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Basic Concepts in Digital Image Processing

Explore how the fundamental tools of digital image processing can be utilized to manipulate, rehabilitate, edit, resize, rotate, and store images captured with an optical microscope (or other digital image recording device). The interactive tutorials linked below each consider a specific algorithm or related series of algorithms that are useful for processing digital images.

Digital Image Sampling Frequency - In order to match the optical and electronic resolution of a microscope and the accompanying camera system, a digital image should have a sufficient number of samples per horizontal line so that the display faithfully represents the original signal presented to the digitizing device. This interactive tutorial examines how variations in specimen sampling frequency affect the resolution of the final image.

Spatial Resolution - Spatial resolution is a term that refers to the number of pixels utilized in construction of a digital image. Images having higher spatial resolution are composed with a greater number of pixels than those of lower spatial resolution. This interactive tutorial explores variations in digital image spatial resolution, and how these values affect the final appearance of the image.

Gray-Level Resolution - When describing digital images, gray-level resolution is a term that refers to the number of shades of gray utilized in preparing the image for display. Digital images having higher gray-level resolution are composed with a larger number of gray shades and have a greater dynamic range than those of lower gray-level resolution.

Contrast Manipulation in Digital Images - The term contrast refers to the amount of color or grayscale differentiation that exists between various image features in both analog and digital images. Images having a higher contrast level generally display a greater degree of color or grayscale variation than those of lower contrast. This interactive tutorial investigates the wide range of adjustment that is possible in digital image contrast manipulation, and how contrast variations affect the final appearance of the image.

Contrast Stretching and Histogram Normalization - Contrast modification in digital images is a point process that involves application (addition, subtraction, multiplication, or division) of an identical constant value to every pixel in the image. This tutorial explores how redistributing brightness values through application of contrast stretching and histogram normalization algorithms can rehabilitate digital images having poor contrast.

Grayscale Image Complement - Grayscale image complement operations are useful for enhancing the visibility of subtle brightness variations among gray levels in regions of a digital image where fine details are obscured. This interactive tutorial explores the effects of grayscale complement operations on grayscale digital images and their histograms.

Image Averaging and Noise Removal - Random noise is a problem that often arises in fluorescence microscopy due to the extremely low light levels experienced with this technique, and its presence can seriously degrade the spatial resolution of a digital image. To remedy the situation, an image averaging algorithm can often be applied to digital images in order to enhance spatial resolution while sacrificing a small degree of temporal resolution.

Balancing Color in Digital Images - Color balancing belongs to a class of digital image enhancement algorithms that are useful for correcting color casts in captured images. In most cases, unusual overall color casts (or uniform discolorations) typically result from color temperature effects in specimen illumination or improper adjustment of the electronic detector (usually a digital CCD camera system) used to capture the image.

Levels Adjustment in Digital Images - The tonal range of a digital image is related to the amount of contrast present in the image, with a broad tonal range producing good contrast, and a narrow tonal range indicative of poor contrast. Levels adjustment is a digital image enhancement algorithm that can substantially improve the tonal range of a digital image, thereby increasing overall image contrast.

Output Look-Up Table Manipulation - Manipulation of the transfer function, and its corresponding look-up table (LUT), provides a flexible and powerful approach to adjusting the appearance of a digital image. Contrast and color values can be altered without modifying the original digitized image, and an adjustable curve may be utilized to interactively alter values present in the look-up table.

Line Intensity Scanning - The line intensity scan function is a graphical tool that is useful for measuring intensity and contrast along a single horizontal or vertical row of pixels in digital images. The technique is often employed to compare brightness values in related digital images, and to determine the average values over several adjacent pixel rows in a single image. This interactive tutorial explores the line intensity scan technique for measuring intensity levels in blocks of up to seven pixels in a digital image.

Background Subtraction - Application of a suitable background subtraction algorithm is a useful technique for correcting image defects that are associated with nonuniform brightness, often (but not always) attributed to uneven illumination in the microscope. This interactive tutorial explores image processing schemes utilizing either a previously recorded background image or a processing technique that relies on the creation of a background image from the original digital image.

White and Black Balance - The overall color of a digital image captured with an optical microscope is dependent not only upon the spectrum of visible light wavelengths transmitted through or reflected by the specimen, but also on the spectral content of the illuminator. In color digital camera systems that employ either charge-coupled device (CCD) or complementary metal oxide semiconductor (CMOS) image sensors, white and/or black balance (baseline) adjustment is often necessary in order to produce acceptable color quality in digital images.

Gamma Correction - The perceived brightness of a digital image captured with an optical microscope is dependent not only upon the conditions of specimen illumination, but also on the sensitivity and linearity of the detector upon which the image was acquired. In addition, the characteristics of the display device (television, computer monitor, flat-screen display) where the digital image is viewed also affect the intensity distribution and interrelationship of contrast between light and dark regions in the specimen. The effects are characterized by a variable known as gamma, which is explored in this interactive tutorial.

Filtering Digital Images

Adjustment of Digital Image Sharpness - The sharpness of a digital image refers to the degree of clarity in both coarse and fine specimen detail. A lack of sharpness in digital images captured with the microscope often results from poor focus adjustment, vibration, or the specimen not being flat with respect to the imaging plane. This common artifact can also result from a variety of optical aberrations such as spherical aberration, astigmatism, coma, geometrical distortion, and field curvature. Although many of these problems can be corrected by ensuring that the microscope and specimen are properly configured, it is often necessary to correct captured digital images that suffer from a lack of sharpness through digital image processing techniques.

