Search Results (34)

Background/Objectives: Optical coherence tomography angiography (OCTA) enables noninvasive quantitative assessment of the retinal microvasculature and is widely used in diabetic retinopathy (DR). However, OCTA-derived metrics are highly dependent on post-processing techniques, particularly vessel binarization. This study aimed to compare local and global binarization methods applied after Frangi filtering for vessel enhancement in parafoveal vessel density analysis. Methods: This cross-sectional study included 69 participants: 17 healthy controls and 52 diabetic patients, classified as the following: no DR (n = 14), non-proliferative DR (NPDR, n = 18), or proliferative DR (PDR, n = 20). All subjects underwent comprehensive ophthalmological examination and OCTA imaging of the superficial capillary plexus using a Topcon OCTA system. Images were processed using a custom MATLAB protocol. Following Frangi filtering, five binarization methods were applied: three local (Phansalkar, local Otsu, adaptive mean) and two global (global mean and global Otsu). Parafoveal vessel density was quantified within the four inner quadrants of the ETDRS grid. Results: Statistically significant differences in vessel density were consistently observed between PDR group and both the control and no DR groups across all local binarization methods. Among global methods, only global Otsu thresholding detected a significant difference between PDR and control. The most robust differences were predominantly identified in the nasal and inferior quadrants. Conclusions: Local adaptive binarization methods demonstrated superior sensitivity and structural preservation for parafoveal vessel density analysis in DR. Global methods showed limited discriminative capability. These findings support the preferential use of local adaptive techniques for reliable OCTA-based vascular assessment in diabetic retinopathy. Full article

Accurate segmentation of coronary artery calcifications (CAC) from cardiac CT is challenged by class imbalance, small lesion size, and anatomical ambiguity. We present an anatomically guided, cascaded framework that couples heart and vessel priors with a heterogeneous U-Net ensemble for robust, vessel-aware CAC segmentation. First, a ResU-Net trained on MM-WHS isolates the heart region of interest (ROI). Second, a ResU-Net trained on ASOCA—using Frangi vesselness enhancement—segments the coronary arteries, yielding vessel masks that constrain downstream lesion detection. Third, calcifications are segmented within the vessel-constrained ROI using an ensemble of U-Net variants (baseline U-Net, Residual U-Net, Attention U-Net, UNet++). At inference, a rank-based selective fusion strategy prioritizes predictions with strong morphological consistency and vessel conformity, suppressing false positives. On the Stanford COCA gated dataset, the proposed ensemble outperforms individual models (Dice 84.25%, sensitivity 87.10%, specificity 98.00%), with ablations demonstrating additional gains when vessel priors are integrated into selective fusion (Dice 85.50%, sensitivity 88.53%). Results confirm that combining dataset-specific anatomical priors with selective ensembling improves boundary sharpness, small-lesion detectability, and anatomical plausibility, supporting reliable CAC segmentation in clinical imaging workflows. Full article

Background: Delirium is a common, underdiagnosed neuropsychiatric syndrome in older adults, associated with high mortality and functional decline. Given its multifactorial nature and overlap with frailty, radiological markers may improve risk stratification in the emergency department (ED). Methods: We conducted a retrospective study on a small sample of 30 patients diagnosed with delirium in the emergency department who had recently undergone brain, thoracic, or abdominal CT scans for unrelated clinical indications. Using post-processing software, we analyzed radiological markers, including coronary artery calcifications (to estimate vascular age), cerebral atrophy (via the Global Cortical Atrophy scale), and cachexia (based on abdominal fat and psoas muscle volumetry). Results: Five domains were identified as significant predictors of 12-month mortality in univariate Cox regression: vascular age, delirium etiology, cerebral atrophy, delirium subtype (hyperactive vs. hypoactive), and cachexia. Based on these domains, we developed an exploratory 10-point delirium score. This score demonstrated acceptable diagnostic accuracy for mortality prediction (sensitivity 0.93, specificity 0.73, positive predictive value 0.77, negative predictive value 0.91) in this limited cohort. Conclusions: While preliminary and based on a small, retrospective sample of 30 patients, this multidimensional approach integrating clinical and radiological data may help improve risk stratification in elderly patients with delirium. Radiological phenotyping, particularly in terms of vascular aging and sarcopenia/cachexia, offers objective insights into patient frailty and could inform more personalized treatment pathways from the ED to safe discharge home, pending further validation. Full article

