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Advanced Image Analysis and Processing for Biomedical Applications

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A special issue of Applied Sciences (ISSN 2076-3417). This special issue belongs to the section "Applied Biosciences and Bioengineering".

Deadline for manuscript submissions: closed (31 March 2022) | Viewed by 37724

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Dr. Francesco Isgrò

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Department of Electrical Engineering and Information Technology, University of Naples Federico II, 80125 Naples, Italy
Interests: machine learning; deep learning; computer vision; XAI; BCI
Special Issues, Collections and Topics in MDPI journals

Dr. Andrea Apicella

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Dipartimento di Salute Mentale e Fisica e Medicina Preventiva, Università della Campania L. Vanvitelli, Largo Madonna delle Grazie 1, 80138 Napoli, Italy
Interests: computer vision; pattern recognition and machine learning; biomedical image analysis; neural networks

Dr. Domenico Tegolo

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Dipartimento di Matematica e Informatica, Università di Palermo, via Archirafi 34, 90123 Palermo, Italy
Interests: medical imaging; computer vision; retinal image; neuronal networks; Random graphs.

Dr. Cesare Valenti

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Dipartimento di Matematica e Informatica, Università di Palermo, via Archirafi 34, 90123 Palermo, Italy
Interests: medical imaging; discrete tomography and geometry; evolutionary computation; computer vision.

Dr. Roberto Prevete

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Dipartimento di Ingegneria Elettrica e delle Tecnologie dell’Informazione, Università degli Studi di Napoli Federico II, Via Claudio 21, 80125 Napoli, Italy
Interests: neural networks; artificial intelligence; pattern recognition and machine learning; computational neuroscience; biomedical image analysis;
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Special Issue Information

Dear Colleagues,

Medical images contain plenty of information about the anatomical structures that are important for a valid diagnosis, and then can be helpful to doctors for determining the most adequate treatment. The analysis and processing of biomedical images is an interdisciplinary and dynamic area of specialisation, covering biology, physics, medicine, engineering, and computer science. The main objective is the application of image processing and analysis techniques to biological or medical problems. For instance, in biomedicine, it is possible to use computational methods of image processing and analysis to model and visualize human organs from medical images. These methods can have different objectives, such as enhanced visualization, 3D reconstruction, segmentation, motion and deformation analysis, and registration.

This Special Issue on Advanced Image Analysis and Processing for Biomedical Applications aims to provide an assorted and complementary collection of contributions showing new advancements and applications of advanced imaging analysis and processing in the biomedical imaging area. The ultimate objective is to promote research and advancement in the field, by publishing high-quality research articles and reviews in this rapidly growing interdisciplinary field.

Topics of interest include, but are not limited to, the following:

Image enhancement, segmentation, registration, and fusion for biomedical applications;
Image acquisition and processing for biomedical applications;
Reconstruction, motion, and deformation analysis for biomedical applications;
Computer-aided diagnosis, surgery, therapy, treatment, and telemedicine systems;
Application of machine learning and artificial intelligence in medicine;
Telemedicine systems for elderly care;
Mobile applications and low-cost systems;
Sparse representation and dictionary learning-based methods for medical image processing and understanding;
Deep learning for biomedical image analysis;
Natural language processing for biomedical image analysis;
Bio-inspired contour detection;
Biomedical ultrasonics;
Fluorescence image analysis;
Cardiovascular image analysis;
Super-resolution microscopy;
Retinal image analysis;
Virtual surgery.

