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Innovations in Biomedical Imaging

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

Deadline for manuscript submissions: closed (31 December 2023) | Viewed by 32251

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Special Issue Editors

Prof. Dr. Ewa Pietka

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Guest Editor

Faculty of Biomedical Engineering, Silesian University of Technology, 41-800 Zabrze, Poland
Interests: imaging informatics; computer assisted diagnosis; treatment and rehabilitation; image navigation in minimally invasive surgery

Prof. Dr. Arkadiusz Gertych

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Guest Editor

Department of Surgery, Cedars-Sinai Medical Center, 8700 Beverly Blvd, Los Angeles, CA 90048, USA
Interests: imaging informatics; artificial intelligence; bioinformatics; computational oncology

Prof. Dr. Wojciech Więcławek

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Guest Editor

Department of Informatics and Medical Equipment, Silesian University of Technology, Roosevelta 40, 41-800 Zabrze, Poland
Interests: imaging informatics; bioelectronics; biomedical engineering

Special Issue Information

Dear Colleagues,

The continuous growth in the amount of biomedical data and variety of multimodal content necessitates the demand for a fast and reliable technology able to ensure a modern healthcare enterprise. Bridging the gap among physicians, scientists, and engineers for the benefit of patients has become the most common way to address healthcare needs. Biomedical engineering is also recognized as a research and development frontier in employing new technology in clinical, pre-clinical, and outside of clinical environments. Technological assistance can be found in the prevention, disease diagnosis, treatment planning, surgical navigation, and modeling of body mechanics rehabilitation. Homecare support for any type of disability may improve the standard of living, safety, and comfort, particularly for geriatric patients.

A major aim of biomedical engineering is to provide new solutions to problems across multiple medical disciplines to generate new quantitative data and enable the testing of new hypotheses. The objective of this Special Issue is to publish state-of-the-art research contributions, which address advances in the area of biomedical engineering, personalized medicine, computer-assisted diagnosis, treatment, and rehabilitation. This scope could be further specialized to cover biomedical engineering applications in imaging, neuroscience, artificial intelligence, computational oncology, bioelectronics, biomechanics, biomaterials, and implants, and artificial organs.

Prof. Dr. Ewa Pietka
Prof. Dr. Arkadiusz Gertych
Prof. Dr. Wojciech Więcławek
Guest Editors

If you want to learn more information or need any advice, you can contact the Special Issue Editor Vesna Marinkovic via <[email protected]> directly.

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Keywords

imaging informatics
neuroscience
artificial intelligence
computational oncology
bioinformatics
bioelectronics
biomechanics
biomaterials
implants and artificial organs

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Published Papers (9 papers)

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Research

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19 pages, 7537 KiB

Open AccessArticle

Three-Dimensional Segmentation Assisted with Clustering Analysis for Surface and Volume Measurements of Equine Incisor in Multidetector Computed Tomography Data Sets

by Marta Borowska, Tomasz Jasiński, Sylwia Gierasimiuk, Jolanta Pauk, Bernard Turek, Kamil Górski and Małgorzata Domino

Sensors 2023, 23(21), 8940; https://doi.org/10.3390/s23218940 - 2 Nov 2023

Viewed by 844

Abstract

Dental diagnostic imaging has progressed towards the use of advanced technologies such as 3D image processing. Since multidetector computed tomography (CT) is widely available in equine clinics, CT-based anatomical 3D models, segmentations, and measurements have become clinically applicable. This study aimed to use a 3D segmentation of CT images and volumetric measurements to investigate differences in the surface area and volume of equine incisors. The 3D Slicer was used to segment single incisors of 50 horses’ heads and to extract volumetric features. Axial vertical symmetry, but not horizontal, of the incisors was evidenced. The surface area and volume differed significantly between temporary and permanent incisors, allowing for easy eruption-related clustering of the CT-based 3D images with an accuracy of >0.75. The volumetric features differed partially between center, intermediate, and corner incisors, allowing for moderate location-related clustering with an accuracy of >0.69. The volumetric features of mandibular incisors’ equine odontoclastic tooth resorption and hypercementosis (EOTRH) degrees were more than those for maxillary incisors; thus, the accuracy of EOTRH degree-related clustering was >0.72 for the mandibula and >0.33 for the maxilla. The CT-based 3D images of equine incisors can be successfully segmented using the routinely achieved multidetector CT data sets and the proposed data-processing approaches. Full article

