In this post I will talk about the Cancer Imaging Archive, a massive collection of freely available medical image data, and demonstrate how to programmatically access and retrieve that data through a Python client tapping into the provided REST API.
In this post I will demonstrate volume rendering of 3D image data in VTK. This will include loading and casting a segmented label-field, defining appropriate color and opacity transfer functions, setting volume properties, and performing volume rendering with different VTK classes, e.g., ray-casting or texture-mapping, which are implemented either on the CPU or GPU.
In this post I will show how to use SimpleITK to perform multi-modal segmentation on a T1 and T2 MRI dataset for better accuracy and performance. The tutorial will include input and output of MHD images, visualization tricks, as well as uni-modal and multi-modal segmentation of the datasets.
In this post I will demonstrate SimpleITK, an abstraction layer over the ITK library, to segment/label the white and gray matter from an MRI dataset. I will start with an intro on what SimpleITK is, what it can do, and how to install it. The tutorial will include loading a DICOM file-series, image smoothing/denoising, region-growing image filters, binary hole filling, as well as visualization tricks.
In this post I will show how to use VTK to trace rays emanating from the cell-centers of a source mesh, intersecting with another target mesh, and then show you how to cast subsequent rays bouncing off the target adhering to physics laws. This will include calculating the cell-centers in a mesh, calculating the normal vectors at those cells, vector visualization through glyphs, as well as other elements of visualization like textures and scene-lighting.
In this post I will demonstrate how to use VTK to perform ray-casting, i.e., intersecting lines/rays with surface meshes, and retrieving the coordinates of those intersection points. This will include loading such meshes from STL files, performing the intersections, as well as visualizing the mesh, lines, and points in VTK. In addition, I will present my very own pycaster package which cleanly wraps the VTK parts and allows for cleaner code and added functionality.
In this post I will demonstrate how to use VTK to read in a series of DICOM files from a CT examination and extract a mesh surface of the bone structures. I will then show you how to visualize the mesh with VTK and save it, the mesh that is, into an STL file.
I’ll be showing how to use the pydicom package and/or VTK to read a series of DICOM images into a NumPy array. This will involve reading metadata from the DICOM files and the pixel-data itself.
In this post I will show how to ‘convert’ NumPy arrays to VTK arrays and files by means of the vtk.util.numpy_support module and the little-known PyEVTK package respectively.
This post will show the only, currently available, way to integrate those beautiful renderings you might create with VTK into an IPython Notebook by means of off-screen-rendering.
PyScience 