Athletes are susceptible to a wide variety of traumatic and non-traumatic vascular injuries to the lower limb. This paper aims to predict the three-dimensional flow pattern of blood through an S-shaped geometrical artery model. This model has created by using Fluid Structure Interaction (FSI) software. The modeling of the geometrical Sshaped artery is suitable for understanding the pattern of blood flow under constant normal blood pressure. In this study, a numerical method is used that works on the assumption that the blood is incompressible and Newtonian; thus, a laminar type of flow can be considered. The authors have compared the results with a previous study with FSI validation simulation. The validation and verification of the simulation studies is performed by comparing the maximum velocity at t = 0.4 s, because at this time, the blood accelerates rapidly. In addition, the resulting blood flow at various times, under the same boundary conditions in the S-shaped geometrical artery model, is presented. The graph shows that velocity increases linearly with time. Thus, it can be concluded that the flow of blood increases with respect to the pressure inside the body.

Summary Echocardiogram is an ultrasound image of the heart that demonstrates the size, motion and composition of cardiac structures and is also used to diagnose various abnormalities of the heart including abnormal chamber size, shape and congenital heart disease. Echocardiography provides important morphological and functional details of the heart. Most of the presented automatic cardiac disease recognition systems that use echocardiograms based on defective anatomical region detection. In this paper we present a simple technique for cardiac geometry detection via echocardiogram images which conquer these borders and exploits cues from cardiac structure. To demonstrate the effectiveness of this technique, we present results for cardiac geometry detection through difference intensity of echocardiogram images. We have developed a simple program code for the prediction of cardiac geometry using difference intensity of echocardiogram images. With this code, users can generate node or point for detection of cardiac geometry as ventricle and atrium in size, shape and location.