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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: 31 May 2025 | Viewed by 7173

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Special Issue Editors

Dr. Patrizia Vizza

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Guest Editor

Department of Medical and Surgical Sciences, Magna Graecia University, 88100 Catanzaro, Italy
Interests: biomedical signal processing; medical image analysis; clinical decision support systems; medical/clinical informatics
Special Issues, Collections and Topics in MDPI journals

Dr. Giuseppe Tradigo

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Guest Editor

eCampus University, Via Isimbardi 10, 22060 Novedrate, CO, Italy
Interests: bioinformatics; computational proteomics and genomics; information extraction from health data
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

The analysis of bioimages and their clinical-related extracted features are very useful in supporting physicians in the early detection, investigation, diagnosis, classification, management, and treatment of many pathological conditions and diseases.

Recent advances in sensor technology have allowed the development of many interesting solutions to extract and process clinical information and data useful to discover significant hidden patterns, aiming to define predictive algorithms, methodologies, and tools for the prediction of anomalous behavior, for the study and the classification of diseases.

Bioimaging sensors can furnish quantitative measurements to physicians that they can use in a decision support system for scientific hypotheses and medical diagnoses, treatment, and follow-up.

This Special Issue aims to present and report recent advances and trends concerning novel solutions and technologies in bioimaging sensors and processing, with applications in clinical practice.

Both contributions about the results of recent developments and applications and comprehensive review articles are welcome for submission.

Potential topics include but are not limited to:

Bioimage processing;
Bioimage sensors;
Image segmentation;
Clinical decision support systems;
Early disease detection;
Biomedical computer-aided applications.

Dr. Patrizia Vizza
Dr. Giuseppe Tradigo
Guest Editors

Manuscript Submission Information

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Published Papers (5 papers)

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Research

18 pages, 2256 KiB

Open AccessArticle

Image-Based Detection and Classification of Malaria Parasites and Leukocytes with Quality Assessment of Romanowsky-Stained Blood Smears

by Jhonathan Sora-Cardenas, Wendy M. Fong-Amaris, Cesar A. Salazar-Centeno, Alejandro Castañeda, Oscar D. Martínez-Bernal, Daniel R. Suárez and Carol Martínez

Sensors 2025, 25(2), 390; https://doi.org/10.3390/s25020390 - 10 Jan 2025

Viewed by 672

Abstract

Malaria remains a global health concern, with 249 million cases and 608,000 deaths being reported by the WHO in 2022. Traditional diagnostic methods often struggle with inconsistent stain quality, lighting variations, and limited resources in endemic regions, making manual detection time-intensive and error-prone. This study introduces an automated system for analyzing Romanowsky-stained thick blood smears, focusing on image quality evaluation, leukocyte detection, and malaria parasite classification. Using a dataset of 1000 clinically diagnosed images, we applied feature extraction techniques, including histogram bins and texture analysis with the gray level co-occurrence matrix (GLCM), alongside support vector machines (SVMs), for image quality assessment. Leukocyte detection employed optimal thresholding segmentation utility (OTSU) thresholding, binary masking, and erosion, followed by the connected components algorithm. Parasite detection used high-intensity region selection and adaptive bounding boxes, followed by a custom convolutional neural network (CNN) for candidate identification. A second CNN classified parasites into trophozoites, schizonts, and gametocytes. The system achieved an F1-score of 95% for image quality evaluation, 88.92% for leukocyte detection, and 82.10% for parasite detection. The F1-score—a metric balancing precision (correctly identified positives) and recall (correctly detected instances out of actual positives)—is especially valuable for assessing models on imbalanced datasets. In parasite stage classification, CNN achieved F1-scores of 85% for trophozoites, 88% for schizonts, and 83% for gametocytes. This study introduces a robust and scalable automated system that addresses critical challenges in malaria diagnosis by integrating advanced image quality assessment and deep learning techniques for parasite detection and classification. This system’s adaptability to low-resource settings underscores its potential to improve malaria diagnostics globally. Full article

(This article belongs to the Special Issue Recent Advances in Biomedical Imaging Sensors and Processing)

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20 pages, 6249 KiB

Open AccessArticle

Enhanced Detection of Leishmania Parasites in Microscopic Images Using Machine Learning Models

by Michael Contreras-Ramírez, Jhonathan Sora-Cardenas, Claudia Colorado-Salamanca, Clemencia Ovalle-Bracho and Daniel R. Suárez

Sensors 2024, 24(24), 8180; https://doi.org/10.3390/s24248180 - 21 Dec 2024

Cited by 1 | Viewed by 835

Abstract

Cutaneous leishmaniasis is a parasitic disease that poses significant diagnostic challenges due to the variability of results and reliance on operator expertise. This study addresses the development of a system based on machine learning algorithms to detect Leishmania spp. parasite in direct smear microscopy images, contributing to the diagnosis of cutaneous leishmaniasis. Starting with acquiring and labeling 500 images, an experimental design was implemented, including preprocessing and segmentation techniques such as Otsu, local thresholding, and Iterative Global Minimum Search (IGMS) to improve parasite detection. The phenotypic features of the parasites were extracted, focusing on morphology, texture, and color. Machine learning models (ANN, SVM, and RF) optimized through Grid Search were applied for classification. The model with the best results was a Support Vector Machine (SVM), achieving a sensitivity of 91.87% and a specificity of 89.21% at the crop level. Compared with previous studies, these results highlight the relevance and consistency of the methodology used, supporting the initial hypothesis. This suggests that machine learning techniques offer a promising path toward improving the diagnosis of cutaneous leishmaniasis. Full article