Due to the inherent limitations in remote sensing image quality, seasonal variations, and radiometric inconsistencies, river extraction based on remote sensing image classification often results in omissions. These challenges are particularly pronounced in the detection of narrow and complex river networks, where fine river features are frequently underrepresented, leading to fragmented and discontinuous water body extraction. To address these issues and enhance both the completeness and accuracy of fine river identification, this study proposes an advanced fine river extraction and optimization method. Firstly, a linear river feature enhancement algorithm for preliminary optimization is introduced, which combines Frangi filtering with an improved GA-OTSU segmentation technique. By thoroughly analyzing the global features of high-resolution remote sensing images, Frangi filtering is employed to enhance the river linear characteristics. Subsequently, the improved GA-OTSU thresholding algorithm is applied for feature segmentation, yielding the initial results. In the next stage, to preserve the original river topology and ensure stripe continuity, a river skeleton refinement algorithm is utilized to retain critical skeletal information about the river networks. Following this, river endpoints are identified using a connectivity domain labeling algorithm, and the bounding rectangles of potential disconnected regions are delineated. To address discontinuities, river endpoints are shifted and reconnected based on structural similarity index (SSIM) metrics, effectively bridging gaps in the river network. Finally, nonlinear water optimization combined K-means clustering segmentation, topology and spectral inspection, and small-area removal are designed to supplement some missed water bodies and remove some non-water bodies. Experimental results demonstrate that the proposed method significantly improves the regularization and completeness of river extraction, particularly in cases of fine, narrow, and discontinuous river features. The approach ensures more reliable and consistent river delineation, making the extracted results more robust and applicable for practical hydrological and environmental analyses. Full article

Objective (1): To develop and validate a machine learning (ML) model using radiomic features (RFs) extracted from [18F]FDG PET-CT to predict abdominal aortic aneurysm (AAA) growth rate. Methods (2): This retrospective study included 98 internal and 55 external AAA patients undergoing [18F]FDG PET-CT. RFs were extracted from manual segmentations of AAAs using PyRadiomics. Recursive feature elimination (RFE) reduced features for model optimisation. A multi-layer perceptron (MLP) was developed for AAA growth prediction and compared against Random Forest (RF), XGBoost, and Support Vector Machine (SVM). Accuracy was evaluated via cross-validation, with uncertainty quantified using dropout (MLP), standard deviation (RF), and 95% prediction intervals (XGBoost). External validation used independent data from two centres. Ground truth growth rates were calculated from serial ultrasound (US) measurements or CT volumes. Results (3): From 93 initial RFs, 29 remained after RFE. The MLP model achieved an MAE ± SEM of 1.35 ± 3.2e−4 mm/year with the full feature set and 1.35 ± 2.5e−4 mm/year with RFE. External validation yielded 1.8 ± 8.9e−8 mm/year. RF, XGBoost, and SVM models produced comparable accuracies internally (1.4–1.5 mm/year) but showed higher errors during external validation (1.9–1.97 mm/year). The MLP model demonstrated reduced uncertainty with the full feature set across all datasets. Conclusions (4): An MLP model leveraging [18F]FDG PET-CT radiomics accurately predicted AAA growth rates and generalised well to external data. In the future, more sophisticated stratification could guide individualised patient care, facilitating risk-tailored management of AAAs. Full article

Background/Objectives: Accurate detection and classification of nodules in medical images, particularly rheumatoid nodules, are critical due to the varying nature of these nodules, where their specific type is often unknown before analysis. This study addresses the challenges of multi-class prediction in nodule detection, with a specific focus on rheumatoid nodules, by employing a comprehensive approach to feature extraction and classification. We utilized a diverse dataset of nodules, including rheumatoid nodules sourced from the DermNet dataset and local rheumatologists. Method: This study integrates 62 features, combining traditional image characteristics with advanced graph-based features derived from a superpixel graph constructed through Delaunay triangulation. The key steps include image preprocessing with anisotropic diffusion and Retinex enhancement, superpixel segmentation using SLIC, and graph-based feature extraction. Texture analysis was performed using Gray-Level Co-occurrence Matrix (GLCM) metrics, while shape analysis was conducted with Fourier descriptors. Vascular pattern recognition, crucial for identifying rheumatoid nodules, was enhanced using the Frangi filter. A Hybrid CNN–Transformer model was employed for feature fusion, and feature selection and hyperparameter tuning were optimized using Gray Wolf Optimization (GWO) and Particle Swarm Optimization (PSO). Feature importance was assessed using SHAP values. Results: The proposed methodology achieved an accuracy of 85%, with a precision of 0.85, a recall of 0.89, and an F1 measure of 0.87, demonstrating the effectiveness of the approach in detecting and classifying rheumatoid nodules in both binary and multi-class classification scenarios. Conclusions: This study presents a robust tool for the detection and classification of nodules, particularly rheumatoid nodules, in medical imaging, offering significant potential for improving diagnostic accuracy and aiding in the early identification of rheumatoid conditions. Full article

