“ Lossy compression” means that in the process of file size reduction certain amount of image information is discarded. There are also loss-less JPEG modes, but these in general are not widely implemented and chances are that most of the images are of the lossy type. This format is commonly used in web pages and supported by the vast majority of digital photographic cameras and image scanners because it can store images in relatively small files at the expense of image quality. JPEG stands for Joint Photographic Experts Group who were the creators of a commonly used method of lossy compression for photographic images. Why (lossy) JPEGs should not be used in imaging Pixels are not little squaresĪnd voxels are not cubes! See the whitepaper by Alvy Ray Smith. This is an essential point to start with for understanding this topic. These numbers represent how light or some other type of signal entered the instrument we are using and interacted with its environment and eventually triggered sensors in the instrument that further processed the information into a digital output.įor example, when you record an image using a light gathering device such as a confocal microscope, the values you get at a certain coordinate or pixel are not color values, but relate to photon counts. It is therefore very important to keep in mind that the pixel values in digital images are numbers, not subjective experiences of color. Our brains process and filter information, so that we perceive visually only after we 1) “see” light information that enters our eyes and interacts with the environment of the eye and eventually individual cells in our retinas, and 2) process the signal in the context of our brains. Human visual perception is very good at certain tasks, such as contrast correction and detecting subtle differences in bright colors, but notoriously bad with other things, such as discerning dark colors, or classifying colors without appropriate reference. In the context of science, digital images are samples of information, sampled at vertex points of n-dimensional grids. In scientific image processing and image analysis, an image is something different than a regular digital photograph of a beautiful scene you shot during your latest vacation. In certain cases (Examples …) it is very helpful to add markers to the slides. The following examples give an overview over the principles of creating good naming schemes: Naming schemesĮffective naming schemes are easy to read by both humans and computers. It may be possible to mitigate these difficulties by preparing the environment somehow-e.g., staining cell membranes with a fluorescent dye. But they are harder to analyze and measure, since many algorithms have difficulty distinguishing between objects. In many cases, it is not possible to avoid objects which overlap. If you must tolerate an uneven illumination for some reason, try to acquire a background image so that you can use a background subtraction-but there may still be issues such as reflection artifacts. You should ensure that this illumination is as evenly distributed as possible, rather than attempting to correct for it after acquisition. Many forms of imaging require some form of illumination. See Why (lossy) JPEGs should not be used in imaging below for details. Do not store raw image data in file formats such as JPEG which use lossy compression. Original data should be saved in a way that preserves the exact sample values. Furthermore, if your objects of interest are described by too few pixels, the error of many statistical computations will be prohibitively high, and some forms of analyses will not be possible at all. Spatial resolution can always be downsampled after the fact-but never upsampled. As a rule of thumb, more samples is better. Digital detectors such as cameras and PMTs can produce sample matrices ranging from 256 x 256 pixels or fewer, up to 128 megapixels or more. Spatial resolution refers to the number (or density, if you prefer) of samples in the image. The goal of this section is to collect information on image acquisition principles that ease the automation of image analysis. Not all data is created equal and thus the analysis of certain images can be easily automated, while others pose a bigger challenge. The page is a collection of principles for the entire image analysis process, from acquisition to processing to analysis.
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