Applications of Image and Video Processing in Medical Imaging

Topic Information

Dear Colleagues,

We invite submissions of original research articles and reviews on topics such as image reconstruction, enhancement, anomaly detection, segmentation, motion correction, modelling, and computer-aided diagnosis. Emphasis is placed on the role of artificial intelligence, machine learning, and deep learning in improving the safety, practicality, and efficacy of medical imaging in clinical applications.

This Special Issue seeks interdisciplinary contributions spanning areas such as radiology, nuclear medicine, ultrasound, interventional imaging, and telemedicine. We welcome works presenting novel algorithms and new applications, as well as those addressing challenges in big data-handling, privacy, and security.

Prof. Dr. Jyh-Cheng Chen
Prof. Dr. Kuangyu Shi
Topic Editors

Keywords

Participating Journals

Journal Name	Impact Factor	CiteScore	Launched Year	First Decision (median)	APC
Applied Sciences applsci	2.5	5.3	2011	18.4 Days	CHF 2400	Submit
Electronics electronics	2.6	5.3	2012	16.4 Days	CHF 2400	Submit
Journal of Imaging jimaging	2.7	5.9	2015	18.3 Days	CHF 1800	Submit
Machine Learning and Knowledge Extraction make	4.0	6.3	2019	20.8 Days	CHF 1800	Submit
Information information	2.4	6.9	2010	16.4 Days	CHF 1600	Submit
Big Data and Cognitive Computing BDCC	3.7	7.1	2017	25.3 Days	CHF 1800	Submit
Signals signals	-	3.2	2020	28.3 Days	CHF 1000	Submit

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Published Papers (2 papers)

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All Applied Sciences Electronics J. Imaging MAKE Information BDCC Signals

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24 pages, 1224 KiB  

Open AccessArticle

MDFormer: Transformer-Based Multimodal Fusion for Robust Chest Disease Diagnosis

by Xinlong Liu, Fei Pan, Hainan Song, Siyi Cao, Chunping Li and Tanshi Li

Electronics 2025, 14(10), 1926; https://doi.org/10.3390/electronics14101926 - 9 May 2025

Viewed by 228

Abstract

With the increasing richness of medical images and clinical data, abundant data support is provided for multimodal chest disease diagnosis methods. However, traditional multimodal fusion methods are often relatively simple, leading to insufficient exploitation of crossmodal complementary advantages. At the same time, existing [...] Read more.

With the increasing richness of medical images and clinical data, abundant data support is provided for multimodal chest disease diagnosis methods. However, traditional multimodal fusion methods are often relatively simple, leading to insufficient exploitation of crossmodal complementary advantages. At the same time, existing multimodal chest disease diagnosis methods usually focus on two modalities, and their scalability is poor when extended to three or more modalities. Moreover, in practical clinical scenarios, missing modality problems often arise due to equipment limitations or incomplete data acquisition. To address these issues, this paper proposes a novel multimodal chest disease classification model, MDFormer. This model designs a crossmodal attention fusion mechanism, MFAttention, and combines it with the Transformer architecture to construct a multimodal fusion module, MFTrans, which effectively integrates medical imaging, clinical text, and vital signs data. When extended to multiple modalities, MFTrans significantly reduces model parameters. At the same time, this paper also proposes a two-stage masked enhancement classification and contrastive learning training framework, MECCL, which significantly improves the model’s robustness and transferability. Experimental results show that MDFormer achieves a classification precision of 0.8 on the MIMIC dataset, and when 50% of the modality data are missing, the AUC can reach 85% of that of the complete data, outperforming models that did not use two-stage training. Full article

(This article belongs to the Topic Applications of Image and Video Processing in Medical Imaging)

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25 pages, 6904 KiB  

Open AccessArticle

A Weighted Facial Expression Analysis for Pain Level Estimation

by Parkpoom Chaisiriprasert and Nattapat Patchsuwan

J. Imaging 2025, 11(5), 151; https://doi.org/10.3390/jimaging11050151 - 9 May 2025

Viewed by 176

Abstract

Accurate assessment of pain intensity is critical, particularly for patients who are unable to verbally express their discomfort. This study proposes a novel weighted analytical framework that integrates facial expression analysis through action units (AUs) with a facial feature-based weighting mechanism to enhance [...] Read more.

Accurate assessment of pain intensity is critical, particularly for patients who are unable to verbally express their discomfort. This study proposes a novel weighted analytical framework that integrates facial expression analysis through action units (AUs) with a facial feature-based weighting mechanism to enhance the estimation of pain intensity. The proposed method was evaluated on a dataset comprising 4084 facial images from 25 individuals and demonstrated an average accuracy of 92.72% using the weighted pain level estimation model, in contrast to 83.37% achieved using conventional approaches. The observed improvements are primarily attributed to the strategic utilization of AU zones and expression-based weighting, which enable more precise differentiation between pain-related and non-pain-related facial movements. These findings underscore the efficacy of the proposed model in enhancing the accuracy and reliability of automated pain detection, especially in contexts where verbal communication is impaired or absent. Full article

(This article belongs to the Topic Applications of Image and Video Processing in Medical Imaging)

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