Dr. Francesco Isgrò
Dr. Andrea Apicella
Dr. Domenico Tegolo
Dr. Cesare Valenti
Dr. Roberto Prevete
Guest Editors

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Keywords

Biomedical image processing
Biomedical image analysis
Machine learning
Computer-assisted diagnosis

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Published Papers (10 papers)

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Research

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13 pages, 2437 KiB

Open AccessArticle

Optimization of Common Iliac Artery Sonography Images via an Indigenous Water Phantom and Taguchi’s Analysis: A Feasibility Study

by Keng-Yi Wu, Chun-Chieh Liang, Chao-Hsun Chuang, Lung-Fa Pan and Lung-Kwang Pan

Appl. Sci. 2022, 12(16), 8197; https://doi.org/10.3390/app12168197 - 16 Aug 2022

Cited by 4 | Viewed by 1467

Abstract

Object: Optimization of common iliac artery sonography images using an indigenous water phantom and Taguchi’s analysis was successfully performed to improve the diagnostic accuracy in routine cardiac examination. Methods: A water phantom with two major compartments was developed, which satisfied Taguchi’s unique criterion of optimization analysis. Two or three levels were assigned to five factors, namely, (A) the probe angle, (B) water depth, (C) sonography preset frame rate, (D) amplitude gain, and (E) imaging compression ratio. The resulting Taguchi’s L₁₈ orthogonal array contained 18 combinations of 5 factors, ensuring the same confidence level as a realm of 162 (2¹ × 3⁴) combinations. The signal-to-noise ratio (S/N) was defined as the minimal difference between the practical survey and predicted areas of 50 mm² for the sonography imaging scans. The artifact was customized by creating stenosis with a diameter of 8 mm inside a silicon pipe with a diameter of 19 mm. Results: The derived optimal parameters included (A) a zero probe angle, (B) water depth of 6 cm, (C) frame rate of 45 Hz, (D) amplitude gain of 50%, and (E) compress ratio of 50% from 3 independent measurements in each group. Further ANOVA confirmed that the frame rate was a dominant factor, with ss (sum of squared variances) of 56.6%, whereas the error and other terms were suppressed to 20.3% and 11.9%, respectively. The risks of the inappropriate setting of S/N were also discussed to avoid any misinterpretations. Conclusions: The quantified water phantom combined with Taguchi’s approach proved to be instrumental in optimizing the sonography image scan quality in routine cardiac examination. Full article

(This article belongs to the Special Issue Advanced Image Analysis and Processing for Biomedical Applications)

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19 pages, 4678 KiB

Open AccessArticle

COVID-19 Chest X-ray Classification and Severity Assessment Using Convolutional and Transformer Neural Networks

by Tuan Le Dinh, Suk-Hwan Lee, Seong-Geun Kwon and Ki-Ryong Kwon

Appl. Sci. 2022, 12(10), 4861; https://doi.org/10.3390/app12104861 - 11 May 2022

Cited by 22 | Viewed by 7975

Abstract

The coronavirus pandemic started in Wuhan, China in December 2019, and put millions of people in a difficult situation. This fatal virus spread to over 227 countries and the number of infected patients increased to over 400 million cases, causing over 6 million deaths worldwide. Due to the serious consequence of this virus, it is necessary to develop a detection method that can respond quickly to prevent the spreading of COVID-19. Using chest X-ray images to detect COVID-19 is one of the promising techniques; however, with a large number of COVID-19 infected cases every day, the number of radiologists available to diagnose the chest X-ray images is not sufficient. We must have a computer aid system that helps doctors instantly and automatically determine COVID-19 cases. Recently, with the emergence of deep learning methods applied for medical and biomedical uses, using convolutional neural net and transformer applications for chest X-ray images can be a supplement for COVID-19 testing. In this paper, we attempt to classify three types of chest X-ray, which are normal, pneumonia, and COVID-19 using deep learning methods on a customized dataset. We also carry out an experiment on the COVID-19 severity assessment task using a tailored dataset. Five deep learning models were obtained to conduct our experiments: DenseNet121, ResNet50, InceptionNet, Swin Transformer, and Hybrid EfficientNet-DOLG neural networks. The results indicated that chest X-ray and deep learning could be reliable methods for supporting doctors in COVID-19 identification and severity assessment tasks. Full article

(This article belongs to the Special Issue Advanced Image Analysis and Processing for Biomedical Applications)

