A new rule-based algorithm for angiography imaging enhancement of coronary arteries

Coronary angiography is the reference technique for the diagnosis of cardiovascular diseases; however, the raw images acquired with this modality are often noisy, characterized by low contrast, and challenging to interpret. The aim of this thesis is the development of a MATLAB-based code for the processing of angiographic sequences, designed to enhance the visibility of the catheter and coronary arteries while preserving the main background anatomical information. The proposed workflow consists of three steps: preprocessing, automatic identification of regions of interest through binary masks, and selective filtering. The first step improves the overall quality of the images, the second detects the catheter and coronary arteries, and the third highlights these structures by reducing background details. The method was tested on sequences acquired with the Skan-C Venus system. A quantitative evaluation, based on fidelity, contrast, and structural similarity indices, compared the final images with the preprocessed ones, confirming that the selective filtering step effectively enhances the regions of interest without introducing significant alterations or compromising temporal continuity across frames. The results are encouraging and demonstrate the feasibility of the proposed workflow. Nevertheless, some limitations remain, mainly related to the limited number of sequences analyzed, the incomplete recognition of thin coronary branches, and the variability of acquisition conditions. These aspects suggest possible future developments toward increased robustness and adaptability of the proposed approach.