Microscope image processing - Revision history

Citation bot: Misc citation tidying. | Use this bot. Report bugs. | Suggested by Abductive | Category:Image processing | #UCB_Category 191/260

2024-11-18T13:26:43Z

Misc citation tidying. | Use this bot. Report bugs. | Suggested by Abductive | Category:Image processing | #UCB_Category 191/260

← Previous revision		Revision as of 13:26, 18 November 2024
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	==References==		==References==
	{{cite book \|last=Russ \|first=John C. \|title=The Image Processing Handbook \|~~origyear~~=1992 \|edition=5th \|date=2006-12-19 \|publisher=CRC Press \|isbn=0-8493-7254-2}}		{{cite book \|last=Russ \|first=John C. \|title=The Image Processing Handbook \|orig-date=1992 \|edition=5th \|date=2006-12-19 \|publisher=CRC Press \|isbn=0-8493-7254-2}}
	* Jan-Mark Geusebroek, Color and Geometrical Structure in Images, Applications in Microscopy, {{ISBN\|90-5776-057-6}}		* Jan-Mark Geusebroek, Color and Geometrical Structure in Images, Applications in Microscopy, {{ISBN\|90-5776-057-6}}
	* Young Ian T., Not just pretty pictures: Digital quantitative microscopy, Proc. Royal Microscopical Society, 1996, 31(4), pp. 311–313.		* Young Ian T., Not just pretty pictures: Digital quantitative microscopy, Proc. Royal Microscopical Society, 1996, 31(4), pp. 311–313.

AnomieBOT: Dating maintenance tags: {{Unreferenced section}} {{No sources section}}

2024-11-04T23:24:45Z

Dating maintenance tags: {{Unreferenced section}} {{No sources section}}

← Previous revision		Revision as of 23:24, 4 November 2024
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	==Image acquisition==		==Image acquisition==
	{{no sources section}}		{{no sources section\|date=November 2024}}

	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 frame rate (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.		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 frame rate (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.
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	==2D image techniques==		==2D image techniques==
	{{unreferenced section}}		{{unreferenced section\|date=November 2024}}
	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 non-uniformities. Such processing involves only basic arithmetic operations between images (i.e. addition, subtraction, multiplication and division). The vast majority of processing done on microscope image is of this nature.		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 non-uniformities. Such processing involves only basic arithmetic operations between images (i.e. addition, subtraction, multiplication and division). The vast majority of processing done on microscope image is of this nature.

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	==3D image techniques==		==3D image techniques==
	{{no sources section}}		{{no sources section\|date=November 2024}}
	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 equipment like [[Confocal microscopy\|confocal microscopes]]. Software is then able to reconstruct a 3D model of the original sample which may be manipulated appropriately. The processing turns a 2D 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.		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 equipment like [[Confocal microscopy\|confocal microscopes]]. Software is then able to reconstruct a 3D model of the original sample which may be manipulated appropriately. The processing turns a 2D 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==		==Analysis==
	{{no sources section}}		{{no sources section\|date=November 2024}}

	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 common 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.		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 common 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.

Plasticwonder: /* Analysis */

2024-11-04T19:34:04Z

Analysis

← Previous revision		Revision as of 19:34, 4 November 2024
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	==Analysis==		==Analysis==
			{{no sources section}}

	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 common 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.		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 common 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.

Plasticwonder: /* Image acquisition */ tag

2024-11-04T19:32:36Z

Image acquisition: tag

← Previous revision		Revision as of 19:32, 4 November 2024
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	==Image acquisition==		==Image acquisition==
			{{no sources section}}

	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 frame rate (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.		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 frame rate (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.

Plasticwonder: /* 3D image techniques */ {{no sources section}} tag

2024-11-04T19:32:10Z

3D image techniques: {{no sources section}} tag

← Previous revision		Revision as of 19:32, 4 November 2024
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	==3D image techniques==		==3D image techniques==
			{{no sources section}}

	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 equipment like [[Confocal microscopy\|confocal microscopes]]. Software is then able to reconstruct a 3D model of the original sample which may be manipulated appropriately. The processing turns a 2D 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.		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 equipment like [[Confocal microscopy\|confocal microscopes]]. Software is then able to reconstruct a 3D model of the original sample which may be manipulated appropriately. The processing turns a 2D 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.

Plasticwonder: /* 2D image techniques */ {{unreferenced section}}

2024-11-04T19:31:46Z

2D image techniques: {{unreferenced section}}

← Previous revision		Revision as of 19:31, 4 November 2024
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	==2D image techniques==		==2D image techniques==
			{{unreferenced section}}

	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 non-uniformities. Such processing involves only basic arithmetic operations between images (i.e. addition, subtraction, multiplication and division). The vast majority of processing done on microscope image is of this nature.		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 non-uniformities. Such processing involves only basic arithmetic operations between images (i.e. addition, subtraction, multiplication and division). The vast majority of processing done on microscope image is of this nature.

Plasticwonder: MOS:Instruct

2024-11-04T19:31:10Z

MOS:Instruct

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	Higher speed acquisition allows dynamic processes to be observed in real time, or stored for later playback and analysis. Combined with the 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.		Higher speed acquisition allows dynamic processes to be observed in real time, or stored for later playback and analysis. Combined with the 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 well depth decreases, reducing the number of electrons that can be stored. In turn, this results in a poorer [[signal-to-noise ratio]].		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 well depth decreases, reducing the number of electrons that can be stored. In turn, this results in a 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 to 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]].		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 to 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]].

Kvng: Adding short description: "Use of digital image processing to process images obtained from a microscope"

2024-10-06T03:45:28Z

Adding short description: "Use of digital image processing to process images obtained from a microscope"

← Previous revision		Revision as of 03:45, 6 October 2024
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			{{Short description\|Use of digital image processing to process images obtained from a microscope}}
	{{Multiple issues\|		{{Multiple issues\|
	{{essay\|date=March 2009}}		{{essay\|date=March 2009}}

Fehufanga: Reverted edits by 143.44.193.140 (talk) to last version by Kvng: nonconstructive edits

2024-09-09T11:28:03Z

Reverted edits by 143.44.193.140 (talk) to last version by Kvng: nonconstructive edits

← Previous revision		Revision as of 11:28, 9 September 2024
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	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 common 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.		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 common 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.

			==See also==
	Nono
			* [[Image processing]]

	==References==		==References==

143.44.193.140 at 11:27, 9 September 2024

2024-09-09T11:27:48Z

← Previous revision		Revision as of 11:27, 9 September 2024
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	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 common 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.		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 common 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.

			Nono
	==See also==
	* [[Image processing]]

	==References==		==References==

← Previous revision		Revision as of 03:45, 6 October 2024
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			{{Short description\|Use of digital image processing to process images obtained from a microscope}}
	{{Multiple issues\|		{{Multiple issues\|
	{{essay\|date=March 2009}}		{{essay\|date=March 2009}}