Convolution Kernels - Many of the most powerful image processing algorithms rely upon a process known as convolution (or spatial convolution), which can be used to perform a wide variety of operations on digital images. Within the suite of image processing techniques available to microscopists with these algorithms are noise reduction through spatial averaging, sharpening of image details, edge detection, and image contrast enhancement. The choice of the convolution kernel is paramount in determining the nature of the convolution operation.

Convolution Kernel Mask Operation - A powerful array of image-processing technologies utilize multipixel operations with convolution kernel masks, in which each output pixel is altered by contributions from a number of adjoining input pixels. These types of operations are commonly referred to as convolution or spatial convolution. This interactive tutorial explores how a convolution operation is performed on a digital image.

Derivative Filters - Derivative filter algorithms provide a quantitative measurement for the rate of change in pixel brightness information present in a digital image. When a derivative filter is applied to a digital image, the resulting information about brightness change rates can be used to enhance contrast, detect edges and boundaries, and to measure feature orientation.

Median Filters for Digital Images - The median filter is an algorithm that is useful for the removal of impulse noise (also known as binary noise), which is manifested in a digital image by corruption of the captured image with bright and dark pixels that appear randomly throughout the spatial distribution. Impulse noise arises from spikes in the output signal that typically result from external interference or poor sensor configuration. This interactive tutorial explores the removal of impulse noise from a digital image using the median filter, and how the application of this and related filtering techniques affect the final appearance of the filtered image.

Fourier Transform Filtering Techniques - Fourier transformation belongs to a class of digital image processing algorithms that can be utilized to transform a digital image into the frequency domain. After an image is transformed and described as a series of spatial frequencies, a variety of filtering algorithms can then be easily computed and applied, followed by retransformation of the filtered image back to the spatial domain. This technique is useful for performing a variety of filtering operations that are otherwise very difficult to perform with a spatial convolution.

Unsharp Mask Filtering - Enhancing the overall sharpness of a digital image often has the effect of revealing fine details that cannot be clearly discerned in the original. The unsharp mask filter algorithm is an extremely versatile sharpening tool that improves the definition of fine detail by removing low-frequency spatial information from the original image.

Difference of Gaussians Edge Enhancement - A majority of the edge enhancement algorithms commonly employed in digital image processing often produce the unwanted side effect of increasing random noise in the image. Because it removes high-frequency spatial detail that can include random noise, the difference of Gaussians algorithm is useful for enhancing edges in noisy digital images. This interactive tutorial explores application of the difference of Gaussians algorithm to images captured in the microscope.

Spatial Averaging - There exist numerous potential sources for noise that can seriously affect the quality of digital images captured in the microscope. Noise can often be eliminated or reduced utilizing a technique known as spatial averaging, but application of these algorithms can eliminate some image detail as well. This interactive tutorial explores the benefits and consequences of spatial averaging as a method for removing noise from digital images.

Transformation of Digital Images

Geometric Transformation: Interpolation and Image Rotation - The geometric transformation of digital images is an important tool for modifying the spatial relationships between pixels in an image, and has become an essential element for the post-processing of digital images. This interactive tutorial explores the basic properties of geometric transformation, and how the algorithms involved in the mechanism of transformation can influence the final appearance as well as the information content of the transformed image.

Geometric Transformation: Pan, Scroll, Rotate, Flip, Scale, and Zoom - The microscopist must often arrange digital images on the output display device for purposes of visually comparing image details, or for illustrating differences between two or more images. Such display operations are facilitated through software packages that enable interactive panning, scrolling, flipping, scaling, and zooming of digital images. Zooming to enlarge image details is useful for the visualization of small structures present in a digital image, and scaling is often necessary to format a digital image to fit within the boundaries of a display medium, as in the case of displaying a collection of thumbnail images.

Creating and Manipulating Binary Digital Images

Binary Threshold Level Selection - When creating a binary image having only two intensity levels (black and white) from an original grayscale digital image that has 256 possible intensity values (for an 8-bit image), a binary threshold level must be chosen to designate the intensity level at which binary segregation occurs. This interactive tutorial explores the use of various algorithms utilized in the methodology for choosing a single binary threshold level.

Binary Slicing of Digital Images - Isolating specimen details from background noise in low-contrast digital images often benefits from the application of contrast enhancement methods. Binary slicing is a technique that can be utilized to create a high-contrast binary image from a low-contrast grayscale image captured in the microscope. Even in situations where a digital image has adequate contrast, binary slicing is very useful for highlighting individual specimen details.

Compression of Digital Images

Color Reduction and Image Dithering - The Graphics Interchange Format (GIF), designed for encoding and storing digital images, is currently in worldwide use, being a popular medium for presentation of images by means of Web browser software. This interactive tutorial explores the compression of digital images using GIF algorithms, and how a lossy storage mechanism affects the final appearance of the image when interpolations are made from images having more than 256 colors.

JPEG Image Compression - The JPEG lossy image compression standard is currently in worldwide use, and is becoming a critical element in the storage of digital images captured with the optical microscope. This interactive tutorial examines compression of digital images with the JPEG algorithm, and how the lossy storage mechanism affects file size and the final image appearance.

Contributing Authors

Kenneth R. Spring - Scientific Consultant, Lusby, Maryland, 20657.

John C. Russ - Materials Science and Engineering Department, North Carolina State University, Raleigh, North Carolina, 27695.

Matthew J. Parry-Hill, Thomas J. Fellers, and Michael W. Davidson - National High Magnetic Field Laboratory, 1800 East Paul Dirac Dr., The Florida State University, Tallahassee, Florida, 32310.


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