What is microscope image processing

microscope image processing

microscope image processing is a broad term that covers the use of digital image processing techniques to process analyze and present images obtained from a microscope such processing is now commonplace in a number of diverse fields such as medicine biological research cancer research drug testing metallurgy etc a number of manufacturers of microscopes now specifically design and features that allow the microscopes to interface to an image processing system until the early 1990s most image acquisition in video microscopy applications was typically done with an analog video camera often simply closed circuit TV cameras while this required the use of a frame grabber to digitize the images video cameras provided images at full video framerate 25 30 frames per second allowing live video recording and processing while the advent of solid-state detectors yielded several advantages the real-time video camera was actually superior in many respects today acquisition is usually done using a CCD camera mounted in the optical path of the microscope the camera may be full color or monochrome very often very high resolution cameras are employed to gain 

as much direct information as possible cryogenic cooling is also common to minimize noise often digital cameras used for this application provide pixel intensity data to a resolution of 12 to 16 bits much higher than is used in consumer imaging products ironically in recent years much effort has been put into acquiring data at video rates or higher 25 to 30 frames per second higher what was once easy with off-the-shelf video cameras now requires special high-speed electronics to handle 

microscope image processing

the vast digital data bandwidth higher speed acquisition allows dynamic processes to be observed in real time or stored for later playback and analysis combined with a high image resolution this approach can generate vast quantities of raw data which can be a challenge to deal with even with a modern computer system it should be observed that while current CCD detectors allow very high image resolution often this involves a trade-off because for a given chip size as the pixel count increases the pixel size decreases as the pixels get smaller their weld depth decreases reducing the number of electrons that can be stored in turn this results in the poorer signal-to-noise ratio for best results one must select an appropriate sensor for a given application because microscope images have an intrinsic limiting resolution it often makes little sense we use a noisy high resolution detector for image acquisition a more modest detector with larger pixels can often produce much higher quality images because of reduced noise this is especially important in low-light applications such as fluorescence microscopy moreover one must also consider the temporal resolution requirements of the application a lower resolution detector will often have a significantly higher acquisition rate permitting the observation of faster events conversely if the observed object is motionless one may wish to acquire images at the highest possible spatial resolution without regard to the time required to acquire a single image image processing for microscopy application begins with fundamental techniques intended to most accurately reproduce the information contained in the microscopic sample this might include adjusting the brightness and contrast of the image averaging images to reduce image noise and correcting for illumination 

microscope image processing

non-uniformities such processing involves only basic arithmetic operations between images that is addition subtraction multiplication and division the vast majority of processing done on microscope images of this nature another class of common 2d operations called image convolution are often used to reduce our enhance image details such blurring and sharpening algorithms in most programs work by altering a pixels value based on a weighted sum of that in the surrounding pixels a more detailed description of kernel-based convolution deserves an entry for itself or by altering the frequency domain function of the image using Fourier transform most image processing techniques are performed in the frequency domain other basic two-dimensional techniques include operations such as image rotation warping color balancing etc at times advanced techniques are employed with the goal of undoing the distortion of the optical path of the microscope thus eliminating distortions and blurring caused by the instrumentation this process is called deconvolution and a variety of algorithms have been developed some of great mathematical complexity the end result is an image far sharper and clearer than could be obtained in the optical domain alone this is typically a three dimensional operation that analyzes a volumetric image that is images taken at a variety of focal planes through the sample and uses this data to reconstruct a more 

microscope image processing

accurate three-dimensional image another common requirement is to take a series of images at a fixed position but at different focal depths since most microscopic samples are essentially transparent and the depth of field of the focused sample is exceptionally narrow it is possible to capture images through a three-dimensional object using 2d lykan focal microscopes software is unable to reconstruct a 3d model of the original sample which may be manipulated appropriately the processing turns it to the instrument into a 3d instrument which would not otherwise exist in recent times this technique has led to a number of scientific discoveries in cell biology analysis of images will vary considerably according to application typical analysis includes determining where the edges of an object are counting similar objects calculating the area perimeter length and other useful measurements of each object a commo approach is to create an image mask which only includes pixels that match certain criteria then perform simpler scanning operations on the resulting mask it is also possible to label objects and track their motion over a series of frames in a video sequence..