DEVELOPMENT AND VALIDATION OF A PROTOTYPE PIPELINE FOR BIOMEDICAL IMAGE ANALYSIS IN CARDIOVASCULAR MONITORING

Cardiovascular diseases remain the leading cause of mortality, which determines the relevance of developing robust methods for cardiac image analysis and patient monitoring. This paper presents and validates a prototype pipeline for automated preprocessing and segmentation of biomedical images, developed within the framework of a scientific project (IRN: AP05132044). The experimental protocol is implemented on the Heart Database dataset (18 patients, 3D+t MRI (three-dimensional with time dimension)), using expert endocardial masks and 36 slices (diastole/systole, 2 slices per patient). Performance evaluation was conducted using PSNR, SSIM, Dice, and IoU metrics. A comparative analysis was performed across five preprocessing modes: none, Gaussian, wavelet, NLM, and hybrid (wavelet + NLM + CLAHE). The results show that the NLM method achieves the best denoising performance (PSNR = 26.07±0.33 dB vs. 22.85±0.21 dB for the baseline, p = 1.46×10⁻¹¹, Wilcoxon test), while the highest segmentation accuracy is obtained with hybrid preprocessing (Dice = 0.681±0.176 vs. 0.636±0.153 without preprocessing, p = 0.018). An analysis of challenging cases revealed that segmentation performance is strongly influenced by endocardial contrast and geometric complexity across different phases of the cardiac cycle. The obtained results demonstrate a statistically significant impact of the preprocessing stage on downstream segmentation quality and confirm the feasibility of the integrated “preprocessing-segmentation-evaluation” pipeline as an engineering foundation for clinical frameworks. The proposed approach is designed for reproducible performance under real-world data heterogeneity and can serve as a basis for further integration into clinical information systems.