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Special Issue "Advances in Image Segmentation: Theory and Applications"

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

Deadline for manuscript submissions: 20 October 2021.

Special Issue Editors

Dr. Marcin Ciecholewski
E-Mail Website
Guest Editor
Faculty of Mathematics, Physics and Informatics, University of Gdansk, ul. Wita Stwosza 57, 80-952 Gdansk, Poland
Interests: image processing; computer vision; image segmentation; object detection; remote sensing image processing; synthetic aperture radar image processing; active contours; mathematical morphology; morphological image processing; pattern recognition algorithms
Prof. Dr. Cosimo Distante
E-Mail Website
Guest Editor
Institute of Applied Sciences and Intelligent Systems “ScienceApp", Consiglio Nazionale delle Ricerche, c/o Dhitech Campus Universitario Ecotekne, via Monteroni sn, 73100 Lecce, Italy
Interests: computer vision; pattern recognition; video surveillance; object tracking; deep learning; audience measurements; visual interaction; human–robot interaction
Special Issues and Collections in MDPI journals

Special Issue Information

Dear Colleagues,

Segmentation is aimed at identifying the borders of objects in the analysed digital image or at splitting the image into various, non-overlapping regions. Segmentation is one of the most important steps in digital image processing systems as its result directly impacts the results of subsequent processing methods, e.g., 3D reconstruction and visualisation, distinguishing of features or classification. Practice shows that it is extremely difficult to arrive at a universal method that would produce high results for different segmentation problems that are being solved. This is all the more so as digital images are acquired with scanners/sensors of different types and with different characteristics.

Image segmentation has become a major topic of interest in various domains, including medical imaging, environmental remote sensing field, land cover applications, etc. In medical image analysis, segmentation can be defined as a method allowing, e.g., the precise shape of the potential lesions to be determined or the shape of the organ to be determined. Very different imaging techniques are available for medical use today (for example, ultrasonography (USG), computed tomography (CT), magnetic resonance imaging (MR)), while segmentation methods should be as closely matched to source images processed as possible.

In remote sensing, segmentation allows assigning labels to image pixels so that pixels in the same region or object are associated with the same label. It can be said that high spatial resolution (HSR) images acquired from planes, satellites or from unmanned aerial vehicles (UAVs) as well as from other platforms are increasingly available. HSR images come from different sensor types, such as hyperspectral, multispectral, synthetic aperture radar (SAR) or thermal infrared sensors.

This Special Issue aims to publish original papers, as well as review articles addressing emerging trends in image segmentation. The main topics include but are not limited to the following:

- Biomedical Image Segmentation (different modalities, e.g., CT, MRI, USG);

- SAR image segmentation;

- Segmentation methods for multisensor data analysis;

- Segmentation methods for time-series data analysis (e.g., agricultural crop areas, urban areas growing or drought/flood monitoring);

- Machine learning;

- Deep learning;

- Review of segmentation methods.

Dr. Marcin Ciecholewski
Dr. Cosimo Distante
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 papers will be 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 2200 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

  • biomedical image segmentation (different modalities, e.g., CT, MRI, USG)
  • SAR image segmentation
  • segmentation methods for multisensor data analysis
  • segmentation methods for time-series data analysis (e.g., agricultural crop areas, urban areas growing or drought/flood monitoring)
  • machine learning
  • deep learning
  • review of segmentation methods

Published Papers (3 papers)

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Research

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Article
Fully Parallel Implementation of Otsu Automatic Image Thresholding Algorithm on FPGA
Sensors 2021, 21(12), 4151; https://0-doi-org.brum.beds.ac.uk/10.3390/s21124151 - 17 Jun 2021
Viewed by 334
Abstract
This work proposes a high-throughput implementation of the Otsu automatic image thresholding algorithm on Field Programmable Gate Array (FPGA), aiming to process high-resolution images in real-time. The Otsu method is a widely used global thresholding algorithm to define an optimal threshold between two [...] Read more.
This work proposes a high-throughput implementation of the Otsu automatic image thresholding algorithm on Field Programmable Gate Array (FPGA), aiming to process high-resolution images in real-time. The Otsu method is a widely used global thresholding algorithm to define an optimal threshold between two classes. However, this technique has a high computational cost, making it difficult to use in real-time applications. Thus, this paper proposes a hardware design exploiting parallelization to optimize the system’s processing time. The implementation details and an analysis of the synthesis results concerning the hardware area occupation, throughput, and dynamic power consumption, are presented. Results have shown that the proposed hardware achieved a high speedup compared to similar works in the literature. Full article
(This article belongs to the Special Issue Advances in Image Segmentation: Theory and Applications)
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Article
Attention-Guided Network with Densely Connected Convolution for Skin Lesion Segmentation
Sensors 2021, 21(10), 3462; https://0-doi-org.brum.beds.ac.uk/10.3390/s21103462 - 16 May 2021
Viewed by 542
Abstract
The automatic segmentation of skin lesions is considered to be a key step in the diagnosis and treatment of skin lesions, which is essential to improve the survival rate of patients. However, due to the low contrast, the texture and boundary are difficult [...] Read more.
The automatic segmentation of skin lesions is considered to be a key step in the diagnosis and treatment of skin lesions, which is essential to improve the survival rate of patients. However, due to the low contrast, the texture and boundary are difficult to distinguish, which makes the accurate segmentation of skin lesions challenging. To cope with these challenges, this paper proposes an attention-guided network with densely connected convolution for skin lesion segmentation, called CSAG and DCCNet. In the last step of the encoding path, the model uses densely connected convolution to replace the ordinary convolutional layer. A novel attention-oriented filter module called Channel Spatial Fast Attention-guided Filter (CSFAG for short) was designed and embedded in the skip connection of the CSAG and DCCNet. On the ISIC-2017 data set, a large number of ablation experiments have verified the superiority and robustness of the CSFAG module and Densely Connected Convolution. The segmentation performance of CSAG and DCCNet is compared with other latest algorithms, and very competitive results have been achieved in all indicators. The robustness and cross-data set performance of our method was tested on another publicly available data set PH2, further verifying the effectiveness of the model. Full article
(This article belongs to the Special Issue Advances in Image Segmentation: Theory and Applications)
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Review

