Image Segmentation Techniques: Current Status and Future Directions

A special issue of Journal of Imaging (ISSN 2313-433X). This special issue belongs to the section "Image and Video Processing".

Deadline for manuscript submissions: closed (31 December 2022) | Viewed by 27071

Special Issue Editors


E-Mail Website
Guest Editor
School of Electronics and Computer Science, University of Southampton, Southampton SO17 1BJ, UK
Interests: computer vision; image processing; machine/deep learning; scientific computing
Special Issues, Collections and Topics in MDPI journals

E-Mail
Guest Editor
National Key Laboratory of Science and Technology on Automatic Target Recognition, National University of Defense Technology, Changsha 410073, China
Interests: image processing; machine learning; object recognition; hyperspectral imaging; image classification

E-Mail
Guest Editor
Department of Mathematics, Hangzhou Dianzi University, Hangzhou 310018, China
Interests: image processing; optimization; tensor analysis; computing

Special Issue Information

Dear Colleagues,

Image segmentation, as a fundamental and challenging task in many subjects such as image processing and computer vision, is of great importance but is constantly challenging to deliver. Briefly speaking, it is the process of assigning a label to every pixel in an image according to certain characteristics such as intensity, biometrics and semantics. It is generally a prerequisite and plays a key role in its ubiquitous practical applications such as machine vision, medical imaging, detection, recognition and autonomous driving. Researchers are increasing their efforts to develop new segmentation techniques based on, e.g., mathematical/statistical models, biometrics and machine learning via deep neural networks to tackle existing and upcoming challenges.

This Special Issue aims to gather innovative research on image segmentation techniques, ranging from the current status to future directions, and from hand-crafted techniques to deep learning, etc. We also welcome submissions including, but not limited to, the following applications in digital imaging, medical imaging, object detection, recognition tasks, etc.

Dr. Xiaohao Cai
Prof. Dr. Ping Zhong
Prof. Dr. Gaohang Yu
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. Journal of Imaging 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 1800 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

  • image segmentation
  • image processing
  • classification
  • recognition
  • variational regularization algorithms
  • neural networks
  • machine learning
  • deep learning
  • digital imaging
  • medical imaging

Published Papers (6 papers)

Order results
Result details
Select all
Export citation of selected articles as:

Research

Jump to: Review

27 pages, 6394 KiB  
Article
Algebraic Multi-Layer Network: Key Concepts
by Igor Khanykov, Vadim Nenashev and Mikhail Kharinov
J. Imaging 2023, 9(7), 146; https://doi.org/10.3390/jimaging9070146 - 18 Jul 2023
Cited by 2 | Viewed by 1012
Abstract
The paper refers to interdisciplinary research in the areas of hierarchical cluster analysis of big data and ordering of primary data to detect objects in a color or in a grayscale image. To perform this on a limited domain of multidimensional data, an [...] Read more.
The paper refers to interdisciplinary research in the areas of hierarchical cluster analysis of big data and ordering of primary data to detect objects in a color or in a grayscale image. To perform this on a limited domain of multidimensional data, an NP-hard problem of calculation of close to optimal piecewise constant data approximations with the smallest possible standard deviations or total squared errors (approximation errors) is solved. The solution is achieved by revisiting, modernizing, and combining classical Ward’s clustering, split/merge, and K-means methods. The concepts of objects, images, and their elements (superpixels) are formalized as structures that are distinguishable from each other. The results of structuring and ordering the image data are presented to the user in two ways, as tabulated approximations of the image showing the available object hierarchies. For not only theoretical reasoning, but also for practical implementation, reversible calculations with pixel sets are performed easily, as with individual pixels in terms of Sleator–Tarjan Dynamic trees and cyclic graphs forming an Algebraic Multi-Layer Network (AMN). The detailing of the latter significantly distinguishes this paper from our prior works. The establishment of the invariance of detected objects with respect to changing the context of the image and its transformation into grayscale is also new. Full article
(This article belongs to the Special Issue Image Segmentation Techniques: Current Status and Future Directions)
Show Figures

