CAELinux is a Linux distribution which is bundled with free software packages related to Computer Aided Engineering (CAE). The free software packages include software that can build a three dimensional solid model, programs that can mesh a geometry, software for carrying out Finite Element Analysis (FEA), programs that can carry out image processing etc. Present work has two goals: 1) To give a brief description of CAELinux 2) To demonstrate that CAELinux could be useful for Computer Aided Engineering, using an example of the three dimensional reconstruction of a pig liver from a stack of CT-scan images. One can note that instead of using CAELinux, using commercial software for reconstructing the liver would cost a lot of money. One can also note that CAELinux is a free and open source operating system and all software packages that are included in the operating system are also free. Hence one can conclude that CAELinux could be a very useful tool in application areas like surgical simulation which require three dimensional reconstructions of biological organs. Also, one can see that CAELinux could be a very useful tool for Computer Aided Engineering, in general.
Active contour segmentation and its robust implementation using level set methods are well-established theoretical approaches that have been studied thoroughly in the image analysis literature. Despite the existence of these powerful segmentation methods, the needs of clinical research continue to be fulfilled, to a large extent, using slice-by-slice manual tracing. To bridge the gap between methodological advances and clinical routine, we developed an open source application called ITK-SNAP, which is intended to make level set segmentation easily accessible to a wide range of users, including those with little or no mathematical expertise. This paper describes the methods and software engineering philosophy behind this new tool and provides the results of validation experiments performed in the context of an ongoing child autism neuroimaging study. The validation establishes SNAP intrarater and interrater reliability and overlap error statistics for the caudate nucleus and finds that SNAP is a highly reliable and efficient alternative to manual tracing. Analogous results for lateral ventricle Wayne Rasband of NIH has created ImageJ, an open source Java-written program that is now at version 1.31 and is used for many imaging applications, including those that that span the gamut from skin analysis to neuroscience. ImageJ is in the public domain and runs on any operating system (OS). ImageJ is easy to use and can do many imaging manipulations. A very large and knowledgeable group makes up the user community for ImageJ. Topics covered are imaging abilities; cross platform; image formats support as of June 2004; extensions, including macros and plug-ins; and imaging library. NIH reports tens of thousands of downloads at a rate of about 24,000 per month currently. ImageJ can read most of the widely used and significant formats used in biomedical images. Manipulations supported are read write of image files and operations on separate pixels, image regions, entire images, and volumes (stacks in ImageJ). Basic operations supported include convolution, edge detection, Fourier transform, histogram and particle analyses, editing and color manipulation, and more advanced operations, as well as visualization. For assistance in using ImageJ, users e-mail each other, and the user base is highly knowledgeable and will answer requests on the mailing list. A thorough manual with many examples and illustrations has been written by Tony Collins of the Wright Cell Imaging Facility at Toronto Western Research Institute and is available, along with other listed resources, via the Web.  In this work, a procedure is presented for the reconstruction of biological organs from image sequences obtained through CT-scan. Although commercial software, which can accomplish this task, are readily available, the procedure presented here needs only free software. The procedure has been applied to reconstruct a liver from the scan data available in literature. 3D biological organs obtained this way can be used for the finite element analysis of biological organs and this has been demonstrated by carrying out an FE analysis on the reconstructed liver.  This work presents a methodology to reconstruct 3D biological organs from image sequences or other scan data using readily available free softwares with the final goal of using the organs (3D solids) for finite element analysis. The methodology deals with issues such as segmentation, conversion to polygonal surface meshes, and finally conversion of these meshes to 3D solids. The user is able to control the detail or the level of complexity of the solid constructed. The methodology is illustrated using 3D reconstruction of a porcine liver as an example. Finally, the reconstructed liver is imported into the commercial software ANSYS, and together with a cyst inside the liver, a nonlinear analysis performed. The results confirm that the methodology can be used for obtaining 3D geometry of biological organs. The results also demonstrate that the geometry obtained by following this methodology can be used for the nonlinear finite element analysis of organs. The methodology (or the procedure) would be of use in surgery planning and surgery simulation since both of these extensively use finite elements for numerical simulations and it is better if these simulations are carried out on patient specific organ geometries. Instead of following the present methodology, it would cost a lot to buy a commercial software which can reconstruct 3D biological organs from scanned image sequences.  Three dimensional digital model of a representative human kidney is needed for a surgical simulator that is capable of simulating a laparoscopic surgery involving kidney. Buying a three dimensional computer model of a representative human kidney, or reconstructing a human kidney from an image sequence using commercial software, both involve (sometimes significant amount of) money. In this paper, author has shown that one can obtain a three dimensional surface model of human kidney by making use of images from the Visible Human Data Set and a few free software packages (ImageJ, ITK-SNAP, and MeshLab in particular). Images from the Visible Human Data Set, and the software packages used here, both do not cost anything. Hence, the practice of extracting the geometry of a representative human kidney for free, as illustrated in the present work, could be a free alternative to the use of expensive commercial software or to the purchase of a digital model.  