Institute of Telecommunications

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Publications

LOSSY COMPRESSION OF BIOMEDICAL IMAGES FOR COMPUTER VISION ANALYSIS

Publication . Paulo, Edgar da Silva; Faria, Sérgio Manuel Maciel de; Távora, Luís Miguel de Oliveira Pegado de Noronha e; Thomaz, Lucas Arrabal

The exponential increase in medical and biomedical data acquisition is compelled by technological advances, namely in the imaging field. However, this exponential growth brings with it challenges in terms of processing capacity, transmission, and data storage. In response to this growing demand, increasingly efficient solutions have emerged, especially through computer vision for automatic image analysis and compression algorithms. This dissertation aims, on the one hand, to evaluate the performance of computer vision systems on previously compressed biomedical images. On the other hand, it increases the useful range of image variations, almost lossless and lossy, decreasing the impact of the change added by this method on the performance of computer vision algorithms in biomedical image analysis. In this sense, YOLO and Detectron2 are employed to evaluate the impact of coding distortion on their ability to detect mitochondria in electron microscopy images. The results of this study reveal that although the distortion introduced by compression affects their detection performance, it is negligible at lower compression ratios. Furthermore, two proposals are presented to improve the useful compression ratio, keeping the images characteristics that allow to perform the automatic detection of mitochondria. On the one hand, it is demonstrated that the proposed training methodology, which incorporates compressed versions of the original data during training, mitigates the impact of distortion on the performance of computer vision algorithms; on the other hand, allocating higher quality levels to regions of interest, compared to background elements, helps to sustain high performance at compression rates where computer vision algorithms typically start to lose effectiveness. These approaches allow the extension of the compression range with little impact on detection performance, thus contributing to the improvement of data processing, storage, and transmission in biomedical applications.

2024-06-14Master thesis

Open access

Digital Forensic Artifacts of FIDO2 Passkeys in Windows 11

Publication . Domingues, Patricio; Frade, Miguel; Negrão, Miguel

FIDO2’s passkey aims to provide a passwordless authentication solution. It relies on two main protocols – WebAuthn and CTAP2 – for authentication in computer systems, relieving users from the burden of using and managing passwords. FIDO2’s passkey leverages asymmetric cryptography to create a unique public/private key pair for website authentication. While the public key is kept at the website/application, the private key is created and stored on the authentication device designated as the authenticator. The authenticator can be the computer itself – same-device signing –, or another device – cross-device signing –, such as an Android smartphone that connects to the computer through a short-range communication method (NFC, Bluetooth). Authentication is performed by the user unlocking the authenticator device. In this paper, we report on the digital forensic artifacts left on Windows 11 systems by registering and using passkeys to authenticate on websites. We show that digital artifacts are created in Windows Registry and Windows Event Log. These artifacts enable the precise dating and timing of passkey registration, as well as the usage and identification of the websites on which they have been activated and utilized. We also identify digital artifacts created when Android smartphones are registered and used as authenticators in a Windows system. This can prove useful in detecting the existence of smartphones linked to a given individual.

2024-07-30Conference object

Open access

Machine Learning in MRI Brain Imaging: A Review of Methods, Challenges, and Future Directions

Publication . Ottoni, Martyna; Kasperczuk, Anna; Tavora, Luis M. N.

In recent years, machine learning (ML) has been increasingly used in many fields, including medicine. Magnetic resonance imaging (MRI) is a non-invasive and effective diagnostic technique; however, manual image analysis is time-consuming and prone to human variability. In response, ML models have been developed to support MRI analysis, particularly in segmentation and classification tasks. This work presents an updated narrative review of ML applications in brain MRI, with a focus on tumor classification and segmentation. A literature search was conducted in PubMed and Scopus databases and Mendeley Catalog (MC)—a publicly accessible bibliographic catalog linked to Elsevier’s Scopus indexing system—covering the period from January 2020 to April 2025. The included studies focused on patients with primary or secondary brain neoplasms and applied machine learning techniques to MRI data for classification or segmentation purposes. Only original research articles written in English and reporting model validation were considered. Studies using animal models, non-imaging data, lacking proper validation, or without accessible full texts (e.g., abstract-only records or publications unavailable through institutional access) were excluded. In total, 108 studies met all inclusion criteria and were analyzed qualitatively. In general, models based on convolutional neural networks (CNNs) were found to dominate current research due to their ability to extract spatial features directly from imaging data. Reported classification accuracies ranged from 95% to 99%, while Dice coefficients for segmentation tasks varied between 0.83 and 0.94. Hybrid architectures (e.g., CNN-SVM, CNN-LSTM) achieved strong results in both classification and segmentation tasks, with accuracies above 95% and Dice scores around 0.90. Transformer-based models, such as the Swin Transformer, reached the highest performance, up to 99.9%. Despite high reported accuracy, challenges remain regarding overfitting, generalization to real-world clinical data, and lack of standardized evaluation protocols. Transfer learning and data augmentation were frequently applied to mitigate limited data availability, while radiomics-based models introduced new avenues for personalized diagnostics. ML has demonstrated substantial potential in enhancing brain MRI analysis and supporting clinical decision-making. Nevertheless, further progress requires rigorous clinical validation, methodological standardization, and comparative benchmarking to bridge the gap between research settings and practical deployment.

2025-10-24Journal article

Open access