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Review
Computational Methods for Liver Vessel Segmentation in Medical Imaging: A Review
Sensors 2021, 21(6), 2027; https://0-doi-org.brum.beds.ac.uk/10.3390/s21062027 - 12 Mar 2021
Cited by 1 | Viewed by 670
Abstract
The segmentation of liver blood vessels is of major importance as it is essential for formulating diagnoses, planning and delivering treatments, as well as evaluating the results of clinical procedures. Different imaging techniques are available for application in clinical practice, so the segmentation [...] Read more.
The segmentation of liver blood vessels is of major importance as it is essential for formulating diagnoses, planning and delivering treatments, as well as evaluating the results of clinical procedures. Different imaging techniques are available for application in clinical practice, so the segmentation methods should take into account the characteristics of the imaging technique. Based on the literature, this review paper presents the most advanced and effective methods of liver vessel segmentation, as well as their performance according to the metrics used. This paper includes results available for four imaging methods, namely: computed tomography (CT), computed tomography angiography (CTA), magnetic resonance (MR), and ultrasonography (USG). The publicly available datasets used in research are also presented. This paper may help researchers gain better insight into the available materials and methods, making it easier to develop new, more effective solutions, as well as to improve existing approaches. This article analyzes in detail various segmentation methods, which can be divided into three groups: active contours, tracking-based, and machine learning techniques. For each group of methods, their theoretical and practical characteristics are discussed, and the pros and cons are highlighted. The most advanced and promising approaches are also suggested. However, we conclude that liver vasculature segmentation is still an open problem, because of the various deficiencies and constraints researchers need to address and try to eliminate from the solutions used. Full article
(This article belongs to the Special Issue Advances in Image Segmentation: Theory and Applications)
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Planned Papers

The below list represents only planned manuscripts. Some of these manuscripts have not been received by the Editorial Office yet. Papers submitted to MDPI journals are subject to peer-review.

Title: Benchmarking of deep architectures for segmentation of medical images
Authors: Zbislaw Tabor
Affiliation: Cracow University of Technology, Warszawska 24, 31-155 Krakow, Poland
Abstract: The first topis concerns comparison of a couple of generic-purpose deep segmentation architectures designed specifically for the segmentation of medical images. These architectures are at least UNet, UNet++, UNet3+, all claimed to provide superior performance over others. The models were however never compared within a unified framework, which includes, besides architecture details, other factors like preprocessing, learning rate scheduler, loss function selection etc. For this reason it is by no means clear that any architectural variants are indeed beneficial for segmentation. In the manuscript we are benchmarckin the recent architectures using a unified segmentation framewotk nnUNet.

Title: Evaluation of shape correction methods for deep segmentation of medical images
Authors: Zbislaw Tabor
Affiliation: Cracow University of Technology, Warszawska 24, 31-155 Krakow, Poland
Abstract: The second topis concerns correction of segmentations of structures seen in medical images made by deep models (specifically UNet) with statistical models of shape. The deep segmentation architectures, although generally providing segmentations of high quality are not aware of typical anatomical shape of segmented organs which in some cases leads to spurious segmentation results which need to be corrected before the segmentation can be used for diagnostic purposes. Some of the approaches to the correction include deep learning variants of active contours or active shape model. A promising approach is based on application of autoencoders which, like active shape model, can learn typical anatomical variance of the segmented objects. In the manuscript we are applying autoencoders to correct spurious segmentations.

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