Journal of Imaging

Coronary computed tomography angiography (CTCA) is established as a first-line investigation for suspected coronary artery disease and is increasingly used for anatomical, functional, and structural cardiac assessment. Fractal analysis (FA) provides a framework for quantifying geometric and spatial complexity that is not captured by conventional measures of stenosis, plaque burden, or chamber geometry. Emerging cardiac CT studies have applied fractal dimension (FD), one quantitative measure derived from FA to coronary plaque, myocardial perfusion, and ventricular trabecular architecture. In coronary imaging, FD-based descriptors of plaque heterogeneity and boundary complexity have shown incremental diagnostic information beyond conventional plaque measures. In dynamic CT perfusion, fractal metrics quantify the spatial heterogeneity of myocardial enhancement and may help distinguish macrovascular from microvascular ischaemia. Cardiac CT studies have also demonstrated the feasibility of deriving ventricular trabecular FD and discriminating left ventricular non-compaction from other cardiomyopathies. This review synthesises the current evidence for FA in cardiac CT, evaluates methodological limitations and evidence maturity, and outlines priorities for standardisation, reproducibility, multimodal integration, and prospective clinical validation.

This study aims to improve quantitative precipitation estimation at high spatial and temporal resolutions using Meteosat Second Generation (MSG) satellite imagery. In this context, two complementary models are exploited for the classification of precipitation intensities into five classes, namely: No rain, Light rain, Moderate rain, Heavy rain and Very heavy rain. To better exploit the information, XGBoost captures the radiometric response at the individual pixel level, while U-Net extracts the texture and spatial context of the cloud neighborhood. The classification architecture merges the probabilistic responses derived from spectral signatures via XGBoost with the extraction of cloud spatio-textural features from the deep multiclass U-Net network. Their output probabilities are combined and recalibrated using a logistic regression meta-classifier into a unified probability vector. To obtain instantaneous estimates, the final value corresponds to the sum of the previously defined precipitation intensities, each multiplied by the probability predicted by the meta-classifier. The models are trained on ground radar observations and validated against temporally independent radar test events from Sétif. Instantaneous precipitation estimates, as well as accumulations, were calculated. The proposed hybrid model significantly outperforms state-of-the-art benchmark architectures (notably, MCM, Ada/Bag-ANN, and DST-ANN/SVM/RF). At the instantaneous scale, the model achieves remarkable accuracy, with a correlation coefficient CC = 0.983, a reduction of RMSE to 0.40 mm/h (representing a decrease of over 45% compared to U-Net alone), and a near-zero bias (BIAS = −0.02 mm/h). During longer-scale temporal integrations, the model maintains strong volumetric consistency and minimal cumulative drift across temporal scales, reaching a CC = 0.993 and an RMSE = 1.38 mm/24 h at the daily scale, as well as a CC = 0.997, an RMSE = 2.76 mm/week, and a BIAS = 0.02 mm/week at the weekly scale. These results confirm that combining spatio-textural and point-wise spectral probabilities provides a reliable solution for high-resolution quantitative precipitation estimation, offering valuable inputs for regional hydrometeorological monitoring in complex geophysical environments. While the trained model is tailored to Mediterranean winter conditions and requires targeted retraining for application to other climatic regions or summer convective regimes, these results confirm that combining spatio-textural and point-wise spectral probabilities provides a highly reliable solution for high-resolution quantitative precipitation estimation.

Fire detection in low-resolution surveillance videos at night is challenging under poor illumination. Conventional methods rely on colour information or computationally intensive deep-learning models, potentially limiting their suitability for grayscale video and resource-constrained environments. This study presents a night-time fire detection method that operates on grayscale video frames and jointly models temporal brightness fluctuations and spatial motion cues using a motion-based temporal representation and a brightness-gradient support vector machine (SVM) classifier. A sliding temporal window aggregates frame-level evidence, thereby reducing isolated false detections and providing more stable fire-event decisions over time. Experiments on the UCF-Crime arson and normal subsets (30 FPS) demonstrate promising video-level fire detection, with all evaluated fire-containing videos correctly identified. The method also shows promising classification performance on normal videos. At the frame level, the mean per-video false alarm ratio and miss rate across fire-containing videos are 4.37 ± 13.69% and 14.00 ± 11.24%, respectively. Ablation results for fire-containing videos show that the full system achieves a pooled frame-level miss rate of 9.51% and an FP-to-ground-truth fire-frame ratio of 0.76%. The CPU-only system processes a single video stream at 32–34 FPS while updating all 12 visualisation panels, demonstrating its potential for real-time surveillance.