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Advanced Image Sensing Systems and Their Application
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Advanced Image Sensing Systems and Their Application

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

Deadline for manuscript submissions: closed (28 February 2022) | Viewed by 14189

Special Issue Editors

Dr. Changho Lee

E-Mail Website
Guest Editor
Department of Nuclear Medicine, Chonnam National University Medical School, Hwasun 58128, Korea
Interests: biomedical optical imaging; molecular imaging; quantitative analysis and signal processing
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Changhan Yoon

E-Mail Website
Guest Editor
1. Department of Biomedical Engineering, Inje University, Gimhae 50834, Republic of Korea
2. Department of Nanoscience and Engineering, Inje University, Gimhae 50834, Republic of Korea
Interests: ultrasound imaging; molecular imaging
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

The development of advanced sensor technology has greatly contributed to the improvement of our quality of life and convenience. In particular, high-performance image sensing systems are utilized in various imaging technologies, including optical imaging, ultrasound imaging, X-ray imaging, MRI, CT, and nuclear imaging, contributing to achieving high-sensitivity, miniaturization, and real-time display. These advanced image sensing systems play a critical role, not only in the medical field, but also in a variety of industries.

This Special Issue covers broad topics on advanced image sensing systems and their applications. The scope range of advanced image sensing systems can be extended to novel imaging sensing systems and key image sensing components, including hardware and software advancements. In addition, biomedical, industrial, and agricultural sensing applications are welcome. The Issue will publish full research papers, communications, and reviews.

Prof. Dr. Changho Lee
Prof. Dr. Changhan Yoon
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. Sensors is an international peer-reviewed open access semimonthly 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

  • advanced image sensing system
  • optical imaging/microscopy/tomography
  • ultrasound/acoustic imaging/microscopy/tomography
  • optoacoustic/phoacoustic imaging/microscopy/tomography
  • multimodal imaging
  • imaging and signal processing algorithm
  • deep learning assitanted imaging sensing techniques
  • biomedical sensing application
  • nondestructive sensing application

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Published Papers (4 papers)

