Dicom To Tiff

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Dhara Lyford

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Aug 4, 2024, 1:57:21 PM8/4/24
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Helloeveryone, I wanted to comment on a couple of problems

How can I export a serie of DICOM files to a serie of individual 16b tif files.

When I try it I get a multi-image TIFF with broken levels.


on the other hand, when I import a series of tiff 16b files and

export it as Dicom, I also get errors in the levels when I open it in another Dicom reader

Thanks in advance and excuse my english

IBL


Have you tried using the Bioformats plugin Importer to select a specific series within the image?

ImageJ Bio-FormatsImport data from many life sciences file formats, and export to several open formats.


I am also curious what the pixel size in Horos might be. By resolution, do you really mean imaging system resolution or pixel resolution? Might Horos be upsampling the image somehow? How do you export them from Horos?


you could try posting (or looking at) the dicom headers to see what they way - in particular are they multi-part images. in the header you can also see what resolution the dicom file actually says it is. You can view the images in image->show info.


I'm new to Python so forgive my ignorance If I don't have all the info correct. I'm trying raster through a directory and convert all the DICOM files within to TIFF files. I have gotten the search functionality to work, but I am having a hard time saving the images as TIFFs. I'm using the pydicom libraries to read in the DICOM and manipulate the header information. Also, I have tried using the save_as function in pydicom to save to TIFF, but I would rather use the save function in PIL to properly set the compression of the TIFF. I think the problem is that I can't/don't understand how to extract the actual image data from a DICOM and place it in a new image.Any Help would be greatly appreciated ... Cheers


DICOMscope is a free DICOM viewer which can display uncompressed, monochrome DICOM images from all modalities and which supports monitor calibration according to DICOM part 14 as well as presentation states. DICOMscope offers a print client (DICOM Basic Grayscale Print Management) which also implements the optional Presentation LUT SOP Class. The development of this prototype was commissioned by the "Committee for the Advancement of DICOM" and demonstrated at the European Congress of Radiology ECR 1999. An enhanced version was developed for the "DICOM Display Consistency Demonstration" at RSNA InfoRAD 1999. The current release 3.5.1 has been demonstrated at ECR 2001 and contains numerous extensions, including a print server, support for encrypted DICOM communication, digital signatures and structured reporting.


Aeskulap is a medical image viewer. It is able to load a series of special images stored in the DICOM format for review. Additionally Aeskulap is able to query and fetch DICOM images from archive nodes (also called PACS) over the network. The goal of this project is to create a full open source replacement for commercially available DICOM viewers. Aeskulap is based on gtkmm, glademm and gconfmm and designed to run under Linux. Ports of these packages are available for different platforms. It should be quite easy to port Aeskulap to any platform were these packages are available.


Granted, this may not be useful as other dicom viewers (answered by others here) have different tools available for examining or measuring things on the images. If you only want to open and look at the raw photos - eg, of a personal exam - then a simple batch convert may work for you:


InVesalius generates 3D medical imaging reconstructions based on a sequence of 2D DICOM files acquired with CT or MRI equipments. InVesalius is internationalized (currently available in English, Portuguese, French, German, Spanish, Catalan, Romanian, Korean, Italian and Czech), multi-platform (GNU Linux, Windows and MacOS) and provides several tools:


I am currently in the process of this huge gif project, so I've been using "Load Files Into Stack..." but I heard that using "Loading Multiple DICOM Files..." may be faster. The issue is that each time I try to open a folder of .png files I get the error: "Sorry, but the designated folder does not contain any DICOM files. Please choose another folder." I've even tried this with .tiff files but I get the same error message. I have the most recent update of CS6, so there really shouldn't be any issues there.


