Ординатура / Офтальмология / Английские материалы / Scanning Laser Imaging of the Retina Basic Concepts and Clinical Applications_Theelen_2011
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19.Babcock HW. The possibility of compensating astronomical seeing. Pub Astr Soc Pac 1953;229-36.
20.Miller DT, Williams DR, Morris GM, Liang J. Images of cone photoreceptors in the living human eye. Vision Res 1996;36:1067-79.
21.Roorda A, Romero-Borja F, Donnelly IW, et al. Adaptive optics scanning laser ophthalmoscopy. Opt Express 2002;10:405-12.
22.Romero-Borja F, Venkateswaran K, Roorda A, Hebert T. Optical slicing of human retinal tissue in vivo with the adaptive optics scanning laser ophthalmoscope. Appl Opt 2005;44:4032-40.
23.Pallikaris A, Williams DR, Hofer H. The reflectance of single cones in the living human eye. Invest Ophthalmol Vis Sci 2003;44:4580-92.
24.Hofer H, Carroll J, Neitz J, et al. Organization of the human trichromatic cone mosaic. J Neurosci 2005;25:9669-79.
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CHAPTER 3
Digital Processing of Retinal Images
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Digital processing of retinal images 35
THE DIGITAL IMAGE
About 50 years have passed since the first successful image digitization took place.1 The first routine use of digital images in ophthalmology was reported about 20 years later.2 Those images still suffered from relatively low resolution and the quality of digital fundus images stayed inferior to analog pictures for a long time. Current ophthalmic digital images have on the whole replaced analog images due to a huge increase in camera quality, image resolution and last not least computer technology.
Digital imaging makes image storage and distribution much easier and facilitates the performance of computerized image analysis. Most digital images are based on raster image types, which are matrices of brightness values visualized by an array of colors or gray tones. Such an image matrix has a certain number of pixels (picture elements), each of which has its own value (Fig. 3.1). When the correct arrangement of pixels is displayed on a computer monitor an image is seen.
Fig. 3.1
Sample of a 9×9 pixel image. The image matrix can be displayed in color/gray shades (left) or brightness values (right). Values are used for image analysis, while shades are needed to visualize the image.
Pixels represent image values within a two-dimensional map. If several matrices are layered in a tomographic image, three-dimensional information is provided. The corresponding pixels are then combined to volume elements (voxels). Both pixels and voxels represent tone values at a certain point. To attain perfect resolution, an infinite number of pixels would be needed. Therefore, the spaces between limited numbers of pixels are filled using reconstruction filters to allow image formation from those discrete entities so that pixels appear as rectangles and voxels as cuboids.3
IMAGE RESOLUTION
The resolution provides the authenticity of an image concerning the representation of the original scene. This is dependent on the optics and aberrations of the imaging system and eye (optical resolution) as well as on the size of the matrix used to digitize the image. Increasing the number of pixels in a matrix enhances the digital resolution that can be achieved. It should be realized that the maximum optical resolution limits the visualization of details within an ophthalmic image, and unnecessary increase of the pixel number can lead to artifacts by banding, i.e. abrupt changes within one shade without additional image information.4 In image analysis it is important to know the particular spatial resolution (pixel size). To compare measurements within two different images, the size has to be calibrated by dividing a known distance by the number of pixels. This allows fundus structures in different images to be compared largely independent of magnification errors by camera optics and refraction of
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the eye. For example, if an image is normalized to 500 pixels for the distance between fovea and the temporal border of the optic nerve head (usually 3000 µm), the resolution will be 6 µm/pixel.5
GRAYSCALE VERSUS COLOR SCALE
Every pixel carries specific brightness information varying from black (minimum) to white (maximum). If different wavelengths are discriminated, a color image can be received and the information is split into three particular wavelengths, red, green and blue. These images are therefore called RGB images. If differentiation into wavelength is not possible or not necessary, a grayscale image is created. Then, only general brightness values are received and split into a number of gray tones.
The brightness resolution depends on the number of brightness values allowed in an image. The most commonly used value is 8 bits per channel. A bit is a variable that can only have two possible values. As a result, an 8-bit gray value image can have 256 (or 28) brightness values. Most RGB images have 3 color channels with 8 bits each (24-bit color image), which allows for 16,777,216 color variations. This color depth is often called True Color mode.
