Image Postprocessing in Digital Radiology—A Primer for Technologists

Major Components of a Digital Imaging System. Image processing consists of preprocessing, in which corrections are made to the raw data, and postprocessing, in which displayed images can be manipulated for the purpose of enhancing diagnostic interpretation capability.

Digital Image Format. The format of a digital image includes columns (M) and rows (N) that define small, square regions called picture elements, or pixels. The field-of-view (FOV) is one dimension of the matrix and is used to calculate the size of the pixel. A right-handed coordinate system is used to describe digital images in the spatial location domain. See text for further explanation.

Effects of Matrix Sizes and Bit Depths. Visual effect of different matrix sizes and bit depths on spatial and contrast resolution of an image, respectively. (Reprinted with permission from Bruno Jaggi, PEng–Biomedical Engineering, British Columbia Institute of Technology.)

Fourier Transform. The Fourier transform (FT) is used to convert an image in the spatial location domain into an image in the spatial frequency domain for processing by a computer. The inverse FT (FT⁻¹) is used to convert the spatial frequency domain image back into a spatial location image for viewing by human observers.

Classes of Image Processing Operations. The 5 fundamental classes of image processing operations according to image processing experts Gonzalez [3] and Baxes [2]. See text for further descriptions of each of these.

Conceptual Framework for a Point Processing Operation. As can be seen, one (point) input image pixel is mapped onto the corresponding output image pixel. The output image pixel value is located at the same location as on the input image matrix depending on the value of the input image pixel.

The Histogram. Graph of the number of pixels in the entire image having the same gray levels (density values), plotted as a function of the gray levels, is referred to as a histogram. See text for further explanation. © 2004, the American Society of Radiologic Technologists. All rights reserved. Reprinted with permission of the ASRT.

The Look-Up-Table—Numeric. Numerical example of the concept of a look-up table (LUT); see text for further explanation. (Reprinted with permission from Perry Sprawls, PhD, Emory University, Atlanta, GA.)

The Look-Up Table–Graphic. Graphic example of the look-up table (LUT) concept. See text for further explanation. (Reprinted with permission from Perry Sprawls, PhD, Emory University, Atlanta, GA.)

The Look-Up Table in Postprocessing. Example of use of the look-up table (LUT) in postprocessing of a chest image. (Reprinted with permission from Perry Sprawls, PhD, Emory University, Atlanta, GA.)

Window Width and Window Level. Graphic illustration of the definitions of the image postprocessing concepts of window width (WW) and the window level (WL).

Effect of Window Width. Visual effect of the window width (WW) on the contrast of an image, with window level (WL) held constant.

Effect of Window Level. Visual effect of the window level (WL) on the brightness of an image, with window width (WW) held constant.

Local Processing. A local processing operation is one in which the output image pixel value is obtained from a small area of pixels around the corresponding input pixel. See text for further explanation.

Convolution Technique. The image postprocessing operation of digital filtering in the spatial location domain, known as the convolution technique.

High-Pass Digital Image Processing Filter. The effect of a high-pass digital image processing filter on an input image. The output image is much sharper than the input image. This filter suppresses the low spatial frequencies in the image which contains the contrast information.

Low-Pass Digital Image Processing Filter. The effect of a low-pass digital imaging processing filter. The output image is blurred (smoothing) compared to the input image.

Unsharp Masking. The image postprocessing technique of unsharp masking is essentially used to sharpen images. See text for further explanation.

Geometric Processing Effect. Effect of geometric processing operation on an input image. The goal of the processing is to change the orientation of the input image.

The Compression Ratio. Graphic illustration of compression ratio. If the original image size is 512 × 512 × 8 (2,097,152-bit image), a 4:1 compression ratio will reduce the size to 524,288-bit image, which requires less storage space.

Lossless (Reversible) Image Compression. Lossless, or reversible, image compression reduces the size of the original image. The most conspicuous difference compared with irreversible compression is that there is no loss of image information content. See text for further explanation.

Lossy (Irreversible) Image Compression. Lossy, or irreversible, image compression reduces the size of the original image. The most conspicuous difference when compared with reversible compression is that there is loss of image information content. This type of compression reduces transmission time and occupies less storage space. See text for further explanation.

Steps in Lossless Image Compression. The lossless compression framework consists of 2 steps: image transformation and encoding.

Steps in Lossy Image Compression. The lossy compression framework consists of three steps: namely, image transformation, quantization, and encoding. See text for further explanation.

Compression Algorithms and Visual Clarity. The effect of JPEG and JPEG 2000 compression algorithms on picture clarity. JPEG 2000 is now used routinely in digital radiology. (Images courtesy of David Seeram.)

High-pass filter, used for sharpening images, and described previously in this article.

Blurring tools, such as those for surface blur, Gaussian blur, radial blur, and lens blur.