(This article belongs to the Special Issue Innovations in Biomedical Imaging)

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18 pages, 1843 KiB

Open AccessArticle

Computer-Aided Ankle Ligament Injury Diagnosis from Magnetic Resonance Images Using Machine Learning Techniques

by Rodrigo S. Astolfi, Daniel S. da Silva, Ingrid S. Guedes, Caio S. Nascimento, Robertas Damaševičius, Senthil K. Jagatheesaperumal, Victor Hugo C. de Albuquerque and José Alberto D. Leite

Sensors 2023, 23(3), 1565; https://doi.org/10.3390/s23031565 - 1 Feb 2023

Cited by 2 | Viewed by 2847

Abstract

Ankle injuries caused by the Anterior Talofibular Ligament (ATFL) are the most common type of injury. Thus, finding new ways to analyze these injuries through novel technologies is critical for assisting medical diagnosis and, as a result, reducing the subjectivity of this process. As a result, the purpose of this study is to compare the ability of specialists to diagnose lateral tibial tuberosity advancement (LTTA) injury using computer vision analysis on magnetic resonance imaging (MRI). The experiments were carried out on a database obtained from the Vue PACS–Carestream software, which contained 132 images of ATFL and normal (healthy) ankles. Because there were only a few images, image augmentation techniques was used to increase the number of images in the database. Following that, various feature extraction algorithms (GLCM, LBP, and HU invariant moments) and classifiers such as Multi-Layer Perceptron (MLP), Support Vector Machine (SVM), k-Nearest Neighbors (kNN), and Random Forest (RF) were used. Based on the results from this analysis, for cases that lack clear morphologies, the method delivers a hit rate of 85.03% with an increase of 22% over the human expert-based analysis. Full article

(This article belongs to the Special Issue Innovations in Biomedical Imaging)

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21 pages, 1883 KiB

Open AccessArticle

Atlas-Based Segmentation in Extraction of Knee Joint Bone Structures from CT and MR

by Piotr Zarychta

Sensors 2022, 22(22), 8960; https://doi.org/10.3390/s22228960 - 19 Nov 2022

Cited by 4 | Viewed by 3933

Abstract

The main goal of the approach proposed in this study, which is dedicated to the extraction of bone structures of the knee joint (femoral head, tibia, and patella), was to show a fully automated method of extracting these structures based on atlas segmentation. In order to realize the above-mentioned goal, an algorithm employed automated image-matching as the first step, followed by the normalization of clinical images and the determination of the 11-element dataset to which all scans in the series were allocated. This allowed for a delineation of the average feature vector for the teaching group in the next step, which automated and streamlined known fuzzy segmentation methods (fuzzy c-means (FCM), fuzzy connectedness (FC)). These averaged features were then transmitted to the FCM and FC methods, which were implemented for the testing group and correspondingly for each scan. In this approach, two features are important: the centroids (which become starting points for the fuzzy methods) and the surface area of the extracted bone structure (protects against over-segmentation). This proposed approach was implemented in MATLAB and tested in 61 clinical CT studies of the lower limb on the transverse plane and in 107 T1-weighted MRI studies of the knee joint on the sagittal plane. The atlas-based segmentation combined with the fuzzy methods achieved a Dice index of 85.52–89.48% for the bone structures of the knee joint. Full article

(This article belongs to the Special Issue Innovations in Biomedical Imaging)

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20 pages, 2390 KiB

Open AccessArticle

Computer Aided Written Character Feature Extraction in Progressive Supranuclear Palsy and Parkinson’s Disease

by Paula Stępień, Jacek Kawa, Emilia J. Sitek, Dariusz Wieczorek, Rafał Sikorski, Magda Dąbrowska, Jarosław Sławek and Ewa Pietka

