The concept of X-ray imaging was introduced by an Italian radiologist Alessandro Vallebona in the early twentieth century. This was one of the first methods proposed to look inside opaque objects. Plenty of machines such as computed tomography (CT), magnetic resonance imaging (MRI), positron emission tomography (PET), and ultrasound have been invented since then to help professionals plan surgery, perform nondestructive testing (NDT) of industrial components, search for oil, locate fish schools and examine the proper development of fetuses etc. All the above devices produce a volumetric dataset as an output as opposed to a 2D image generated from X-ray imaging. These machines are used on a daily basis in the fields of medicine and industrial manufacturing. Radiologists and doctors use them for diagnosis like finding defective positioning of a body part, treatment planning like marking regions for therapy, and intra-operative support like monitoring life critical structures etc . Quality control engineers capitalize on them by performing NDT, first part inspection, and measurement of internal features. In most cases, the output of a scanning device is a raw dataset which is composed of scalar values. Each scalar value describes the specimen at a distinct spatial location in terms of the scanning technique employed. For example, a medical CT uses Hounsfield units to define the densities of the specimen, MRI measures the content of hydrogen atoms and ultrasound records the reflected echo of sound waves to calculate distances and densities of the tissue. A 3D dataset produced by such scanners can be very large and complex to be of any value in its raw format. The purpose of scanning is not to produce meaningful numbers in a high quantity but to be able to get insight into the specimen and to be able to deduce reliable results efficiently. Visualization techniques can be applied to transform large and complex datasets into clear and easy to understand expressive images. The user may have the ability to control the display parameters and to interact with the rendered images. Visualization can reduce the time taken to understand data, to find trends, and to extract valuable conclusions from the dataset.