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11 pages, 4441 KiB

Open AccessArticle

Quality Evaluation Algorithm for Chest Compressions Based on OpenPose Model

by Siqi Zhang, Jie Jin, Chaofang Wang, Wenlong Dong and Bin Fan

Appl. Sci. 2022, 12(10), 4847; https://doi.org/10.3390/app12104847 - 11 May 2022

Cited by 6 | Viewed by 2523

Abstract

Aiming at the problems of the low evaluation efficiency of the existing traditional cardiopulmonary resuscitation (CPR) training mode and the considerable development of machine vision technology, a quality evaluation algorithm for chest compressions (CCs) based on the OpenPose human pose estimation (HPE) model is proposed. Firstly, five evaluation criteria are proposed based on major international CPR guidelines along with our experimental study on elbow straightness. Then, the OpenPose network is applied to obtain the coordinates of the key points of the human skeleton. The algorithm subsequently calculates the geometric angles and displacement of the selected joint key points using the detected coordinates. Finally, it determines whether the compression posture is standard, and it calculates the depth, frequency, position and chest rebound, which are the critical evaluation metrics of CCs. Experimental results show that the average accuracy of network behavior detection reaches 94.85%, and detection speed reaches 25 fps. Full article

(This article belongs to the Special Issue Advanced Image Analysis and Processing for Biomedical Applications)

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11 pages, 3529 KiB

Open AccessArticle

Convolutional Neural Network in the Evaluation of Myocardial Ischemia from CZT SPECT Myocardial Perfusion Imaging: Comparison to Automated Quantification

by Jui-Jen Chen, Ting-Yi Su, Wei-Shiang Chen, Yen-Hsiang Chang and Henry Horng-Shing Lu

Appl. Sci. 2021, 11(2), 514; https://doi.org/10.3390/app11020514 - 7 Jan 2021

Cited by 18 | Viewed by 3037

Abstract

This study analyzes CZT SPECT myocardial perfusion images that are collected at Chang Gung Memorial Hospital, Kaohsiung Medical Center in Kaohsiung. This study focuses on the classification of myocardial perfusion images for coronary heart diseases by convolutional neural network techniques. In these gray scale images, heart blood flow distribution contains the most important features. Therefore, data-driven preprocessing is developed to extract the area of interest. After removing the surrounding noise, the three-dimensional convolutional neural network model is utilized to classify whether the patient has coronary heart diseases or not. The prediction accuracy, sensitivity, and specificity are 87.64%, 81.58%, and 92.16%. The prototype system will greatly reduce the time required for physician image interpretation and write reports. It can assist clinical experts in diagnosing coronary heart diseases accurately in practice. Full article

(This article belongs to the Special Issue Advanced Image Analysis and Processing for Biomedical Applications)

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28 pages, 34010 KiB

Open AccessArticle

Hair Removal Combining Saliency, Shape and Color

by Giuliana Ramella

Appl. Sci. 2021, 11(1), 447; https://doi.org/10.3390/app11010447 - 5 Jan 2021

Cited by 13 | Viewed by 4181

Abstract

In a computer-aided system for skin cancer diagnosis, hair removal is one of the main challenges to face before applying a process of automatic skin lesion segmentation and classification. In this paper, we propose a straightforward method to detect and remove hair from dermoscopic images. Preliminarily, the regions to consider as candidate hair regions and the border/corner components located on the image frame are automatically detected. Then, the hair regions are determined using information regarding the saliency, shape and image colors. Finally, the detected hair regions are restored by a simple inpainting method. The method is evaluated on a publicly available dataset, comprising 340 images in total, extracted from two commonly used public databases, and on an available specific dataset including 13 images already used by other authors for evaluation and comparison purposes. We propose also a method for qualitative and quantitative evaluation of a hair removal method. The results of the evaluation are promising as the detection of the hair regions is accurate, and the performance results are satisfactory in comparison to other existing hair removal methods. Full article

(This article belongs to the Special Issue Advanced Image Analysis and Processing for Biomedical Applications)