I looked on that page before asking here. It says that the script should be able to open .png, .tiff, and others. Plenty of other people have been able to use this function for the same reason I wish to. (I.E. here)


I work with volumetric tiff microscopy images coming from the biomedical domain (one such tiff image captured at a nanometer resolution with an electron microscope is re-saved as an animated gif just for context - here, we scroll through the depth dimension)




X1_019.tif (1.7 MB)

I was wondering if it would be possible to import such tiff files and show them in the browser, or would I need to re-save these images in a different data format to use the Dataloader class. I had an idea for a visualization similar to this in mind.

Any tips are welcome. Thank you!


Thank you @Mugen87 for the suggestion on extracting data from the tif file. I decided to save the tif file as a nrrd and use the NRRDLoader which you also recommended.

Unfortunately the code I am trying right now reveals no significant error, but I also see no output in the browser.

Would you have any insight on what I am doing wrong here? I only started recently with three.js, so any tips are appreciated. Thank you very much!


Born from a collaboration with Emory University, HistomicsTK has evolved into a powerful, user-friendly web-based platform for digital pathology data. It played a vital role in accessing and interpreting the data from The Cancer Genome Atlas (TCGA), a comprehensive public resource encompassing a diverse range of cancers.


Built on the powerful open source data management platform Girder, HistomicsTK revolutionizes the world of digital pathology. By providing access to data via web APIs, user/group authentication, and fine-grained data access control, it ensures streamlined data handling, reliable security, and granular control over data visibility. These attributes contribute to more efficient operations and enhanced data protection, critical elements in an increasingly data-driven field.


The Large Image module, an integral part of HistomicsTK, offers visualization of large, multi-resolution images overlaid with annotations. Compatible with numerous Whole Slide Imaging (WSI) file formats means that it can process outputs from multiple whole slides and digital pathology scanners. It also provides a Python API for managing these images, including tiled access, metadata extraction, and a range of annotations.


Tile Sources: Our Tile Sources continue to improve, notably for multiple formats such as DICOM, TIFF, and Rasterio. DICOM whole slide images are gaining traction in the pathology community. Rasterio is mostly used for geospatial sources but allows reading some common formats on a wider variety of operating systems than our other tile sources. TIFF files come in a broad range of varieties. Although we already utilize libtiff, openslide, and have an ome tiff specific variant that employs libtiff, by using the tifffile module as an additional source for reading TIFFs, we can do a more effective job in reading certain multi-region tiff images. One key example is the Leica SCN format, which can store multiple tissue regions as separate images.


API: Enhancements include a canReadList function, improved class and output representation (python repr, see Figure 1), and properties like frame, dtype, and bandCount. In addition to these, the API has improved getPixel and histograms functions.


Other noteworthy additions include an inline YAML/JSON editor for straightforward data modification and metadata/annotation metadata search modes for ease of data discovery. HistomicsTK also supports ICC Color Profile for better color accuracy in images, which makes it feasible to have consistent results from algorithms and boasts a refined frame selector/histogram range (see Figure 3) for a more user-friendly experience. These updates significantly streamline operations in digital pathology.


Together, these advancements make significant strides forward in digital pathology, offering users greater flexibility and efficiency in managing large volumes of histopathology data. The ongoing evolution of HistomicsTK epitomizes the relentless drive for innovation, striving to provide the most effective solutions to meet the changing needs of the histopathology community.


As the digital pathology industry continues to expand, HistomicsTK is committed to remaining at the forefront, constantly adapting and enhancing its capabilities to meet and exceed the demands of the field. To our valued commercial customers and partners, we look forward to embracing the future of histopathology together with HistomicsTK.


You can download HistomicsTK for free to access the new features of the Large Image module. For guidance and advanced support, you can reach out to our team of experts at Kitware. We can help customize the software according to your specific needs and project requirements, enabling you to leverage HistomicsTK effectively. Contact us for more information.


The research reported in this publication was supported by the National Cancer Institute of the National Institutes of Health under Award Number U24CA194362. The content is solely the responsibility of the authors and does not necessarily represent the official views of the National Institutes of Health.


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