Figure 3.2 illustrates the difference in brightness information between a 3-bit and an 8-bit gray scale image. In Figure 3.3, a color fundus photograph (24-bit) is split into its RGB channels to demonstrate the differences in brightness information between the color channels, which results it disparate fundus structure information per channel.
Fig. 3.2
Gray scale images of a continuous slope from black to white, displayed in 3-bit (top) and 8-bit (bottom) mode. Less gray values reduce brightness information and cause banding.
Fig. 3.3 ◙
24-bit color image of a patient with cone-rod dystrophy (A). The image consists of the blue (B), green (C) and red (D) channel. Note that the optic nerve head is best visualized in blue, macular pathology is best visible in green and the red channel gives an impression of melanin distribution.
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SPECIAL IMAGE SCALES
Standard image data scales are not sufficient to show the results of arithmetical image processing. Instead special formats to illustrate continuous, positive and negative pixel values and floating points are necessary. Appropriate scaled images are 32-bit images; however, these cannot be read from general viewing and office software. If 32-bit images results are to be further processed for presentation, conversion to 8-bit is useful. It must be borne in mind that transformation from 32-bit to other formats will destroy any useful numerical information of the image and replace it with some artificial pixel values. Therefore, all image analysis has to be completed on the 32-bit image before conversion.
IMAGE STORAGE
The saving of image data is an important issue in digital image processing, as the data format used is essential for the amount of image information stored. Images can be stored compressed or uncompressed. Compression does not necessarily mean loss of data, but compression helps to save volume on a data storage device like a CD-ROM. The compression method is responsible if data are saved undestroyed (lossless) or if information is lost (lossy formats). If lossy formats are used, compression-related artifacts can occur and images may not be used for most analysis purposes any more.
IMAGE FORMATS
A digital image is a computer file that contains data on pixel location, number and brightness. Furthermore, information about image type (number of bits), color and in some cases, technical data of the imaging device, patient and time of recording must be stored. There is currently a wealth of image file types and only the most common files in ophthalmic imaging can be presented here.
TAGGED IMAGE FILE FORMAT (TIFF)
TIFF is the most frequently used lossless format because it is very flexible. It offers the possibility of uncompressed data storage, for Lempel–Ziv–Welch (LZW) lossless data compression but it may also be used as a container for lossy formats of the Joint Photographic Experts Group (JPEG). For scientific imaging TIFF is particularly interesting, as it can handle extended data formats like 32-bit images that contain floating rather than binary information as well as image sequences. TIFF files are preferred by publishers as the format also preserves print color options like CMYK color model: http://partners.adobe.com/public/ developer/tiff/index.html (assessed February 26, 2010)
BITMAP FILE FORMAT (BMP)
Bitmaps are very simple file formats that can optionally be stored as text files and can be read and created by a text editor. A RAW mode exists to save pixel values as binary data. The maximum data depth is 32 bits, but in contrast to other formats image metadata or color manipulation are not supported. Bitmaps cannot significantly be compressed and are therefore very large. This format is particularly useful for value-based image analysis.
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JOINT PHOTOGRAPHIC EXPERTS GROUP (JPEG)
JPEG is currently the most widespread image data format for lossy compression data storage.6 The degree of compression can be adjusted to allow for a good compromise between file size and image quality. Although some JPEG compression may be acceptable for images that do not need to offer much clinical detail, this format is not suitable for scientific image processing or high-quality image reading due to loss of fine image elements and artifact
formation (Fig. 3.4).
Fig. 3.4
Effect of image compression on file size and data content. The original image (A) is TIFF, uncompressed, 75 kilobytes; the LZW compressed TIFF image (B) is 22 kilobytes; the JPEG image (C) is 21 kilobytes. Note the artifacts in (C) with only minor advantage of file size reduction according to (B).
IMAGE ANALYSIS SOFTWARE
Special image analysis software is needed to gain access to pixel information of an image, measure pixel dimensions and compare two images with each other. This software is different from image-editing software like Adobe Photoshop® or the GNU Image Manipulation Program (GIMP, www.gimp.org), which are generally used for photo editing inclusive labeling and scaling before publication.
