Biomedical Image Processing for Human Elbow Akshay Vishnoi, Sharad Mehta, Arpan Gupta Department of Mechanical Engineering Graphic Era University Dehradun, India akshaygeu001@gmail.com, sharadm158@gmail.com Abstract Biomedical image processing is an emerging field for analyzing medical images to develop models for clinical analysis and medical intervention. Recently with the development in biomechanics, the models developed can be used for mechanical stress and vibrational analysis of human body parts. In this work, computer aided design (CAD) models are developed for a patient specific human elbow using Magnetic Resonance Imaging (MRI) data using open source software ITK-snap. The 3-D CAD model is developed using segmentation of MRI data using manual method and automatic methods. Different algorithms and parameter variation have been implemented for development of the human elbow model. Keywords Biomedical imaging; CT scan MRI images; CAD model development; segmentation. The elbow joint is an important joint in human body and it is composed of bones, ligaments, tendons and cartilages. The elbow is a very complex joint in the human body. To obtain the 3D reconstructed CAD model of human elbow, we have used 2D medical grey scale image from MRI data. We have used open source software ITK-snap with its different methods and algorithm to generate the CAD model of human elbow. II. INPUT DATA The 2D medical images were obtained from CT scan images and MRI data (grey scale images) from Radiology department. Fig. 1 shows the overall patient elbow that is being analyzed. The images later on show the focused elbow region in different planes. I. INTRODUCTION Biomedical image processing enables analysis and visualization of human body parts and internal structures such as bones, ligaments, tendons, etc. This is generally accomplished through radiography, Magnetic Resonance Imaging (MRI), Ultrasound, Computerized tomography (CT scan) data, thermography, nuclear medicine, etc. Images obtained involve basic image processing techniques such as improving intensity, noise cleaning, filtering, etc. [1]. In the past, this field was related to experimental analysis, however, with the development of computers and image processing capabilities this area has seen drastic improvement over past decades. Various methods and algorithms have been used for image enhancement, grey-level mapping, spectral analysis, region extraction, etc. [2]. Further image extraction and 3D model reconstruction from MRI and CT scan data has received significant attention [3]. The 3D reconstructed CAD models are used in tissue engineering [4], [5]. CAD models are also used for developing models using rapid prototyping [6]. Hacene Ameddah et al. [7], [8] successfully published their work on mechanics using 2D medical images and their successful segmentation to create a 3D model of human Knee for analysis purpose. Figure 1 Greyscale image of human elbow The images were processed and parameters were manipulated to obtain better resolution and clarity in the image. The first processing was done on the image intensity level, which made the image appear distinct from the background. Further processing and manipulation were done such as slice display order, contrast adjustment, layer inspection, display appearance etc. Final 2D output for a single image is show in Fig. 2.
view. The corresponding region on interest in another view can also be selected by drawing appropriate region. This process is manually repeated for various sectional views and the model is formed. This method is quite laborious, however it gives the discretion of selection to the user. Figure 2 Greyscale image of one sectional view The images were given as input to the software ITK-snap. The view in the software is show in fig. 3, where three windows show axial, coronal, and sagittal planes views of the human elbow. The fourth window is the region where 3D model will be developed. Figure 4 Manual segmentation of MRI data. IV. AUTOMATIC SEGMENTATION The methodology behind automatic segmentation in ITK-snap is based on an algorithm called as snake evolution. The term snake is used to refer to a closed curve (or surface in 3D) that represents segmentation. In snake evolution method, the snake evolves from a very rough estimate of the anatomical structure of interest to a very close approximation of the structure; there are two methods to perform automatic segmentation. One is intensity based region method and another method is image edge based method. In this paper, work is accomplished by performing intensity region based method because the results obtained by this method are more accurate and reliable than the image edge based method. And the probabilities of errors are also reduced as compare to image edge based method. Figure 3 Software view showing three cross-sectional view of the input data. III. MANUAL SEGMENTATION After basic image manipulation and enhancement, the segmentation process is initiated. There are different methods for 3D model construction. The simplest, but lengthiest is the manual segmentation. Segmentation in bio images in SNAP stands for allocating a label to each voxel in the structure. A label is a number between 0 and 255 according to the software. Associated with each label is a name and a set of display settings, such as the color used to display the label. The green portion around the polygon indicates all currently selected vertices. To paint closed polygons, the polygon tool is used. A closed loop is drawn over the region of interest (such as tissue or bone etc as shown in Fig. 4) in one cross-sectional A. INTENSITY BASED REGIONS This procedure starts with filling up the intensity regions using intensity filler, here we assign the value of intensity on a scale of 0-1. Maximum intensity assigned is 1 and minimum is 0. These parameters are altered by modifying the parameters lower threshold, upper threshold and smoothness. Lower threshold value was kept as 725.35, upper threshold was kept 242.96 and smoothness was kept as 7.69. The intensity based region method is demonstrated in Fig. 5. In this figure, blue region is of maximum intensity and white region, which is indicating elbow portion is assigned to minimum with varying intensity.
