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 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.