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Mathematics
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Machine Learning in Image Processing and Pattern Recognition: Modern Methods...
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Machine Learning in Image Processing and Pattern Recognition: Modern Methods and Applications

A special issue of Mathematics (ISSN 2227-7390). This special issue belongs to the section "Mathematics and Computer Science".

Deadline for manuscript submissions: closed (31 January 2022) | Viewed by 10311

Special Issue Editors

Dr. Darian M. Onchis

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Guest Editor
Department of Computer Science, Faculty of Mathematics and Computer Science, West University of Timisoara, 300223 Timisoara, Romania
Interests: machine learning; signal processing; image processing; bioinformatics
Dr. Dan Pescaru

E-Mail Website
Guest Editor
Computer Science Department, Politehnica University Timisoara, 300223 Timișoara, Romania
Interests: cyberphisical systems; computer vision; databases; artifical inteligence
Dr. Flavia Micota

E-Mail Website
Guest Editor
Department of Computer Science, Faculty of Mathematics and Computer Science, West University of Timisoara, 300223 Timisoara, Romania
Interests: data mining; soft computing
Prof. Dr. Pedro Real

E-Mail Website
Guest Editor
Higher Technical School of Informatics Engineering, University of Seville, Avda. Reina Mercedes, s/n, 41012 Seville, Spain
Interests: topological pattern recognition; combinatorial topology; topological dynamics; topological models for digital images; cocyclic error-correcting codes
Dr. Codruta Istin

E-Mail Website
Guest Editor
Computer Science Department, Politehnica University Timisoara, 300223 Timișoara, Romania
Interests: bio-inspired computing systems; software engineering; programming languages and compilers; signal acquisition and processing; artificial intelligence; machine learning; neural networks

Special Issue Information

Dear colleagues,

Machine learning research is continuously progressing at an amazing speed for a scientific discipline. The so-called “classical machine learning”, a prequel to the current deep learning-based AI summer, provided us with interesting algorithms for image processing and pattern recognition. However, nowadays, we are experiencing new developments emerging from everywhere in the world and the improvements seen in the algorithms in recent years have completely remodeled the field.

Therefore, the purpose of this Special Issue is to stimulate a systematic incursion in the modern methods of machine learning for image processing and pattern recognition and to correlate them with practical applications. Of particular interest are papers trying to connect the algebraic topology methods with machine learning models for a better study and evaluation of datasets and their organization, papers concerning the efficient training of neural networks using bio-inspired heuristics, papers involving explainable machine learning perspectives or papers dealing with novel incremental learning algorithms.

From the application side, we welcome papers introducing novel machine learning models for vision, bioinformatics, medical image processing and diagnosis or automatic video analysis and scene understanding. Of course, we are not limiting ourselves to only these topics and any theoretically solid contribution to the field of machine learning related to image processing or pattern recognition will be considered.

Dr. Darian M. Onchis
Dr. Dan Pescaru
Dr. Flavia Micota
Dr. Pedro Real
Dr. Codruta Istin
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. Mathematics 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

  • Neural networks
  • Explainable artificial intelligence (XAI)
  • Algebraic topology
  • Bioinformatics
  • Medical pattern recognition
  • Incremental learning

Published Papers (4 papers)