C. SNAKE PROPAGATION To propagate snake in such a required fashion, we need to set propagation parameters and indulge forces like balloon force and curvature force. Balloon force may vary from contracting nature to expanding nature and as well as to static mode also. The balloon force governs the nature of bubbles, while final iterations are in process. Curvature forces can vary from detailed, smooth and spherical nature. Here balloon force being expanding 0.8 and curvature force being detailed 0.40. Figure 7 describes the fashion of snake propagation. Figure 5 Intensity based regions. B. SNAKE INITIALISATION In next step to complete automatic segmentation bubbles are introduced in such a fashion so that they expands three dimensionally to cover the region which is assigned to zero or minimum intensities. These bubbles may vary in quantities according to the requirement and structure. Initially bubbles are placed in 2D planes, but simultaneously they take place three dimensionally, that means, we assign bubbles in one single plane only automatically they allocate their location in other respective plane, and expands. Figure 7 Bubbles expansion and propagation At the same time a 3D outlook of allocated bubbles can be obtained in 3D output window as shown in figure 8. Figure 6 Bubbles initialization Figure 8 3-D Outlook of allocated bubbles
V. RESULTS 3D reconstructed model is obtained after performing number of iterations (~2200). The model is shown in the fourth window of the software (Fig. 9, 10). The model is exported in CAD format i.e. STL (stereolithography model), which can be further used for mechanical analysis in finite element softwares. for bones and ligaments is kept at red color and rest of the elbow structure is kept blue in color, which helps in differentiating between elbow bone and elbow structure. The CAD model is viewed by using an open source software ParaView. This software helps in visualization of the final 3D CAD model of human elbow structure. Figure 9 shows the CAD model under process in 2D window as well as in 3D window. Figure 11 Final 3D CAD model in ParaView. Figure 9 Model under process window Figure 10 shows the 3D outlook window of ITK-snap software complete CAD model of elbow structure in ITK-snap software. VI. DISCUSSION The developed three dimensional CAD model of human elbow bone can be used for further research work. Researchers can use this model for further mechanical analysis such as force/stress analysis, vibrational analysis, etc. This can be carried out using finite element analysis or other analysis methods. The 3D CAD models developed can be used to develop instruments and artificial organs which can help handicaps. This model and analysis details will also help to innovate new medical tools which will help patients in different sense. This work can help to develop a CAD model for different body parts like bones, soft tissue, ligaments, skin etc. with their different stuff properties which is important for safety point of view. Figure 10 3D CAD model in ITK-snap Figure 11 represents the final 3D CAD model of human elbow, which is successfully visualized by ParaView software. ParaView software also allows to represent the CAD model of human elbow with different labels, as in figure 11, the cavity VII. CONCLUSION In this work, biomedical image processing has been carried out for MRI data of a human elbow. The 2D greyscale image for different cross-sectional views are segmented to form a 3D restructured model of a human elbow. The segmentation can be broadly performed using manual and automatic methods. The automatic methods have various algorithms and parameters that can be varied to obtain various 3D models. The segmentation was performed using open source software ITK-snap. The 3D model obtained was exported to STL format, which can be used for mechanical finite element analysis, to perform stress and vibration analysis. Thus the
study can be of great help in designing biomedical instruments and devices, aids for handicaps, and sport equipments. REFERENCES [1] H. K. Huang, Biomedical image processing., Crit. Rev. Bioeng., vol. 5, no. 3, pp. 185 271, 1980. [2] A. P. Dhawan, A review on biomedical image processing and future trends, Comput. Methods Programs Biomed., vol. 31, no. 3, pp. 141 183, 1990. [3] B. Starly, Z. Fang, W. Sun, A. Shokoufandeh, and W. Regli, Three-dimensional reconstruction for medical- CAD modeling, Comput. Aided. Des. Appl., vol. 2, no. 1 4, pp. 431 438, 2005. [4] W. Sun and P. Lal, Recent development on computer aided tissue engineering a review, Comput. Methods Programs Biomed., vol. 67, no. 2, pp. 85 103, 2002. [5] W. Sun, B. Starly, J. Nam, and A. Darling, Bio-CAD modeling and its applications in computer-aided tissue engineering, Comput. Des., vol. 37, no. 11, pp. 1097 1114, 2005. [6] R. Jamieson and H. Hacker, Direct slicing of CAD models for rapid prototyping, Rapid Prototyp. J., vol. 1, no. 2, pp. 4 12, 1995. [7] H. Ameddah and M. Assas, BIO-CAD MODELING OF HUMAN KNEE. [8] H. Ameddah and M. Assas, Three-Dimensional (3D) Bio-Cad Modeling of Human Knee, Adv. Sci. Lett., vol. 19, no. 3, pp. 932 936, 2013.