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Research

26 pages, 3064 KiB  
Article
ECG Recurrence Plot-Based Arrhythmia Classification Using Two-Dimensional Deep Residual CNN Features
by Bhekumuzi M. Mathunjwa, Yin-Tsong Lin, Chien-Hung Lin, Maysam F. Abbod, Muammar Sadrawi and Jiann-Shing Shieh
Sensors 2022, 22(4), 1660; https://doi.org/10.3390/s22041660 - 20 Feb 2022
Cited by 39 | Viewed by 4923
Abstract
In this paper, an effective electrocardiogram (ECG) recurrence plot (RP)-based arrhythmia classification algorithm that can be implemented in portable devices is presented. Public databases from PhysioNet were used to conduct this study including the MIT-BIH Atrial Fibrillation Database, the MIT-BIH Arrhythmia Database, the [...] Read more.
In this paper, an effective electrocardiogram (ECG) recurrence plot (RP)-based arrhythmia classification algorithm that can be implemented in portable devices is presented. Public databases from PhysioNet were used to conduct this study including the MIT-BIH Atrial Fibrillation Database, the MIT-BIH Arrhythmia Database, the MIT-BIH Malignant Ventricular Ectopy Database, and the Creighton University Ventricular Tachyarrhythmia Database. ECG time series were segmented and converted using an RP, and two-dimensional images were used as inputs to the CNN classifiers. In this study, two-stage classification is proposed to improve the accuracy. The ResNet-18 architecture was applied to detect ventricular fibrillation (VF) and noise during the first stage, whereas normal, atrial fibrillation, premature atrial contraction, and premature ventricular contractions were detected using ResNet-50 in the second stage. The method was evaluated using 5-fold cross-validation which improved the results when compared to previous studies, achieving first and second stage average accuracies of 97.21% and 98.36%, sensitivities of 96.49% and 97.92%, positive predictive values of 95.54% and 98.20%, and F1-scores of 95.96% and 98.05%, respectively. Furthermore, a 5-fold improvement in the memory requirement was achieved when compared with a previous study, making this classifier feasible for use in resource-constricted environments such as portable devices. Even though the method is successful, first stage training requires combining four different arrhythmia types into one label (other), which generates more data for the other category than for VF and noise, thus creating a data imbalance that affects the first stage performance. Full article
(This article belongs to the Special Issue Advanced Image Sensing Systems and Their Application)
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18 pages, 17842 KiB  
Article
New End-to-End Strategy Based on DeepLabv3+ Semantic Segmentation for Human Head Detection
by Mohamed Chouai, Petr Dolezel, Dominik Stursa and Zdenek Nemec
Sensors 2021, 21(17), 5848; https://doi.org/10.3390/s21175848 - 30 Aug 2021
Cited by 8 | Viewed by 2788
Abstract
In the field of computer vision, object detection consists of automatically finding objects in images by giving their positions. The most common fields of application are safety systems (pedestrian detection, identification of behavior) and control systems. Another important application is head/person detection, which [...] Read more.
In the field of computer vision, object detection consists of automatically finding objects in images by giving their positions. The most common fields of application are safety systems (pedestrian detection, identification of behavior) and control systems. Another important application is head/person detection, which is the primary material for road safety, rescue, surveillance, etc. In this study, we developed a new approach based on two parallel Deeplapv3+ to improve the performance of the person detection system. For the implementation of our semantic segmentation model, a working methodology with two types of ground truths extracted from the bounding boxes given by the original ground truths was established. The approach has been implemented in our two private datasets as well as in a public dataset. To show the performance of the proposed system, a comparative analysis was carried out on two deep learning semantic segmentation state-of-art models: SegNet and U-Net. By achieving 99.14% of global accuracy, the result demonstrated that the developed strategy could be an efficient way to build a deep neural network model for semantic segmentation. This strategy can be used, not only for the detection of the human head but also be applied in several semantic segmentation applications. Full article
(This article belongs to the Special Issue Advanced Image Sensing Systems and Their Application)
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20 pages, 3543 KiB  
Article
Application of Photo Texture Analysis and Weather Data in Assessment of Air Quality in Terms of Airborne PM10 and PM2.5 Particulate Matter
by Monika Chuchro, Wojciech Sarlej, Marta Grzegorczyk and Karolina Nurzyńska
Sensors 2021, 21(16), 5483; https://doi.org/10.3390/s21165483 - 14 Aug 2021
Cited by 5 | Viewed by 2273
Abstract
The study was undertaken in Krakow, which is situated in Lesser Poland Voivodeship, where bad PM10 air-quality indicators occurred on more than 100 days in the years 2010–2019. Krakow has continuous air quality measurement in seven locations that are run by the [...] Read more.
The study was undertaken in Krakow, which is situated in Lesser Poland Voivodeship, where bad PM10 air-quality indicators occurred on more than 100 days in the years 2010–2019. Krakow has continuous air quality measurement in seven locations that are run by the Province Environmental Protection Inspectorate. The research aimed to create regression and classification models for PM10 and PM2.5 estimation based on sky photos and basic weather data. For this research, one short video with a resolution of 1920 × 1080 px was captured each day. From each film, only five frames were used, the information from which was averaged. Then, texture analysis was performed on each averaged photo frame. The results of the texture analysis were used in the regression and classification models. The regression models’ quality for the test datasets equals 0.85 and 0.73 for PM10 and 0.63 for PM2.5. The quality of each classification model differs (0.86 and 0.73 for PM10, and 0.80 for PM2.5). The obtained results show that the created classification models could be used in PM10 and PM2.5 air quality assessment. Moreover, the character of the obtained regression models indicates that their quality could be enhanced; thus, improved results could be obtained. Full article
(This article belongs to the Special Issue Advanced Image Sensing Systems and Their Application)
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30 pages, 9577 KiB  
Article
Depth-Image Segmentation Based on Evolving Principles for 3D Sensing of Structured Indoor Environments
by Miloš Antić, Andrej Zdešar and Igor Škrjanc
Sensors 2021, 21(13), 4395; https://doi.org/10.3390/s21134395 - 27 Jun 2021
Viewed by 3161
Abstract
This paper presents an approach of depth image segmentation based on the Evolving Principal Component Clustering (EPCC) method, which exploits data locality in an ordered data stream. The parameters of linear prototypes, which are used to describe different clusters, are estimated in a [...] Read more.
This paper presents an approach of depth image segmentation based on the Evolving Principal Component Clustering (EPCC) method, which exploits data locality in an ordered data stream. The parameters of linear prototypes, which are used to describe different clusters, are estimated in a recursive manner. The main contribution of this work is the extension and application of the EPCC to 3D space for recursive and real-time detection of flat connected surfaces based on linear segments, which are all detected in an evolving way. To obtain optimal results when processing homogeneous surfaces, we introduced two-step filtering for outlier detection within a clustering framework and considered the noise model, which allowed for the compensation of characteristic uncertainties that are introduced into the measurements of depth sensors. The developed algorithm was compared with well-known methods for point cloud segmentation. The proposed approach achieves better segmentation results over longer distances for which the signal-to-noise ratio is low, without prior filtering of the data. On the given database, an average rate higher than 90% was obtained for successfully detected flat surfaces, which indicates high performance when processing huge point clouds in a non-iterative manner. Full article
(This article belongs to the Special Issue Advanced Image Sensing Systems and Their Application)
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