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Recent Advances in Medical Image Processing Technologies
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Recent Advances in Medical Image Processing Technologies

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

Deadline for manuscript submissions: closed (31 March 2022) | Viewed by 30331

Special Issue Editors

Dr. Elisenda Bonet

E-Mail Website
Guest Editor
1. BCNatal Fetal Medicine Researcher Center, Hospitals Clinic and Sant Joan de Déu, University of Barcelona, 08193 Barcelona, Spain
2. Escola Tècnica Superior d’Enginyeria de Telecomunicació de Barcelona (ETSETB), Universitat Politecnica de Catalunya-BarcelonaTech, 08034 Barcelona, Spain
Interests: medical imaging; MRI; ultrasound; data mining; artificial intelligence; biomedical data representation; clinical diagnosis; quantitative imaging; automatic processing pipelines; end-to-end processing
Dr. Xavier Paolo Burgos-Artizzu

E-Mail
Guest Editor
1. CSO at Transmural Biotech, 08820 Barcelona, Spain
2. Scientist at BCNatal Fetal Medicine Reasearch Center (Hospital Clínic and Hospital Sant Joan de Déu), 08193 Barcelona, Spain
Interests: computer vision; machine learning; deep learning; medical imaging; artificial intelligence; clinical diagnosis

Special Issue Information

Dear Colleagues,

The purpose of this Special Issue is to introduce the current developments in medical imaging exploiting artificial intelligence (AI) and other techniques.

The categorization and analysis of biomedical images is extremely time-consuming in both clinical and research settings. Recent advances in artificial intelligence (AI) and medical imaging have allowed the deployment of novel methods to automatically analyze large quantities of data. To name just a few examples, they can be used to classify images, detect structures of interest, aid clinicians in performing more accurate clinical diagnoses or even improve the precision of surgeries through visual guidance. These methods could herald a novel era of personalized and more precise medicine, especially given that biomedical images provide information beyond the detectability capacity of the human eye and that current AI methods are capable of surpassing the performance of humans in many tasks.

The exploitation of these novel techniques brings the possibility of multiple different applications. Potential contributions to this Special Issue should focus on any application of AI methods to the processing and analysis of clinical data such as patient clinical history, images or videos, with the clear goal of aiding clinicians in improving current clinical care.

We look forward to receiving your contributions.

Dr. Elisenda Bonet
Dr. Xavier Paolo Burgos-Artizzu
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

  • artificial intelligence
  • computer vision
  • intelligent sensors
  • biomedical video processing and understanding
  • biomedical image processing and understanding
  • biomedical signal processing
  • biomedical multimodal processing and understanding
  • deep learning and machine learning
  • diseases
  • aid diagnosis
  • supervised, semi-supervised and unsupervised learning
  • data representation
  • summarization and visualization
  • modeling
  • data mining
  • image and video segmentation and classification
  • guided clinical surgery
  • real-time image and video processing

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Further information on MDPI's Special Issue polices can be found here.

Published Papers (6 papers)