(This article belongs to the Special Issue Recent Advances in Biomedical Imaging Sensors and Processing)

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10 pages, 4721 KiB

Open AccessArticle

Enhanced Timing Performance of Dual-Ended PET Detectors for Brain Imaging Using Dual-Finishing Crystal Approach

by Guen Bae Ko, Dongjin Kwak and Jae Sung Lee

Sensors 2024, 24(20), 6520; https://doi.org/10.3390/s24206520 - 10 Oct 2024

Viewed by 760

Abstract

This study presents a novel approach to enhancing the timing performance of dual-ended positron emission tomography (PET) detectors for brain imaging by employing a dual-finishing crystal method. The proposed method integrates both polished and unpolished surfaces within the scintillation crystal block to optimize time-of-flight (TOF) and depth-of-interaction (DOI) resolutions. A dual-finishing detector was constructed using an 8 × 8 LGSO array with a 2 mm pitch, and its performance was compared against fully polished and unpolished crystal blocks. The results indicate that the dual-finishing method significantly improves the timing resolution while maintaining good energy and DOI resolutions. Specifically, the timing resolution achieved with the dual-finishing block was superior, measuring 192.0 ± 12.8 ps, compared to 206.3 ± 9.4 ps and 234.8 ± 17.9 ps for polished and unpolished blocks, respectively. This improvement in timing is crucial for high-performance PET systems, particularly in brain imaging applications where high sensitivity and spatial resolution are paramount. Full article

(This article belongs to the Special Issue Recent Advances in Biomedical Imaging Sensors and Processing)

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13 pages, 1686 KiB

Open AccessArticle

Characterization of Heart Diseases per Single Lead Using ECG Images and CNN-2D

by Lerina Aversano, Mario Luca Bernardi, Marta Cimitile, Debora Montano and Riccardo Pecori

Sensors 2024, 24(11), 3485; https://doi.org/10.3390/s24113485 - 28 May 2024

Viewed by 1333

Abstract

Cardiopathy has become one of the predominant global causes of death. The timely identification of different types of heart diseases significantly diminishes mortality risk and enhances the efficacy of treatment. However, fast and efficient recognition necessitates continuous monitoring, encompassing not only specific clinical conditions but also diverse lifestyles. Consequently, an increasing number of studies are striving to automate and progress in the identification of different cardiopathies. Notably, the assessment of electrocardiograms (ECGs) is crucial, given that it serves as the initial diagnostic test for patients, proving to be both the simplest and the most cost-effective tool. This research employs a customized architecture of Convolutional Neural Network (CNN) to forecast heart diseases by analyzing the images of both three bands of electrodes and of each single electrode signal of the ECG derived from four distinct patient categories, representing three heart-related conditions as well as a spectrum of healthy controls. The analyses are conducted on a real dataset, providing noteworthy performance (recall greater than 80% for the majority of the considered diseases and sometimes even equal to 100%) as well as a certain degree of interpretability thanks to the understanding of the importance a band of electrodes or even a single ECG electrode can have in detecting a specific heart-related pathology. Full article

(This article belongs to the Special Issue Recent Advances in Biomedical Imaging Sensors and Processing)

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17 pages, 4430 KiB

Open AccessArticle

Hybrid Approach to Colony-Forming Unit Counting Problem Using Multi-Loss U-Net Reformulation

by Vilen Jumutc, Artjoms Suponenkovs, Andrey Bondarenko, Dmitrijs Bļizņuks and Alexey Lihachev

Sensors 2023, 23(19), 8337; https://doi.org/10.3390/s23198337 - 9 Oct 2023

Cited by 2 | Viewed by 2665

Abstract

Colony-Forming Unit (CFU) counting is a complex problem without a universal solution in biomedical and food safety domains. A multitude of sophisticated heuristics and segmentation-driven approaches have been proposed by researchers. However, U-Net remains the most frequently cited and used deep learning method in these domains. The latter approach provides a segmentation output map and requires an additional counting procedure to calculate unique segmented regions and detect microbial colonies. However, due to pixel-based targets, it tends to generate irrelevant artifacts or errant pixels, leading to inaccurate and mixed post-processing results. In response to these challenges, this paper proposes a novel hybrid counting approach, incorporating a multi-loss U-Net reformulation and a post-processing Petri dish localization algorithm. Firstly, a unique innovation lies in the multi-loss U-Net reformulation. An additional loss term is introduced in the bottleneck U-Net layer, focusing on the delivery of an auxiliary signal that indicates where to look for distinct CFUs. Secondly, the novel localization algorithm automatically incorporates an agar plate and its bezel into the CFU counting techniques. Finally, the proposition is further enhanced by the integration of a fully automated solution, which comprises a specially designed uniform Petri dish illumination system and a counting web application. The latter application directly receives images from the camera, processes them, and sends the segmentation results to the user. This feature provides an opportunity to correct the CFU counts, offering a feedback loop that contributes to the continued development of the deep learning model. Through extensive experimentation, the authors of this paper have found that all probed multi-loss U-Net architectures incorporated into the proposed hybrid approach consistently outperformed their single-loss counterparts, as well as other comparable models such as self-normalized density maps and YOLOv6, by at least 1% to 3% in mean absolute and symmetric mean absolute percentage errors. Further significant improvements were also reported through the means of the novel localization algorithm. This reaffirms the effectiveness of the proposed hybrid solution in addressing contemporary challenges of precise in vitro CFU counting. Full article

(This article belongs to the Special Issue Recent Advances in Biomedical Imaging Sensors and Processing)

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