Sensors 2022, 22(4), 1688; https://doi.org/10.3390/s22041688 - 21 Feb 2022

Viewed by 2308

Abstract

Parkinson’s disease (PD) and progressive supranuclear palsy (PSP) are neurodegenerative movement disorders associated with cognitive dysfunction. The Luria’s Alternating Series Test (LAST) is a clinical tool sensitive to both graphomotor problems and perseverative tendencies that may suggest the dysfunction of prefrontal and/or frontostriatal areas and may be used in PD and PSP assessment. It requires the participant to draw a series of alternating triangles and rectangles. In the study, two clinical groups—51 patients with PD and 22 patients with PSP—were compared to 32 neurologically intact seniors. Participants underwent neuropsychological assessment. The LAST was administered in a paper and pencil version, then scanned and preprocessed. The series was automatically divided into characters, and the shapes were recognized as rectangles or triangles. In the feature extraction step, each rectangle and triangle was regarded both as an image and a two-dimensional signal, separately and as a part of the series. Standard and novel features were extracted and normalized using characters written by the examiner. Out of 71 proposed features, 51 differentiated the groups (p < 0.05). A classifier showed an accuracy of 70.5% for distinguishing three groups. Full article

(This article belongs to the Special Issue Innovations in Biomedical Imaging)

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18 pages, 4195 KiB

Open AccessArticle

Ultra-Widefield Fluorescein Angiography Image Brightness Compensation Based on Geometrical Features

by Wojciech Więcławek, Marta Danch-Wierzchowska, Marcin Rudzki, Bogumiła Sędziak-Marcinek and Slawomir Jan Teper

Sensors 2022, 22(1), 12; https://doi.org/10.3390/s22010012 - 21 Dec 2021

Cited by 3 | Viewed by 2577

Abstract

Ultra-widefield fluorescein angiography (UWFA) is an emerging imaging modality used to characterise pathologies in the retinal vasculature, such as microaneurysms (MAs) and vascular leakages. Despite its potential value for diagnosis and disease screening, objective quantitative assessment of retinal pathologies by UWFA is currently limited because laborious manual processing is required. In this report, we describe a geometrical method for uneven brightness compensation inherent to UWFA imaging technique. The correction function is based on the geometrical eyeball shape, therefore it is fully automated and depends only on pixel distance from the center of the imaged retina. The method’s performance was assessed on a database containing 256 UWFA images with the use of several image quality measures that show the correction method improves image quality. The method is also compared to the commonly used CLAHE approach and was also employed in a pilot study for vascular segmentation, giving a noticeable improvement in segmentation results. Therefore, the method can be used as an image preprocessing step in retinal UWFA image analysis. Full article

(This article belongs to the Special Issue Innovations in Biomedical Imaging)

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16 pages, 6884 KiB

Open AccessArticle

Real-Time Back Surface Landmark Determination Using a Time-of-Flight Camera

by Daniel Ledwoń, Marta Danch-Wierzchowska, Marcin Bugdol, Karol Bibrowicz, Tomasz Szurmik, Andrzej Myśliwiec and Andrzej W. Mitas

Sensors 2021, 21(19), 6425; https://doi.org/10.3390/s21196425 - 26 Sep 2021

Cited by 5 | Viewed by 2373

Abstract

Postural disorders, their prevention, and therapies are still growing modern problems. The currently used diagnostic methods are questionable due to the exposure to side effects (radiological methods) as well as being time-consuming and subjective (manual methods). Although the computer-aided diagnosis of posture disorders is well developed, there is still the need to improve existing solutions, search for new measurement methods, and create new algorithms for data processing. Based on point clouds from a Time-of-Flight camera, the presented method allows a non-contact, real-time detection of anatomical landmarks on the subject’s back and, thus, an objective determination of trunk surface metrics. Based on a comparison of the obtained results with the evaluation of three independent experts, the accuracy of the obtained results was confirmed. The average distance between the expert indications and method results for all landmarks was 27.73 mm. A direct comparison showed that the compared differences were statically significantly different; however, the effect was negligible. Compared with other automatic anatomical landmark detection methods, ours has a similar accuracy with the possibility of real-time analysis. The advantages of the presented method are non-invasiveness, non-contact, and the possibility of continuous observation, also during exercise. The proposed solution is another step in the general trend of objectivization in physiotherapeutic diagnostics. Full article

(This article belongs to the Special Issue Innovations in Biomedical Imaging)

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17 pages, 20600 KiB

Open AccessArticle

Deep Learning-Based High-Frequency Ultrasound Skin Image Classification with Multicriteria Model Evaluation

by Joanna Czajkowska, Pawel Badura, Szymon Korzekwa, Anna Płatkowska-Szczerek and Monika Słowińska

