An Automated Recognition Algorithm for Mesenteric Arterial Embolism Detection
Acute superior mesenteric artery thrombosis is a high-mortality disease computed tomography angiography provides a definitive diagnosis. Currently,clinical observation is the primary diagnostic method,as there is no computer-aided diagnosis system for automatically identifying superior mesenteric artery embolism. Therefore,this paper proposes the"Abdominal Artery Segmentation—Branch Annotation—Embolism Recognition"approach. First,an improved region growing method is used for automatic abdominal artery segmentation,achieving precise abdominal vascular segmentation. Then a graph matching algorithm is used to realize branch annotation by combining image position information and geometric features to accurately extract the branches of the superior mesenteric artery from the abdominal artery theory. Finally,a grayscale co-occurrence matrix is established to extract the first-order and second-order texture features of the image,and embolism identification is realized through the eXtreme Gradient Boosting ( XGBoost) algorithm classifier. The experimental results show that in the vascular segmentation stage,the vascular networks and detailed features of this experiment are clearer,the average accuracy of embolism recognition can reach 0.939,and the recognition effect is good in different types of superior mesenteric artery embolism samples.
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