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Research

23 pages, 10003 KiB  
Article
Ensemble of 2D Residual Neural Networks Integrated with Atrous Spatial Pyramid Pooling Module for Myocardium Segmentation of Left Ventricle Cardiac MRI
by Iftikhar Ahmad, Abdul Qayyum, Brij B. Gupta, Madini O. Alassafi and Rayed A. AlGhamdi
Mathematics 2022, 10(4), 627; https://0-doi-org.brum.beds.ac.uk/10.3390/math10040627 - 17 Feb 2022
Cited by 6 | Viewed by 1664
Abstract
Cardiac disease diagnosis and identification is problematic mostly by inaccurate segmentation of the cardiac left ventricle (LV). Besides, LV segmentation is challenging since it involves complex and variable cardiac structures in terms of components and the intricacy of time-based crescendos. In addition, full [...] Read more.
Cardiac disease diagnosis and identification is problematic mostly by inaccurate segmentation of the cardiac left ventricle (LV). Besides, LV segmentation is challenging since it involves complex and variable cardiac structures in terms of components and the intricacy of time-based crescendos. In addition, full segmentation and quantification of the LV myocardium border is even more challenging because of different shapes and sizes of the myocardium border zone. The foremost purpose of this research is to design a precise automatic segmentation technique employing deep learning models for the myocardium border using cardiac magnetic resonance imaging (MRI). The ASPP module (Atrous Spatial Pyramid Pooling) was integrated with a proposed 2D-residual neural network for segmentation of the myocardium border using a cardiac MRI dataset. Further, the ensemble technique based on a majority voting ensemble method was used to blend the results of recent deep learning models on different set of hyperparameters. The proposed model produced an 85.43% dice score on validation samples and 98.23% on training samples and provided excellent performance compared to recent deep learning models. The myocardium border was successfully segmented across diverse subject slices with different shapes, sizes and contrast using the proposed deep learning ensemble models. The proposed model can be employed for automatic detection and segmentation of the myocardium border for precise quantification of reflow, myocardial infarction, myocarditis, and h cardiomyopathy (HCM) for clinical applications. Full article
(This article belongs to the Special Issue Machine Learning in Image Processing and Pattern Recognition: Modern Methods and Applications)
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32 pages, 51304 KiB  
Article
INF-GAN: Generative Adversarial Network for Illumination Normalization of Finger-Vein Images
by Jin Seong Hong, Jiho Choi, Seung Gu Kim, Muhammad Owais and Kang Ryoung Park
Mathematics 2021, 9(20), 2613; https://0-doi-org.brum.beds.ac.uk/10.3390/math9202613 - 17 Oct 2021
Cited by 3 | Viewed by 1999
Abstract
When images are acquired for finger-vein recognition, images with nonuniformity of illumination are often acquired due to varying thickness of fingers or nonuniformity of illumination intensity elements. Accordingly, the recognition performance is significantly reduced as the features being recognized are deformed. To address [...] Read more.
When images are acquired for finger-vein recognition, images with nonuniformity of illumination are often acquired due to varying thickness of fingers or nonuniformity of illumination intensity elements. Accordingly, the recognition performance is significantly reduced as the features being recognized are deformed. To address this issue, previous studies have used image preprocessing methods, such as grayscale normalization or score-level fusion methods for multiple recognition models, which may improve performance in images with a low degree of nonuniformity of illumination. However, the performance cannot be improved drastically when certain parts of images are saturated due to a severe degree of nonuniformity of illumination. To overcome these drawbacks, this study newly proposes a generative adversarial network for the illumination normalization of finger-vein images (INF-GAN). In the INF-GAN, a one-channel image containing texture information is generated through a residual image generation block, and finger-vein texture information deformed by the severe nonuniformity of illumination is restored, thus improving the recognition performance. The proposed method using the INF-GAN exhibited a better performance compared with state-of-the-art methods when the experiment was conducted using two open databases, the Hong Kong Polytechnic University finger-image database version 1, and the Shandong University homologous multimodal traits finger-vein database. Full article
(This article belongs to the Special Issue Machine Learning in Image Processing and Pattern Recognition: Modern Methods and Applications)
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17 pages, 1932 KiB  
Article
Multi-Output Learning Based on Multimodal GCN and Co-Attention for Image Aesthetics and Emotion Analysis
by Haotian Miao, Yifei Zhang, Daling Wang and Shi Feng
Mathematics 2021, 9(12), 1437; https://0-doi-org.brum.beds.ac.uk/10.3390/math9121437 - 20 Jun 2021
Cited by 14 | Viewed by 2407
Abstract
With the development of social networks and intelligent terminals, it is becoming more convenient to share and acquire images. The massive growth of the number of social images makes people have higher demands for automatic image processing, especially in the aesthetic and emotional [...] Read more.
With the development of social networks and intelligent terminals, it is becoming more convenient to share and acquire images. The massive growth of the number of social images makes people have higher demands for automatic image processing, especially in the aesthetic and emotional perspective. Both aesthetics assessment and emotion recognition require a higher ability for the computer to simulate high-level visual perception understanding, which belongs to the field of image processing and pattern recognition. However, existing methods often ignore the prior knowledge of images and intrinsic relationships between aesthetic and emotional perspectives. Recently, machine learning and deep learning have become powerful methods for researchers to solve mathematical problems in computing, such as image processing and pattern recognition. Both images and abstract concepts can be converted into numerical matrices and then establish the mapping relations using mathematics on computers. In this work, we propose an end-to-end multi-output deep learning model based on multimodal Graph Convolutional Network (GCN) and co-attention for aesthetic and emotion conjoint analysis. In our model, a stacked multimodal GCN network is proposed to encode the features under the guidance of the correlation matrix, and a co-attention module is designed to help the aesthetics and emotion feature representation learn from each other interactively. Experimental results indicate that our proposed model achieves competitive performance on the IAE dataset. Progressive results on the AVA and ArtPhoto datasets also prove the generalization ability of our model. Full article
(This article belongs to the Special Issue Machine Learning in Image Processing and Pattern Recognition: Modern Methods and Applications)
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15 pages, 2335 KiB  
Article
A Residual-Learning-Based Multi-Scale Parallel-Convolutions- Assisted Efficient CAD System for Liver Tumor Detection
by Muazzam Maqsood, Maryam Bukhari, Zeeshan Ali, Saira Gillani, Irfan Mehmood, Seungmin Rho and Young-Ae Jung
Mathematics 2021, 9(10), 1133; https://0-doi-org.brum.beds.ac.uk/10.3390/math9101133 - 17 May 2021
Cited by 21 | Viewed by 2498
Abstract
Smart multimedia-based medical analytics and decision-making systems are of prime importance in the healthcare sector. Liver cancer is commonly stated to be the sixth most widely diagnosed cancer and requires an early diagnosis to help with treatment planning. Liver tumors have similar intensity [...] Read more.
Smart multimedia-based medical analytics and decision-making systems are of prime importance in the healthcare sector. Liver cancer is commonly stated to be the sixth most widely diagnosed cancer and requires an early diagnosis to help with treatment planning. Liver tumors have similar intensity levels and contrast as compared to neighboring tissues. Similarly, irregular tumor shapes are another major issue that depends on the cancer stage and tumor type. Generally, liver tumor segmentation comprises two steps: the first one involves liver identification, and the second stage involves tumor segmentation. This research work performed tumor segmentation directly from a CT scan, which tends to be more difficult and important. We propose an efficient algorithm that employs multi-scale parallel convolution blocks (MPCs) and Res blocks based on residual learning. The fundamental idea of utilizing multi-scale parallel convolutions of varying filter sizes in MPCs is to extract multi-scale features for different tumor sizes. Moreover, the utilization of residual connections and residual blocks helps to extract rich features with a reduced number of parameters. Moreover, the proposed work requires no post-processing techniques to refine the segmentation. The proposed work was evaluated using the 3DIRCADb dataset and achieved a Dice score of 77.15% and 93% accuracy. Full article
(This article belongs to the Special Issue Machine Learning in Image Processing and Pattern Recognition: Modern Methods and Applications)
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