Segmentation is usually performed in the spatial domain and is likely hindered by similar intensity, intensity inhomogeneity, and partial volume effect. In this article, a visual-selection method is proposed to carry out segmentation in the intensity space such that the aforementioned difficulties are alleviated and better results can be produced. The proposed procedure utilizes volume rendering to explore the input data and builds a transfer function, encoding the intensity distribution of the target. Then, by using this transfer function and image processing techniques, a region of interest (ROI) is constructed in the intensity field. At the following stage, a texture-based region growing computation is conducted to extract the target from the ROI. Experiments show that the proposed method produces high quality results for a phantom which is composed of plates with similar intensities and textures. It also out-performs a traditional segmentation system in separating organs and tissues from a torso CT-scan data set.
The possible achievements of accurate and intuitive 3D image segmentation are endless. For our specific research, we aim to give doctors around the world, regardless of their computer knowledge, a virtual reality (VR) 3D image segmentation tool which allows medical professionals to better visualize their patients’ data sets, thus attaining the best understanding of their respective conditions.We implemented an intuitive virtual reality interface that can accurately display MRI and CT scans and quickly and precisely segment 3D images, offering two different segmentation algorithms. Simply put, our application must be able to fit into even the most busy and practiced physicians’ workdays while providing them with a new tool, the likes of which they have never seen before.