الفهرس 
Coronary Artery Disease
METHODOLOGY OF THE PROPOSED SYSTEMOnly 14 pages are availabe for public view
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Abstract
Coronary angiography is a gold standard to examine the healthiness of the coronary artery (CA). Nevertheless, the angiography images have poor visualization owing to the overlapping and crossing of the arteries in the angiogram. At the same time, it is challenging to remove the vessels from X-ray angiograms. Typically, the vessels’ extraction is plagued by several issues, including poor contrast between the background and the coronary arteries; the overlying shades of the chest’s bones; and the unidentified/ readily malleable structure of the artery tree. Various clinical procedures depend strongly on the accurate evaluation of the CA, requiring precise appearance of the CA in the X-ray angiography images. Moreover, for medical diagnosis, it is required to quantify different parameters of the blood vessels, including their diameter, reflectiveness, vascularity, and aberrant branching. For instance, spotting the presence of arteries that are a specific width might indicate stenosis. To identify the seriousness of the vascular illness, and choose the best course of treatment, stenosis must be detected and graded. Therefore, this thesis offered two different computer-aided detection methods for the right coronary artery (RCA) detection/extraction to overcome the previously mentioned restrictions on the angiography images. The first proposed system is named Optimized Frangi System (OFS) in which Frangi filter-based improved adapted filter was presented. In the proposed OFS system, the genetic algorithm (GA) was applied to optimize its significant parameters for enhancing the automated segmentation process of the angiography images. The suggested improved Frangi filter (FF) was employed to create a vesselness map next to the contrast enrichment of the original angiography images. The primary RCA vessel was then extracted using regional growing segmentation. Compared to the state-of-the-art, the findings on the dataset of angiography images demonstrated the dominance of the vascular areas’ extraction with achieved accuracy 98.58%. Instead, the second proposed system is named Main Artery selector System (MASS), which depends on combining the geometric shape characteristics with the catheter localization, and geodesic distance transformation through two stages. In the first stage, the acquired image was subjected to contrast enhancement during the preprocessing stage as performed in the proposed OFS, before the Jerman filter was used to create the vesselness map, and K-means. The primary blood vessel of the RCA was then extracted in stage 2 using the geometric shape characteristics of the RCA, the skeleton gradient transform, and the start/end locations. The outcomes confirmed the predominance of the second proposed system for evacuating the RCA vessel, with 2%-30% improved Dice values assessed with using conventional techniques. Finally, the stenosis was detected using window fitting technique for calculating the average diameter after localizing the center-line points. The measured accuracy, and mean percentage error confirmed the effectiveness of the proposed system for the stenosis detection compared to the reported corresponding stenosis diameter by the physician.