The NanoZoomer is a high-speed, high-resolution digital slide scanner. It converts glass slides into digital or virtual slides using the latest technology in virtual microscopy. These high-resolution virtual slides are very large and require a large amount of memory (2 GB or recommended) and storage space.
Supported NanoZoomer file types:
HCImage can handle NanoZoomer image files in several ways depending upon the size of the file and the type of analysis the user wants to perform. The various options are listed below, followed by a more in-depth description.
NanoZoomer NDP images and VMU files are very large and can be difficult to work with due to their size. Nano-Extract lets user create a set of parameters to identify the objects of interest using a macro (or script). Using the specified parameters, the macro will extract and organize the data into a manageable format for analysis. The macro may be saved for repeated use on other files.
| Icon | Name | Description |
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Run Macro | Click to run a newly created or previously saved macro. |
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Load Macro | Use Load Macro to browse for and load a previously saved macro. |
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Macro Editor | The Macro Editor lets users create a new macro, load an existing macro, and edit and save new and existing macros. |
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Enhance | Enhance is used to optimize images by removing defects and imperfections that make object identification very difficult. Use a combination of enhance operations such as Convolutions In manipulating an image with a digital image processor, the substitution of the grey value of each pixel with another grey value that takes into account the values of neighboring pixels. The convolution mask or kernel used to calculate the influence of the neighbors determines the degree to which the picture is sharpened or smoothed by the convolution process. This contrasts with a point operation, where the grey value of each pixel is transformed without considering the neighbors.
A group of pixels called a kernel is compared against other pixels and the mathmatical result is placed in the center of the kernel., Edge OperatorsEnhances object boundaries for detection and measurements. and Image ArithmeticArithmetically combine the current image with the saved image. when dealing with complex image structures. For more information on image enhancements see "Enhance Overview." |
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Identify | The Identify dialog box controls the way that a binary image is created for later editing, modification and measurement. This is done using a technique called ThresholdingThe maximum and minimum values for a range of grayscale intensities, to be used for segmentation of an image. (See also Segmentation) or Segmentation. The binary image is a map of all detected pixels that may later be combined together into objects for measurement. For information on object identification see "Identify Overview." |
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Modify | Once the binary image has been created, use Modify (short for Binary Image Modification) to improve object size, shape and connectivity or to combine with other binary images. The goal is to improve the binary image for better object measurements. Create a combination of operations, edit or remove, save and load binary layers from memory and disk using the Modify dialog. For more information about modifying the binary image layer see "Modify Overview." |
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Qualify | Qualify allows another level of refinement and provides the means to filter selected objects from the binary image for inclusion in a measured dataset. Qualify compares object populations against one or more qualification criteria as a type of filtering system to reject various objects based on size, shape, intensity or position. For more information about removing objects based on physical characteristics see "Qualify Overview." |
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Binary Editor | Sometimes there is a need to manually edit the binary image. The Binary Editor is a great tool that provides precision, accuracy and a variety of tools for editing objects. For more information about editing binary images see "The Binary Editor (Draw/Erase)." |
The Split option will take the original NanoZoomer image file and divide it equally into smaller sub-images saved as a data document. The size of the NanoZoomer is very large and even when it is split into smaller sub-images, these images are going to be large as well. For instance, a 151552 x 90103 pixel, 1.09 GB, NDP image file, would be split into over a thousand 4789 x 2816 pixel sub-images. The size of the new data document is over 67 GB.
Open will attempt to load the image NanoZoomer image file as a single image document. Because of the memory requirements, this option will not work with large files.