Computerized Tomographic (CT) imaging is used to diagnose a wide range of medical diseases. CT images give a 3-dimensional picture by detecting density differences between different features in the body. In this application, we are studying lung tumors and how to monitor their growth.
Analysis of CT scans can give information about whether a tumor is growing and a danger to the patient. CT scans are taken over a period of time, and the images in the region of a suspected tumor are compared.
Through visualization, we are able to see the structures present in the CT images. Out of a series of hundreds of slices of data that compose a data set of the lungs, we can select out slices of interest that may contain a tumor, and create an isosurface at a pixel value that represents the density of the tumor. As you can see in the pictures below, the density of the tumor is very close to the density of the vascular structures around it.
We take the raw data from the DICOM images files of the CT scan and use it to calculate an isosurface, made up of polygons that we can visualize with our group software. A marching cubes algorithm is used to locate the isosurface. If every pixel in the images were used, the marching cubes algorithm would result in an enormous number of polygons for our visualization system. We have the capability of using every other pixel or every 3rd pixel, etc. for display purposes.
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