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Research

16 pages, 45713 KiB  
Article
A Novel Method Based on GAN Using a Segmentation Module for Oligodendroglioma Pathological Image Generation
by Juwon Kweon, Jisang Yoo, Seungjong Kim, Jaesik Won and Soonchul Kwon
Sensors 2022, 22(10), 3960; https://doi.org/10.3390/s22103960 - 23 May 2022
Cited by 7 | Viewed by 3357
Abstract
Digital pathology analysis using deep learning has been the subject of several studies. As with other medical data, pathological data are not easily obtained. Because deep learning-based image analysis requires large amounts of data, augmentation techniques are used to increase the size of [...] Read more.
Digital pathology analysis using deep learning has been the subject of several studies. As with other medical data, pathological data are not easily obtained. Because deep learning-based image analysis requires large amounts of data, augmentation techniques are used to increase the size of pathological datasets. This study proposes a novel method for synthesizing brain tumor pathology data using a generative model. For image synthesis, we used embedding features extracted from a segmentation module in a general generative model. We also introduce a simple solution for training a segmentation model in an environment in which the masked label of the training dataset is not supplied. As a result of this experiment, the proposed method did not make great progress in quantitative metrics but showed improved results in the confusion rate of more than 70 subjects and the quality of the visual output. Full article
(This article belongs to the Special Issue Recent Advances in Medical Image Processing Technologies)
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16 pages, 3146 KiB  
Article
Hyperconnected Openings Codified in a Max Tree Structure: An Application for Skull-Stripping in Brain MRI T1
by Carlos Paredes-Orta, Jorge Domingo Mendiola-Santibañez, Danjela Ibrahimi, Juvenal Rodríguez-Reséndiz, Germán Díaz-Florez and Carlos Alberto Olvera-Olvera
Sensors 2022, 22(4), 1378; https://doi.org/10.3390/s22041378 - 11 Feb 2022
Cited by 8 | Viewed by 2098
Abstract
This article presents two procedures involving a maximal hyperconnected function and a hyperconnected lower leveling to segment the brain in a magnetic resonance imaging T1 weighted using new openings on a max-tree structure. The openings are hyperconnected and are viscous transformations. The first [...] Read more.
This article presents two procedures involving a maximal hyperconnected function and a hyperconnected lower leveling to segment the brain in a magnetic resonance imaging T1 weighted using new openings on a max-tree structure. The openings are hyperconnected and are viscous transformations. The first procedure considers finding the higher hyperconnected maximum by using an increasing criterion that plays a central role during segmentation. The second procedure utilizes hyperconnected lower leveling, which acts as a marker, controlling the reconstruction process into the mask. As a result, the proposal allows an efficient segmentation of the brain to be obtained. In total, 38 magnetic resonance T1-weighted images obtained from the Internet Brain Segmentation Repository are segmented. The Jaccard and Dice indices are computed, compared, and validated with the efficiency of the Brain Extraction Tool software and other algorithms provided in the literature. Full article
(This article belongs to the Special Issue Recent Advances in Medical Image Processing Technologies)
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18 pages, 4743 KiB  
Article
AI Based Monitoring of Different Risk Levels in COVID-19 Context
by César Melo, Sandra Dixe, Jaime C. Fonseca, António H. J. Moreira and João Borges
Sensors 2022, 22(1), 298; https://doi.org/10.3390/s22010298 - 31 Dec 2021
Cited by 6 | Viewed by 2490
Abstract
COVID-19 was responsible for devastating social, economic, and political effects all over the world. Although the health authorities imposed restrictions provided relief and assisted with trying to return society to normal life, it is imperative to monitor people’s behavior and risk factors to [...] Read more.
COVID-19 was responsible for devastating social, economic, and political effects all over the world. Although the health authorities imposed restrictions provided relief and assisted with trying to return society to normal life, it is imperative to monitor people’s behavior and risk factors to keep virus transmission levels as low as possible. This article focuses on the application of deep learning algorithms to detect the presence of masks on people in public spaces (using RGB cameras), as well as the detection of the caruncle in the human eye area to make an accurate measurement of body temperature (using thermal cameras). For this task, synthetic data generation techniques were used to create hybrid datasets from public ones to train state-of-the-art algorithms, such as YOLOv5 object detector and a keypoint detector based on Resnet-50. For RGB mask detection, YOLOv5 achieved an average precision of 82.4%. For thermal masks, glasses, and caruncle detection, YOLOv5 and keypoint detector achieved an average precision of 96.65% and 78.7%, respectively. Moreover, RGB and thermal datasets were made publicly available. Full article
(This article belongs to the Special Issue Recent Advances in Medical Image Processing Technologies)
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14 pages, 2808 KiB  
Article
Generative Adversarial Networks to Improve Fetal Brain Fine-Grained Plane Classification
by Alberto Montero, Elisenda Bonet-Carne and Xavier Paolo Burgos-Artizzu
Sensors 2021, 21(23), 7975; https://doi.org/10.3390/s21237975 - 29 Nov 2021
Cited by 23 | Viewed by 4205
Abstract