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20 pages, 8118 KiB

Open AccessFeature PaperArticle

Exudates as Landmarks Identified through FCM Clustering in Retinal Images

by Hadi Hamad, Tahreer Dwickat, Domenico Tegolo and Cesare Valenti

Appl. Sci. 2021, 11(1), 142; https://doi.org/10.3390/app11010142 - 25 Dec 2020

Cited by 11 | Viewed by 2762

Abstract

The aim of this work was to develop a method for the automatic identification of exudates, using an unsupervised clustering approach. The ability to classify each pixel as belonging to an eventual exudate, as a warning of disease, allows for the tracking of a patient’s status through a noninvasive approach. In the field of diabetic retinopathy detection, we considered four public domain datasets (DIARETDB0/1, IDRID, and e-optha) as benchmarks. In order to refine the final results, a specialist ophthalmologist manually segmented a random selection of DIARETDB0/1 fundus images that presented exudates. An innovative pipeline of morphological procedures and fuzzy C-means clustering was integrated in order to extract exudates with a pixel-wise approach. Our methodology was optimized, and verified and the parameters were fine-tuned in order to define both suitable values and to produce a more accurate segmentation. The method was used on 100 tested images, resulting in averages of sensitivity, specificity, and accuracy equal to 83.3%, 99.2%, and 99.1%, respectively. Full article

(This article belongs to the Special Issue Advanced Image Analysis and Processing for Biomedical Applications)

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18 pages, 2456 KiB

Open AccessArticle

Image Reconstruction in Diffuse Optical Tomography Using Adaptive Moment Gradient Based Optimizers: A Statistical Study

by Nada Chakhim, Mohamed Louzar, Abdellah Lamnii and Mohammed Alaoui

Appl. Sci. 2020, 10(24), 9117; https://doi.org/10.3390/app10249117 - 20 Dec 2020

Cited by 3 | Viewed by 2340

Abstract

Diffuse optical tomography (DOT) is an emerging modality that reconstructs the optical properties in a highly scattering medium from measured boundary data. One way to solve DOT and recover the quantities of interest is by an inverse problem approach, which requires the choice of an optimization algorithm for the iterative approximation of the solution. However, the well-established and proven fact of the no free lunch principle holds in general. This paper aims to compare the behavior of three gradient descent-based optimizers on solving the DOT inverse problem by running randomized simulation and analyzing the generated data in order to shade light on any significant difference—if existing at all—in performance among these optimizers in our specific context of DOT. The major practical problems when selecting or using an optimization algorithm in a production context for a DOT system is to be confident that the algorithm will have a high convergence rate to the true solution, reasonably fast speed and high quality of the reconstructed image in terms of good localization of the inclusions and good agreement with the true image. In this work, we harnessed carefully designed randomized simulations to tackle the practical problem of choosing the right optimizer with the right parameters in the context of practical DOT applications, and derived statistical results concerning rate of convergence, speed, and quality of image reconstruction. The statistical analysis performed on the generated data and the main results for convergence rate, reconstruction speed, and quality between three optimization algorithms are presented in the paper at hand. Full article

(This article belongs to the Special Issue Advanced Image Analysis and Processing for Biomedical Applications)

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18 pages, 1071 KiB

Open AccessArticle

Radiomics-Based Prediction of Overall Survival in Lung Cancer Using Different Volumes-Of-Interest

by Natascha Claudia D’Amico, Rosa Sicilia, Ermanno Cordelli, Lorenzo Tronchin, Carlo Greco, Michele Fiore, Alessia Carnevale, Giulio Iannello, Sara Ramella and Paolo Soda

