plates

by Sang-Kyu Jung

Appl. Sci. 2023, 13(4), 2274; https://doi.org/10.3390/app13042274 - 10 Feb 2023

Viewed by 1205

Abstract

Animal movement is one of the important phenotypes in animal research. A large number of small animals can be tested in high-throughput studies using multiwell plates to study the effects of different genes, chemicals, and the external environment on animal locomotion. In this paper, we propose AniWellTracker, which is a free image analysis software optimized for analyzing individual animal locomotion using multiwell plates. In the tracking mode, the center coordinates of individual animals are calculated by analyzing images. In the review mode, not only the animal’s movement path, but also its speed, distance traveled, location frequency, rotation angle, etc. are analyzed and visualized using the built-in chart function. To test the usefulness of AniWellTracker, a case study was conducted to investigate the effect of two household cleaning agents on the swimming speed of zebrafish. AniWellTracker, written in Visual Basic .NET, is a standalone graphical user-interface software that does not use commercial software or external image analysis libraries and is expected to be of significant help to researchers. Full article

(This article belongs to the Special Issue Image Processing and Analysis for Biotechnology and Bioprocess Engineering)

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8 pages, 1670 KiB

Open AccessCommunication

Application of Digital Image Analysis for Assessment of Starch Content and Distribution in Potatoes

by Tomasz Boruczkowski, Hanna Boruczkowska, Wioletta Drożdż and Bartosz Raszewski

Appl. Sci. 2022, 12(24), 12988; https://doi.org/10.3390/app122412988 - 18 Dec 2022

Viewed by 1249

Abstract

This study presents the possibility of using digital image analysis for the assessment of the starch content and distribution in potatoes. Tubers of six cultivars that were stored for 3 months in contrasting conditions (4 °C vs. −15 °C) were used in the experiment. The starch distribution in the potato tubers was assessed on the basis of histograms of the pixel values along four lines in the tuber cross-sections. Next, the basic statistics were calculated and used for the analysis of variance. The applied method allowed more precise distinguishing between the studied potato cultivars than comparing the total starch content alone. The new method also clearly distinguished potatoes stored in a freezer from those kept in a cold store. Full article

(This article belongs to the Special Issue Image Processing and Analysis for Biotechnology and Bioprocess Engineering)

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15 pages, 2239 KiB

Open AccessArticle

Novel Genetic Associations for Skin Aging Phenotypes and Validation of Previously Reported Skin GWAS Results

by Mi-Yeon Cha, Ja-Eun Choi, Da-Som Lee, So-Ra Lee, Sang-In Lee, Jong-Ho Park, Jin-Hee Shin, In Soo Suh, Byung Ho Kim and Kyung-Won Hong

Appl. Sci. 2022, 12(22), 11422; https://doi.org/10.3390/app122211422 - 10 Nov 2022

Viewed by 2056

Abstract

Facial skin characteristics are complex traits determined by genetic and environmental factors. Because genetic factors continuously influenced facial skin characteristics, identifying associations between genetic variants [single-nucleotide polymorphisms (SNPs)] and facial skin characteristics may clarify genetic contributions. We previously reported a genome-wide association study (GWAS) for five skin phenotypes (wrinkles, pigmentation, moisture content, oil content, and sensitivity) conducted in 1079 subjects. In this study, face measurements and genomic data were generated for 261 samples, and significant SNPs described in previous papers were verified. We conducted a GWAS to identify additional genetic markers using the combined population of the previous study and current study samples. We identified 6 novel significant loci and 21 suggestive loci in the combined study with p-values < 5.0 × 10⁻⁸ (wrinkles: 4 SNPs; moisture content: 148 SNPs; pigmentation: 6 SNPs; sensitivity: 18 SNPs). Identifying SNPs using molecular genetic functional analysis is considered necessary for studying the mechanisms through which these genes affect the skin. We confirmed that of 23 previously identified SNPs, none were replicated. SNPs that could not be verified in a combined study may have been accidentally identified in an existing GWAS, or the samples added to this study may not have been a sufficient sample number to confirm those SNPs. The results of this study require validation in other independent population groups or larger samples. Although this study requires further research, it has the potential to contribute to the development of cosmetic-related genetic research in the future. Full article

(This article belongs to the Special Issue Image Processing and Analysis for Biotechnology and Bioprocess Engineering)

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9 pages, 3540 KiB

Open AccessArticle

A Mathematical Modeling and Statistical Analysis of Phycobiliprotein Fluorescence Decay under Exposure to Excitation Light

by Jinha Hwang and Alyssa H. Shin

Appl. Sci. 2022, 12(15), 7469; https://doi.org/10.3390/app12157469 - 25 Jul 2022

Viewed by 1189

Abstract

Photosynthetic phycobiliprotein complexes from Spirulina maxima were purified and fractioned by gel chromatography. A mathematical model was developed for the fractionated phycobiliprotein complexes to successfully represent the fluorescence decay rate under exposure to excitation light. Each fractionated complex had a different ratio of phycobiliproteins, such as allophycocyanin, phycocyanin, or phycoerythrin, but their fluorescence decay trends were determined to statistically have a high similarity. The mathematical model was derived based on mass balance in the sense that the fluorescence of phycobiliprotein complex was linearly dependent on its mass. The model considered both exponentially decreasing (early light-exposure period) and linearly decreasing (later period), and successfully fit the whole period of fluorescence decay trend. Full article

(This article belongs to the Special Issue Image Processing and Analysis for Biotechnology and Bioprocess Engineering)

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