This article presents a wireless in situ sensor designed to continuously monitor profiles of parameters in porous media, such as soil moisture, salinity, and temperature. A review of existing in situ soil sensors reveals that it is the only device capable of measuring the complex permittivity of the medium, allowing for conversions into moisture and salinity that are independent of the instrument. Flow perturbation and invasiveness have also been minimized to maintain good representativeness. Plans include autonomous networks of such sensors, facilitated by the use of the recent radio mode LoRaWAN and cost optimizations for series production. Costs were reduced through electronic simplification and integration, and the use of low-cost modular sensing parts in soil, while still maintaining high measurement quality. A complete set of sensor data recorded during a three-month trial is also presented and interpreted. Full article

How to obtain internal cavity features and perform image matching is a great challenge for laparoscopic 3D reconstruction. This paper proposes a method for detecting and associating vascular features based on dual-branch weighted fusion vascular structure enhancement. Our proposed method is divided into three stages, including analyzing various types of minimally invasive surgery (MIS) images and designing a universal preprocessing framework to make our method generalized. We propose a Gaussian weighted fusion vascular structure enhancement algorithm using the dual-branch Frangi measure and MFAT (multiscale fractional anisotropic tensor) to address the structural measurement differences and uneven responses between venous vessels and microvessels, providing effective structural information for vascular feature extraction. We extract vascular features through dual-circle detection based on branch point characteristics, and introduce NMS (non-maximum suppression) to reduce feature point redundancy. We also calculate the ZSSD (zero sum of squared differences) and perform feature matching on the neighboring blocks of feature points extracted from the front and back frames. The experimental results show that the proposed method has an average accuracy and repeatability score of 0.7149 and 0.5612 in the Vivo data set, respectively. By evaluating the quantity, repeatability, and accuracy of feature detection, our method has more advantages and robustness than the existing methods. Full article

The Frangi neuron proposed in this work is a complex element that allows high-level Hessian-based image processing. Its adaptive parameters (weights) can be trained using a minimum number of training data. In our experiment, we showed that just one image is enough to optimize the values of the weights. An intuitive application of the Frangi neuron is to use it in image segmentation process. In order to test the performance of the Frangi neuron, we used diverse medical datasets on which second-order structures are visualized. The Frangi network presented in this paper trained on a single image proved to be significantly more effective than the U-net trained on the same dataset. For the datasets tested, the network performed better as measured by area under the curve receiver operating characteristic (ROC AUC) than U-net and the Frangi algorithm. However, the Frangi network performed several times faster than the non-GPU implementation of Frangi. There is nothing to prevent the Frangi neuron from being used as part of any other network as a component to process two-dimensional images, for example, to detect certain second-order features in them. Full article

We propose a deep learning-based reduced order modelling approach for micro- electromechanical systems. The method allows treating parametrised, fully coupled electromechanical problems in a non-intrusive way and provides solutions across the whole device domain almost in real time, making it suitable for design optimisation and control purposes. The proposed technique specifically addresses the steady-state response, thus strongly reducing the computational burden associated with the neural network training stage and generating deep learning models with fewer parameters than similar architectures considering generic time-dependent problems. The approach is validated on a disk resonating gyroscope exhibiting auto-parametric resonance. Full article

Micro-electro-mechanical-systems are complex structures, often involving nonlinearites of geometric and multiphysics nature, that are used as sensors and actuators in countless applications. Starting from full-order representations, we apply deep learning techniques to generate accurate, efficient, and real-time reduced order models to be used for the simulation and optimization of higher-level complex systems. We extensively test the reliability of the proposed procedures on micromirrors, arches, and gyroscopes, as well as displaying intricate dynamical evolutions such as internal resonances. In particular, we discuss the accuracy of the deep learning technique and its ability to replicate and converge to the invariant manifolds predicted using the recently developed direct parametrization approach that allows the extraction of the nonlinear normal modes of large finite element models. Finally, by addressing an electromechanical gyroscope, we show that the non-intrusive deep learning approach generalizes easily to complex multiphysics problems. Full article