Appl. Sci. 2020, 10(18), 6425; https://doi.org/10.3390/app10186425 - 15 Sep 2020

Cited by 15 | Viewed by 3379

Abstract

Lung cancer accounts for the largest amount of deaths worldwide with respect to the other oncological pathologies. To guarantee the most effective cure to patients for such aggressive tumours, radiomics is increasing as a novel and promising research field that aims at extracting knowledge from data in terms of quantitative measures that are computed from diagnostic images, with prognostic and predictive ends. This knowledge could be used to optimize current treatments and to maximize their efficacy. To this end, we hereby study the use of such quantitative biomarkers computed from CT images of patients affected by Non-Small Cell Lung Cancer to predict Overall Survival. The main contributions of this work are two: first, we consider different volumes of interest for the same patient to find out whether the volume surrounding the visible lesions can provide useful information; second, we introduce 3D Local Binary Patterns, which are texture measures scarcely explored in radiomics. As further validation, we show that the proposed signature outperforms not only the features automatically computed by a deep learning-based approach, but also another signature at the state-of-the-art using other handcrafted features. Full article

(This article belongs to the Special Issue Advanced Image Analysis and Processing for Biomedical Applications)

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22 pages, 20545 KiB

Open AccessArticle

ACDC: Automated Cell Detection and Counting for Time-Lapse Fluorescence Microscopy

by Leonardo Rundo, Andrea Tangherloni, Darren R. Tyson, Riccardo Betta, Carmelo Militello, Simone Spolaor, Marco S. Nobile, Daniela Besozzi, Alexander L. R. Lubbock, Vito Quaranta, Giancarlo Mauri, Carlos F. Lopez and Paolo Cazzaniga

Appl. Sci. 2020, 10(18), 6187; https://doi.org/10.3390/app10186187 - 6 Sep 2020

Cited by 8 | Viewed by 6060

Abstract

Advances in microscopy imaging technologies have enabled the visualization of live-cell dynamic processes using time-lapse microscopy imaging. However, modern methods exhibit several limitations related to the training phases and to time constraints, hindering their application in the laboratory practice. In this work, we present a novel method, named Automated Cell Detection and Counting (ACDC), designed for activity detection of fluorescent labeled cell nuclei in time-lapse microscopy. ACDC overcomes the limitations of the literature methods, by first applying bilateral filtering on the original image to smooth the input cell images while preserving edge sharpness, and then by exploiting the watershed transform and morphological filtering. Moreover, ACDC represents a feasible solution for the laboratory practice, as it can leverage multi-core architectures in computer clusters to efficiently handle large-scale imaging datasets. Indeed, our Parent-Workers implementation of ACDC allows to obtain up to a

3.7 \times

speed-up compared to the sequential counterpart. ACDC was tested on two distinct cell imaging datasets to assess its accuracy and effectiveness on images with different characteristics. We achieved an accurate cell-count and nuclei segmentation without relying on large-scale annotated datasets, a result confirmed by the average Dice Similarity Coefficients of

76.84

and

88.64

and the Pearson coefficients of

0.99

and

0.96

, calculated against the manual cell counting, on the two tested datasets. Full article

(This article belongs to the Special Issue Advanced Image Analysis and Processing for Biomedical Applications)

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Review

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15 pages, 608 KiB

Open AccessReview

Head–Neck Cancer Delineation

by Enrico Antonio Lo Faso, Orazio Gambino and Roberto Pirrone

Appl. Sci. 2021, 11(6), 2721; https://doi.org/10.3390/app11062721 - 18 Mar 2021

Cited by 4 | Viewed by 2524

Abstract

Head–Neck Cancer (HNC) has a relevant impact on the oncology patient population and for this reason, the present review is dedicated to this type of neoplastic disease. In particular, a collection of methods aimed at tumor delineation is presented, because this is a fundamental task to perform efficient radiotherapy. Such a segmentation task is often performed on uni-modal data (usually Positron Emission Tomography (PET)) even though multi-modal images are preferred (PET-Computerized Tomography (CT)/PET-Magnetic Resonance (MR)). Datasets can be private or freely provided by online repositories on the web. The adopted techniques can belong to the well-known image processing/computer-vision algorithms or the newest deep learning/artificial intelligence approaches. All these aspects are analyzed in the present review and comparison among various approaches is performed. From the present review, the authors draw the conclusion that despite the encouraging results of computerized approaches, their performance is far from handmade tumor delineation result. Full article

(This article belongs to the Special Issue Advanced Image Analysis and Processing for Biomedical Applications)

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