Generative adversarial networks (GANs) have been recently applied to medical imaging on different modalities (MRI, CT, X-ray, etc). However there are not many applications on ultrasound modality as a data augmentation technique applied to downstream classification tasks. This study aims to explore and [...] Read more.
Generative adversarial networks (GANs) have been recently applied to medical imaging on different modalities (MRI, CT, X-ray, etc). However there are not many applications on ultrasound modality as a data augmentation technique applied to downstream classification tasks. This study aims to explore and evaluate the generation of synthetic ultrasound fetal brain images via GANs and apply them to improve fetal brain ultrasound plane classification. State of the art GANs stylegan2-ada were applied to fetal brain image generation and GAN-based data augmentation classifiers were compared with baseline classifiers. Our experimental results show that using data generated by both GANs and classical augmentation strategies allows for increasing the accuracy and area under the curve score. Full article
(This article belongs to the Special Issue Recent Advances in Medical Image Processing Technologies)
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25 pages, 6361 KiB  
Article
An Automatic Detection and Classification System of Five Stages for Hypertensive Retinopathy Using Semantic and Instance Segmentation in DenseNet Architecture
by Qaisar Abbas, Imran Qureshi and Mostafa E. A. Ibrahim
Sensors 2021, 21(20), 6936; https://doi.org/10.3390/s21206936 - 19 Oct 2021
Cited by 25 | Viewed by 11319
Abstract
The stage and duration of hypertension are connected to the occurrence of Hypertensive Retinopathy (HR) of eye disease. Currently, a few computerized systems have been developed to recognize HR by using only two stages. It is difficult to define specialized features to recognize [...] Read more.
The stage and duration of hypertension are connected to the occurrence of Hypertensive Retinopathy (HR) of eye disease. Currently, a few computerized systems have been developed to recognize HR by using only two stages. It is difficult to define specialized features to recognize five grades of HR. In addition, deep features have been used in the past, but the classification accuracy is not up-to-the-mark. In this research, a new hypertensive retinopathy (HYPER-RETINO) framework is developed to grade the HR based on five grades. The HYPER-RETINO system is implemented based on pre-trained HR-related lesions. To develop this HYPER-RETINO system, several steps are implemented such as a preprocessing, the detection of HR-related lesions by semantic and instance-based segmentation and a DenseNet architecture to classify the stages of HR. Overall, the HYPER-RETINO system determined the local regions within input retinal fundus images to recognize five grades of HR. On average, a 10-fold cross-validation test obtained sensitivity (SE) of 90.5%, specificity (SP) of 91.5%, accuracy (ACC) of 92.6%, precision (PR) of 91.7%, Matthews correlation coefficient (MCC) of 61%, F1-score of 92% and area-under-the-curve (AUC) of 0.915 on 1400 HR images. Thus, the applicability of the HYPER-RETINO method to reliably diagnose stages of HR is verified by experimental findings. Full article
(This article belongs to the Special Issue Recent Advances in Medical Image Processing Technologies)
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18 pages, 9059 KiB  
Article
Neovascularization Detection and Localization in Fundus Images Using Deep Learning
by Michael Chi Seng Tang, Soo Siang Teoh, Haidi Ibrahim and Zunaina Embong
Sensors 2021, 21(16), 5327; https://doi.org/10.3390/s21165327 - 6 Aug 2021
Cited by 30 | Viewed by 3838
Abstract
Proliferative Diabetic Retinopathy (PDR) is a severe retinal disease that threatens diabetic patients. It is characterized by neovascularization in the retina and the optic disk. PDR clinical features contain highly intense retinal neovascularization and fibrous spreads, leading to visual distortion if not controlled. [...] Read more.
Proliferative Diabetic Retinopathy (PDR) is a severe retinal disease that threatens diabetic patients. It is characterized by neovascularization in the retina and the optic disk. PDR clinical features contain highly intense retinal neovascularization and fibrous spreads, leading to visual distortion if not controlled. Different image processing techniques have been proposed to detect and diagnose neovascularization from fundus images. Recently, deep learning methods are getting popular in neovascularization detection due to artificial intelligence advancement in biomedical image processing. This paper presents a semantic segmentation convolutional neural network architecture for neovascularization detection. First, image pre-processing steps were applied to enhance the fundus images. Then, the images were divided into small patches, forming a training set, a validation set, and a testing set. A semantic segmentation convolutional neural network was designed and trained to detect the neovascularization regions on the images. Finally, the network was tested using the testing set for performance evaluation. The proposed model is entirely automated in detecting and localizing neovascularization lesions, which is not possible with previously published methods. Evaluation results showed that the model could achieve accuracy, sensitivity, specificity, precision, Jaccard similarity, and Dice similarity of 0.9948, 0.8772, 0.9976, 0.8696, 0.7643, and 0.8466, respectively. We demonstrated that this model could outperform other convolutional neural network models in neovascularization detection. Full article
(This article belongs to the Special Issue Recent Advances in Medical Image Processing Technologies)
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