Computer Aided Modeling and Finite Element Analysis of Human Elbow

Computer Aided Modeling and Finite Element Analysis of Human Elbow

Arpan Gupta (Indian Institute of Technology Mandi, India) and O.P. Singh (Indian Institute of Technology Mandi, India)
DOI: 10.4018/978-1-5225-0571-6.ch042
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Abstract

Finite element modeling (FEM) plays a significant role in the design of various devices in the engineering field of automotive, aerospace, defense etc. In the recent past, FEM is assisting engineers and healthcare professional in analyzing and designing various medical devices with advanced functionality. Computer aided engineering can predict failure circumstances, which can be avoided for the health and well-being of people. In this research work, computer aided engineering analysis of human elbow is presented beginning with modeling of human elbow from medical image data, and predicting the stresses in elbow during carrying heavy loads. The analysis is performed by using finite element method. The results predict the stress level and displacement in the human bone during heavy weight lifting. Thus, it can be used to predict the safe load that a particular person can carry without bone injury. The present analysis focused on a particular model of bone for a particular individual. However, safe load can be determined for various age groups by generating more detailed model including tendons, ligaments and by using patient specific material properties.
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Development Of A Multi-Component Cad Model Of Human Elbow Structure

Grey scale images are processed to understand the visual aspects of 2D data and to enhance the images. The input data was 2D grey scale bio medical images of human elbow as shown in Figure 1.

Figure 1.

MRI 2D image of elbow

The image is further processed to get better visualization by varying parameters such as intensity and contrast. This manipulation in parameters improves the resolution quality and clarity level. Figure 2 shows the final input 2D image. As it can be seen, the image is significantly enhanced, showing clearly the soft tissue region and bone separately. This distinction will be used for the development of multi-component model, i.e., for separating bone from the soft tissue.

Figure 2.

Sectional view of input image

Figure 3 shows the three different sectional views of grayscale image viz. axial, coronal, and sagittal planes view.

Figure 3.

Cross-sectional view of input image

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