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gradient-decode-dicom
Advanced tools
Gradient Decode DICOM is a dicom image and tag reader op for TensorFlow
Decode DICOM contains two Tensorflow Operations that allow reading information from DICOM files.
loads a dicom image file and returns its pixel information in the specified output format.
loads a dicom image file and returns a sting tensor with the values of each of the supplied tags.
* Description: byte string with the file bytes. Use tf.io.read_file to read the file contents into a string.
* Type: string
* Description: Type for the output tensor
* Type: Dtype
* Possible Values: uint8, uint16, uint32, uint64, float, float16, double
* Default Value: uint16
* Description: Whether or not to include the color_dimension. If decoded image is monochrome and this attribute is True the output tensor will have an extra singleton dimension for the color.
* Type: Bool
* Default Value: True
* Description: This attribute establishes the behavior in case an error occurs on opening the image or if the output type cannot accomodate all the possible input values. *'strict'* throws an error, *'skip'* returns 0 and *'lossy'* continues with the operation as if no error occurred.
* Possible Values: 'strict', 'skip', 'lossy'
* Default Value: 'skip'
* Description: This attribute establishes what to do with the scale of the input values. *'auto'* will autoscale the input values, if the output type is integer, *'auto'* will use the maximum output scale, if the output is float, *'auto'* will scale to [0,1]. *'preserve'* keeps the values as they are, an input value greater than the maximum possible output will be clipped.
* Possible Values: 'auto', 'preserve'
* Default Value: 'preserve'
* Description: Pixel data of the DICOM image
* Type: Tensor of selected type (see attribute dtype)
* Dimensions: [F, I, J, C]
* F: Number of frames
* I: Number of rows
* J: Number of columns
* C: Color dimensions
pip install gradient-decode-dicom
FAQs
Gradient Decode DICOM is a dicom image and tag reader op for TensorFlow
We found that gradient-decode-dicom demonstrated a healthy version release cadence and project activity because the last version was released less than a year ago. It has 1 open source maintainer collaborating on the project.
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