Figure 1

26 pages, 4735 KiB  
Article
Data Augmentation in Classification and Segmentation: A Survey and New Strategies
by Khaled Alomar, Halil Ibrahim Aysel and Xiaohao Cai
J. Imaging 2023, 9(2), 46; https://doi.org/10.3390/jimaging9020046 - 17 Feb 2023
Cited by 40 | Viewed by 11324
Abstract
In the past decade, deep neural networks, particularly convolutional neural networks, have revolutionised computer vision. However, all deep learning models may require a large amount of data so as to achieve satisfying results. Unfortunately, the availability of sufficient amounts of data for real-world [...] Read more.
In the past decade, deep neural networks, particularly convolutional neural networks, have revolutionised computer vision. However, all deep learning models may require a large amount of data so as to achieve satisfying results. Unfortunately, the availability of sufficient amounts of data for real-world problems is not always possible, and it is well recognised that a paucity of data easily results in overfitting. This issue may be addressed through several approaches, one of which is data augmentation. In this paper, we survey the existing data augmentation techniques in computer vision tasks, including segmentation and classification, and suggest new strategies. In particular, we introduce a way of implementing data augmentation by using local information in images. We propose a parameter-free and easy to implement strategy, the random local rotation strategy, which involves randomly selecting the location and size of circular regions in the image and rotating them with random angles. It can be used as an alternative to the traditional rotation strategy, which generally suffers from irregular image boundaries. It can also complement other techniques in data augmentation. Extensive experimental results and comparisons demonstrated that the new strategy consistently outperformed its traditional counterparts in, for example, image classification. Full article
(This article belongs to the Special Issue Image Segmentation Techniques: Current Status and Future Directions)
Show Figures

Figure 1

18 pages, 5625 KiB  
Article
GEMA—An Automatic Segmentation Method for Real-Time Analysis of Mammalian Cell Growth in Microfluidic Devices
by Ramiro Isa-Jara, Camilo Pérez-Sosa, Erick Macote-Yparraguirre, Natalia Revollo, Betiana Lerner, Santiago Miriuka, Claudio Delrieux, Maximiliano Pérez and Roland Mertelsmann
J. Imaging 2022, 8(10), 281; https://doi.org/10.3390/jimaging8100281 - 14 Oct 2022
Cited by 2 | Viewed by 1877
Abstract
Nowadays, image analysis has a relevant role in most scientific and research areas. This process is used to extract and understand information from images to obtain a model, knowledge, and rules in the decision process. In the case of biological areas, images are [...] Read more.
Nowadays, image analysis has a relevant role in most scientific and research areas. This process is used to extract and understand information from images to obtain a model, knowledge, and rules in the decision process. In the case of biological areas, images are acquired to describe the behavior of a biological agent in time such as cells using a mathematical and computational approach to generate a system with automatic control. In this paper, MCF7 cells are used to model their growth and death when they have been injected with a drug. These mammalian cells allow understanding of behavior, gene expression, and drug resistance to breast cancer. For this, an automatic segmentation method called GEMA is presented to analyze the apoptosis and confluence stages of culture by measuring the increase or decrease of the image area occupied by cells in microfluidic devices. In vitro, the biological experiments can be analyzed through a sequence of images taken at specific intervals of time. To automate the image segmentation, the proposed algorithm is based on a Gabor filter, a coefficient of variation (CV), and linear regression. This allows the processing of images in real time during the evolution of biological experiments. Moreover, GEMA has been compared with another three representative methods such as gold standard (manual segmentation), morphological gradient, and a semi-automatic algorithm using FIJI. The experiments show promising results, due to the proposed algorithm achieving an accuracy above 90% and a lower computation time because it requires on average 1 s to process each image. This makes it suitable for image-based real-time automatization of biological lab-on-a-chip experiments. Full article
(This article belongs to the Special Issue Image Segmentation Techniques: Current Status and Future Directions)
Show Figures