The measurement accuracy of Brillouin scattering spectra is crucial for ocean remote sensing by Brillouin scattering lidar. Due to the limited resolution of ICCD cameras, the traditional processing methods remain at the pixel or partial sub-pixel level, which cannot meet the requirements of high-performance lidar. In this paper, to extract the frequency shift with high precision from stimulated Brillouin scattering (SBS) lidar, a novel spectral processing method with sub-pixel recognition accuracy is proposed based on the Hessian matrix and Steger algorithm combined with the least square fitting method. Firstly, the Hessian matrix and Frangi filter are used for signal denoising. Then, the center points of SBS spectra at the sub-pixel level are extracted using the Steger algorithm and are connected and classified according to the signal type. On that basis, the frequency shifts of Brillouin scattering are calculated by using the center and radii of interference spectra after through fitting by the least squares method. Finally, the water temperatures are inverted by using the frequency shifts of Brillouin scattering. The results show that the processing method proposed in this paper can accurately calculate the frequency shift of Brillouin scattering. The measured errors of frequency shift are generally at an order of MHz, and the inversion accuracy of water temperature can be as low as 0.14 °C. This work is essential to the application for remote sensing the seawater parameters by using the Brillouin lidar technique. Full article

Development of energy-efficient and high-performance bioreactors requires progress in methods for assessing the key parameters of the biosynthesis process. With a wide variety of approaches and methods for determining the phase contact area in gas–liquid flows, the question of obtaining its accurate quantitative estimation remains open. Particularly challenging are the issues of getting information about the mass transfer coefficients instantly, as well as the development of predictive capabilities for the implementation of effective flow control in continuous fermentation both on the laboratory and industrial scales. Motivated by the opportunity to explore the possibility of applying classical and non-classical computer vision methods to the results of high-precision video records of bubble flows obtained during the experiment in the bioreactor vessel, we obtained a number of results presented in the paper. Characteristics of the bioreactor’s bubble flow were estimated first by classical computer vision (CCV) methods including an elliptic regression approach for single bubble boundaries selection and clustering, image transformation through a set of filters and developing an algorithm for separation of the overlapping bubbles. The application of the developed method for the entire video filming makes it possible to obtain parameter distributions and set dropout thresholds in order to obtain better estimates due to averaging. The developed CCV methodology was also tested and verified on a collected and labeled manual dataset. An onwards deep neural network (NN) approach was also applied, for instance the segmentation task, and has demonstrated certain advantages in terms of high segmentation resolution, while the classical one tends to be more speedy. Thus, in the current manuscript both advantages and disadvantages of the classical computer vision method (CCV) and neural network approach (NN) are discussed based on evaluation of bubbles’ number and their area defined. An approach to mass transfer coefficient estimation methodology in virtue of obtained results is also represented. Full article

The segmentation of hepatic vessels is crucial for liver surgical planning. It is also a challenging task because of its small diameter. Hepatic vessels are often captured in images of low contrast and resolution. Our research uses filter enhancement to improve their contrast, which helps with their detection and final segmentation. We have designed a specific fusion of the Ranking Orientation Responses of Path Operators (RORPO) enhancement filter with a raw image, and we have compared it with the fusion of different enhancement filters based on Hessian eigenvectors. Additionally, we have evaluated the 3D U-Net and 3D V-Net neural networks as segmentation architectures, and have selected 3D V-Net as a better segmentation architecture in combination with the vessel enhancement technique. Furthermore, to tackle the pixel imbalance between the liver (background) and vessels (foreground), we have examined several variants of the Dice Loss functions, and have selected the Weighted Dice Loss for its performance. We have used public 3D Image Reconstruction for Comparison of Algorithm Database (3D-IRCADb) dataset, in which we have manually improved upon the annotations of vessels, since the dataset has poor-quality annotations for certain patients. The experiments demonstrate that our method achieves a mean dice score of 76.2%, which outperforms other state-of-the-art techniques. Full article

A retinal vessel analysis is a procedure that can be used as an assessment of risks to the eye. This work proposes an unsupervised multimodal approach that improves the response of the Frangi filter, enabling automatic vessel segmentation. We propose a filter that computes pixel-level vessel continuity while introducing a local tolerance heuristic to fill in vessel discontinuities produced by the Frangi response. This proposal, called the local-sensitive connectivity filter (LS-CF), is compared against a naive connectivity filter to the baseline thresholded Frangi filter response and to the naive connectivity filter response in combination with the morphological closing and to the current approaches in the literature. The proposal was able to achieve competitive results in a variety of multimodal datasets. It was robust enough to outperform all the state-of-the-art approaches in the literature for the OSIRIX angiographic dataset in terms of accuracy and 4 out of 5 works in the case of the IOSTAR dataset while also outperforming several works in the case of the DRIVE and STARE datasets and 6 out of 10 in the CHASE-DB dataset. For the CHASE-DB, it also outperformed all the state-of-the-art unsupervised methods. Full article