Latest revision as of 15:36, 7 May 2012

Understanding Image Basics

In this chapter. . .

• Supported graphic formats overview
• Understanding the MIPAV dimensionality and coordinate system
• Data types
• Headers
• Endianness
• Viewing and editing image attributes

This chapter also explains the dimensionality and coordinate system used by MIPAV, the data types MIPAV supports, the header offset information, endianness, and how to view and edit image attributes.It also provides a basic information about image file formats supported by MIPAV. More information about graphic file formats and the formats that MIPAV supports can be found in Appendix C: "Supported formats" .

Supported graphic formats overview

MIPAV supports three categories of graphic file formats: vector (i.e., volume of interest, or VOI), bitmap (i.e., 2D to 5D grayscale or RGB images), and 3D images.
Vector file formats are usually used to store line-based elements. These elements can be geometric shapes, such as polygons, curves, or splines. MIPAV can read and generate several vector-formatted file types including the MIPAV lookup table (LUT), MIPAV MTX, MIPAV Graphics Plot (PLT), and MIPAV VOI. See also Appendix C: "Supported formats" .
Bitmap file formats are usually used to store real-world graphics data e.g., photographs or medical images such as CT scans. The data portion of bitmap files contains numerical data that indicates the color of each pixel or voxel in the image. MIPAV can read and generate a number of bitmap formatted file types, such as Adobe Photoshop (PSD), Graphics Interchange File (GIF), Sun Raster (RS), Tagged Image File Format (TIFF), and Truevision Graphics Adapter (TGA).
</font>MIPAV uses 3D graphic formatted files to store descriptions of the color and shape of 3D models of real-world and imaginary objects.
To support the wide range of image-processing needs of the NIH intramural research community, MIPAV reads and writes the image file types indicated in Table 1, refer to Appendix C: "Supported formats".

Understanding the MIPAV dimensionality and coordinate system

Internally, MIPAV is designed to store images of any dimensionality and thus is n dimensional. Figure 1 is a model of an example 3D-image where each position within the grid represents a voxel.
voxel-The smallest distinguishable cube-shaped part of a 3D-image.

The voxel in the upper left-most position is considered the origin of the dataset. (A volumetric dataset is a set of cross-sectional images). Ideally, each voxel would be isotropic (i.e., cubic), but clinical datasets are often acquired in such a manner that the resolution in the x and y dimensions is greater than the resolution in the z dimension, thus producing rectangular voxels (elongated in the z. This voxel anisotropy is taken into account in the execution of the algorithms found in MIPAV.

Because MIPAV's image storage buffer is n-dimensional, it can store 2D, 3D, and 4D medical images. CT and MRI are examples of 3D medical images where the three dimensions (x, y, and z) are spatial dimensions. Other 3D datasets include fluoroscopy volumes, where the first two dimensions are spatial and the third dimension is time.

Re-ordering data from image to voxel order

When recorded in scanner or image order, multi-component images are stored as consecutive volumes. This is convenient for visualization, since one can easily render a particular 3D volume, however, it is inconvenient for parallel processing, as one must read the entire 4D image in order to get the specific component(s) for a particular processing.

In voxel order all components for a particular voxel are stored together. This allows to read the image one voxel at a time, or skip ahead to a particular voxel, without storing the entire image into memory.

See also: [Orientation and Voxel-Order Terminology: RAS, LAS, LPI, RPI, XYZ and All That].

Data types

A data type is the classification of a particular type of information. For example, a floating point data type indicates a number with a decimal point. Data read from image files are stored in MIPAV's data structures. MIPAV supports all the basic data types shown in Table 1 and may in the future support more composite data types including color and complex numbers. The image data read from the file is stored in a data buffer of the same basic data type, preventing the reduction of image information that results from conversion into a fixed data type.

Headers

If you attempt to open a raw image, you may need to supply MIPAV with the header offset. The header offset indicates the size of the space reserved at the beginning of the file where specific types of information is kept. This space, which is called the header, precedes the image data. If you know the length of the header, enter it in the header offset text box. When MIPAV accesses the file, it skips the header offset and begins to read the image data. Note that not all image file formats have a header.

Endianness

Endianness refers to the byte ordering of the data. Some computers order the data with the least significant byte (LSB) first followed by the most significant byte (MSB). This byte order is referred as "little endian" or Intel byte ordering. machines that use little-endian byte ordering are VAXes, Intel x86, and Pentium. The reverse is MSB then LSB, which is referred as "big endian" or Motorola byte ordering. Machines that use big-endian byte ordering are IBM System 3D, RISC, and a Motorola 680x0. MIPAV is biendian; that is, it supports both big- and little-endian byte-ordering formats.

Viewing and editing image attributes

Information about the image appears in the Image Information dialog box and in the Image Attributes dialog box.

The Image Attributes window displays the name of the image file and information about the resolution of the image. The About Image window displays more detailed information about the image, such as the orientation of the image or the number of bits per pixel or voxel.

Viewing image attributes

To view the attributes of an image, select Image > Attributes > View Header in the MIPAV window. For DICOM datasets, the Image Information dialog box similar to the one shown in Figure 2-a appears. For non-DICOM datasets, an Image Information dialog box similar to the one in Figure 2-b appears.

Figure 2. The Info dialog box for DICOM (a) and non-DICOM (b) images