Figure 1

27 pages, 2559 KiB  
Article
Graphical Image Region Extraction with K-Means Clustering and Watershed
by Sandra Jardim, João António and Carlos Mora
J. Imaging 2022, 8(6), 163; https://doi.org/10.3390/jimaging8060163 - 8 Jun 2022
Cited by 15 | Viewed by 3924
Abstract
With a wide range of applications, image segmentation is a complex and difficult preprocessing step that plays an important role in automatic visual systems, which accuracy impacts, not only on segmentation results, but directly affects the effectiveness of the follow-up tasks. Despite the [...] Read more.
With a wide range of applications, image segmentation is a complex and difficult preprocessing step that plays an important role in automatic visual systems, which accuracy impacts, not only on segmentation results, but directly affects the effectiveness of the follow-up tasks. Despite the many advances achieved in the last decades, image segmentation remains a challenging problem, particularly, the segmenting of color images due to the diverse inhomogeneities of color, textures and shapes present in the descriptive features of the images. In trademark graphic images segmentation, beyond these difficulties, we must also take into account the high noise and low resolution, which are often present. Trademark graphic images can also be very heterogeneous with regard to the elements that make them up, which can be overlapping and with varying lighting conditions. Due to the immense variation encountered in corporate logos and trademark graphic images, it is often difficult to select a single method for extracting relevant image regions in a way that produces satisfactory results. Many of the hybrid approaches that integrate the Watershed and K-Means algorithms involve processing very high quality and visually similar images, such as medical images, meaning that either approach can be tweaked to work on images that follow a certain pattern. Trademark images are totally different from each other and are usually fully colored. Our system solves this difficulty given it is a generalized implementation designed to work in most scenarios, through the use of customizable parameters and completely unbiased for an image type. In this paper, we propose a hybrid approach to Image Region Extraction that focuses on automated region proposal and segmentation techniques. In particular, we analyze popular techniques such as K-Means Clustering and Watershedding and their effectiveness when deployed in a hybrid environment to be applied to a highly variable dataset. The proposed system consists of a multi-stage algorithm that takes as input an RGB image and produces multiple outputs, corresponding to the extracted regions. After preprocessing steps, a K-Means function with random initial centroids and a user-defined value for k is executed over the RGB image, generating a gray-scale segmented image, to which a threshold method is applied to generate a binary mask, containing the necessary information to generate a distance map. Then, the Watershed function is performed over the distance map, using the markers defined by the Connected Component Analysis function that labels regions on 8-way pixel connectivity, ensuring that all regions are correctly found. Finally, individual objects are labelled for extraction through a contour method, based on border following. The achieved results show adequate region extraction capabilities when processing graphical images from different datasets, where the system correctly distinguishes the most relevant visual elements of images with minimal tweaking. Full article
(This article belongs to the Special Issue Image Segmentation Techniques: Current Status and Future Directions)
Show Figures

Figure 1

24 pages, 1176 KiB  
Article
Topological Voting Method for Image Segmentation
by Nga T. T. Nguyen and Phuong B. Le
J. Imaging 2022, 8(2), 16; https://doi.org/10.3390/jimaging8020016 - 18 Jan 2022
Cited by 3 | Viewed by 2304
Abstract
Image segmentation is one of the main problems in image processing. In order to improve the accuracy of segmentation, one often creates a number of masks (annotations) for a same image and then uses some voting methods on these masks to obtain a [...] Read more.
Image segmentation is one of the main problems in image processing. In order to improve the accuracy of segmentation, one often creates a number of masks (annotations) for a same image and then uses some voting methods on these masks to obtain a more accurate mask. In this paper, we propose a voting method whose voting rule is not pixel-wise but takes into account the natural geometric-topological properties of the masks. On three concrete examples, we show that our voting method outperforms the usual arithmetical voting method. Full article
(This article belongs to the Special Issue Image Segmentation Techniques: Current Status and Future Directions)
Show Figures

Figure 1

Review

Jump to: Research

27 pages, 2464 KiB  
Review
A Review of Watershed Implementations for Segmentation of Volumetric Images
by Anton Kornilov, Ilia Safonov and Ivan Yakimchuk
J. Imaging 2022, 8(5), 127; https://doi.org/10.3390/jimaging8050127 - 26 Apr 2022
Cited by 23 | Viewed by 4431
Abstract
Watershed is a widely used image segmentation algorithm. Most researchers understand just an idea of this method: a grayscale image is considered as topographic relief, which is flooded from initial basins. However, frequently they are not aware of the options of the algorithm [...] Read more.
Watershed is a widely used image segmentation algorithm. Most researchers understand just an idea of this method: a grayscale image is considered as topographic relief, which is flooded from initial basins. However, frequently they are not aware of the options of the algorithm and the peculiarities of its realizations. There are many watershed implementations in software packages and products. Even if these packages are based on the identical algorithm–watershed, by flooding their outcomes, processing speed, and consumed memory, vary greatly. In particular, the difference among various implementations is noticeable for huge volumetric images; for instance, tomographic 3D images, for which low performance and high memory requirements of watershed might be bottlenecks. In our review, we discuss the peculiarities of algorithms with and without waterline generation, the impact of connectivity type and relief quantization level on the result, approaches for parallelization, as well as other method options. We present detailed benchmarking of seven open-source and three commercial software implementations of marker-controlled watershed for semantic or instance segmentation. We compare those software packages for one synthetic and two natural volumetric images. The aim of the review is to provide information and advice for practitioners to select the appropriate version of watershed for their problem solving. In addition, we forecast future directions of software development for 3D image segmentation by watershed. Full article
(This article belongs to the Special Issue Image Segmentation Techniques: Current Status and Future Directions)
Show Figures

Figure 1

Back to TopTop