A large variety of proprietary or free programs for medical image analysis are available (http://en.wikipedia.org/wiki/List_of_image_analysis_software; assessed March 1, 2010). Most programs are specifically designed for radiology or microscopy purposes. Therefore, only a few programs may be suitable for ophthalmic image analysis.
The public domain program ImageJ (http://rsb.info.nih.gov/ij/, official version inclusive user manual also available on the CD added to this book) is increasingly used for retinal image analysis.7-10 ImageJ was developed at the National Institute of Health and first released in 1997. Its open source architecture allows for quick bug fixing and the development of individual plug-ins for specific image analysis steps. This makes the software flexible and adjustable for use in ophthalmic imaging.
IMAGEJ IN OPHTHALMIC IMAGE PROCESSING
ImageJ is platform-independent, image-processing software based on Java programming language (Sun Microsystems, Santa Clara, CA, USA). It can run on virtually every computer system with a Java runtime environment (JRE). The JRE is freely available and can be downloaded either separately from Sun Microsystems (http://java.sun.com/) or bundled with ImageJ (http://rsbweb.nih.gov/ij/download.html). A large bundle of plug-ins may be downloaded. Once saved in the plug-ins folder of ImageJ, these plug-ins can be accessed via the program menu like any other item. Major advantages of ImageJ are its support of many image formats, and that not only image data but also metadata of an image are imported. After
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an image has been opened, image type and image data can be changed and adjusted for image analysis purposes.
All image analysis performed to receive the imaging results in this thesis was limited to gray value images. Therefore, this introduction is limited to the processing of ophthalmic grayvalue images with ImageJ as color fundus image analysis goes beyond the scope of this thesis.
Once it has been installed, ImageJ the is started by clicking ImageJ.exe and a small window appears including a drop-down menu and a number of buttons (Fig. 3.5). All program functions can be accessed via the menus, but some specified functions may quickly be effected by the buttons. The Help menu provides online help and access to updates and new plug-ins.
Fig. 3.5
ImageJ main window with drop-down menus and quick access buttons for macros and special functions.
OPENING IMAGES AND MOVIE FILES IN IMAGEJ
An image may be opened via a standard navigation window by using File | Open. The image will then be displayed in its own window, and information about type and file size is displayed at the top. By moving the mouse pointer over the image, the individual pixel values are displayed together with the x and y coordinates at that individual point. With this, a first easy image analysis is possible (Fig. 3.6).
Fig. 3.6
A FAF image opened with ImageJ. The image window provides information on name, file type, image size in pixels, depth of gray-shades and file size (encircled). The main window provides the pixel value at the mouse pointer position (black arrow).
Importing movie files can be important for early phase of angiographies or tomographic images. Movies are series of single images illustrating either a dynamic process or a scan
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through a large data set. Movies van be opened in Audio Video Interleave (AVI) format and are imported by File | Import | AVI. A series of single images can also be stored as one single AVI file by File | Save as | AVI if it is opened in a single window (image stack).
SINGLE IMAGE ANALYSIS
Digital images include a huge amount of information, some of which is obvious at first sight and some of which is obscured. A lot of information can be received and analyzed by human observers. However, computerized image analysis can be quicker and may result in a more precise analysis.5 All information of an image can be read from the whole image or a selected part. For selection of a region of interest (ROI), a number of tools are directly available via the buttons on the ImageJ menu bar (Fig. 3.5), including rectangular, elliptical, line and point selections.
IMAGE HISTOGRAM
The histogram illustrates the distribution of pixel values within an image and can be used as a quality measure of fundus image performance. In addition, histograms can be helpful to decide on image segmentation and image similarity. The best image contrast and detailed information is provided by an image with a well-centered and broadly distributed histogram that fills in all existing pixel values. High-contrast images may show two large peaks with few values in between.11 By significant underor overexposure the histogram is shifted to the left or the right, respectively, and the profile of the histogram may be reduced (Fig. 3.7). However, the shape and distribution of the histogram of an image may be influenced by typical changes within the scene of a (fundus) image.6 The image histogram can easily be accessed by the ImageJ menu Analyze | Histogram.
Fig. 3.7
Histograms of consecutive FAF images of one patient performed at one day. A well balanced histogram (A), underexposure (B) and overexposure (C) are displayed. Gray bars indicate quality margins for histogram gray values, which should include the black fractions (mean ± standard deviation) of the gray value pixel counts of the individual histograms.