Sensors 2021, 21(17), 5846; https://doi.org/10.3390/s21175846 - 30 Aug 2021

Cited by 9 | Viewed by 2851

Abstract

This study presents the first application of convolutional neural networks to high-frequency ultrasound skin image classification. This type of imaging opens up new opportunities in dermatology, showing inflammatory diseases such as atopic dermatitis, psoriasis, or skin lesions. We collected a database of 631 images with healthy skin and different skin pathologies to train and assess all stages of the methodology. The proposed framework starts with the segmentation of the epidermal layer using a DeepLab v3+ model with a pre-trained Xception backbone. We employ transfer learning to train the segmentation model for two purposes: to extract the region of interest for classification and to prepare the skin layer map for classification confidence estimation. For classification, we train five models in different input data modes and data augmentation setups. We also introduce a classification confidence level to evaluate the deep model’s reliability. The measure combines our skin layer map with the heatmap produced by the Grad-CAM technique designed to indicate image regions used by the deep model to make a classification decision. Moreover, we propose a multicriteria model evaluation measure to select the optimal model in terms of classification accuracy, confidence, and test dataset size. The experiments described in the paper show that the DenseNet-201 model fed with the extracted region of interest produces the most reliable and accurate results. Full article

(This article belongs to the Special Issue Innovations in Biomedical Imaging)

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21 pages, 1041 KiB

Open AccessArticle

Affective State during Physiotherapy and Its Analysis Using Machine Learning Methods

by Patrycja Romaniszyn-Kania, Anita Pollak, Marcin D. Bugdol, Monika N. Bugdol, Damian Kania, Anna Mańka, Marta Danch-Wierzchowska and Andrzej W. Mitas

Sensors 2021, 21(14), 4853; https://doi.org/10.3390/s21144853 - 16 Jul 2021

Cited by 8 | Viewed by 3207

Abstract

Invasive or uncomfortable procedures especially during healthcare trigger emotions. Technological development of the equipment and systems for monitoring and recording psychophysiological functions enables continuous observation of changes to a situation responding to a situation. The presented study aimed to focus on the analysis of the individual’s affective state. The results reflect the excitation expressed by the subjects’ statements collected with psychological questionnaires. The research group consisted of 49 participants (22 women and 25 men). The measurement protocol included acquiring the electrodermal activity signal, cardiac signals, and accelerometric signals in three axes. Subjective measurements were acquired for affective state using the JAWS questionnaires, for cognitive skills the DST, and for verbal fluency the VFT. The physiological and psychological data were subjected to statistical analysis and then to a machine learning process using different features selection methods (JMI or PCA). The highest accuracy of the kNN classifier was achieved in combination with the JMI method (81.63%) concerning the division complying with the JAWS test results. The classification sensitivity and specificity were 85.71% and 71.43%. Full article

(This article belongs to the Special Issue Innovations in Biomedical Imaging)

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Review

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35 pages, 3069 KiB

Open AccessReview

A Review on Computer Aided Diagnosis of Acute Brain Stroke

by Mahesh Anil Inamdar, Udupi Raghavendra, Anjan Gudigar, Yashas Chakole, Ajay Hegde, Girish R. Menon, Prabal Barua, Elizabeth Emma Palmer, Kang Hao Cheong, Wai Yee Chan, Edward J. Ciaccio and U. Rajendra Acharya

Sensors 2021, 21(24), 8507; https://doi.org/10.3390/s21248507 - 20 Dec 2021

Cited by 27 | Viewed by 8283

Abstract

Amongst the most common causes of death globally, stroke is one of top three affecting over 100 million people worldwide annually. There are two classes of stroke, namely ischemic stroke (due to impairment of blood supply, accounting for ~70% of all strokes) and hemorrhagic stroke (due to bleeding), both of which can result, if untreated, in permanently damaged brain tissue. The discovery that the affected brain tissue (i.e., ‘ischemic penumbra’) can be salvaged from permanent damage and the bourgeoning growth in computer aided diagnosis has led to major advances in stroke management. Abiding to the Preferred Reporting Items for Systematic Review and Meta–Analyses (PRISMA) guidelines, we have surveyed a total of 177 research papers published between 2010 and 2021 to highlight the current status and challenges faced by computer aided diagnosis (CAD), machine learning (ML) and deep learning (DL) based techniques for CT and MRI as prime modalities for stroke detection and lesion region segmentation. This work concludes by showcasing the current requirement of this domain, the preferred modality, and prospective research areas. Full article

(This article belongs to the Special Issue Innovations in Biomedical Imaging)

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