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TADAnisotropicDiffusionCommand Class

Summary
An iterative filter that performs tensor-guided anisotropic diffusion in order to reduce noise while preserving the edges in the image.
Syntax
C#
Objective-C
C++/CLI
Java
Python
public class TADAnisotropicDiffusionCommand : RasterCommand 
@interface LTTADAnisotropicDiffusionCommand : LTRasterCommand 
public class TADAnisotropicDiffusionCommand 
    extends RasterCommand 
public ref class TADAnisotropicDiffusionCommand : public RasterCommand   
class TADAnisotropicDiffusionCommand(RasterCommand): 
Remarks
  • Three classes implement anisotropic diffusion:

    1. AnisotropicDiffusionCommand. The AnisotropiDiffusionCommand is a slower but more accurate version of SRADAnisotropDiffusionCommand.
    2. SRADAnisotropicDiffusionCommand. In comparison with conventional anisotropic diffusion, with the SRADAnisotropicDiffusionCommand uniform areas exhibit more smoothing and edges and features are preserved better.
    3. TADAnisotropicDiffusionCommand. The TADAnisotropicDiffusionCommand employs an edge-seeking diffusion coefficient. Areas within regions are smoothed, but the edges are not affected.
  • Use the TADAnisotropicDiffusionCommand to reduce noise in images (more specifically, medical images). Performance is better than that of the Median or Gaussian Filters.

  • The number of iterations controls the number of times the filter will be applied to an image. The lower the number of iterations, the faster the filter will perform. Higher iterations typically mean clearer results.
  • This command can only process the entire image. It does not support regions.
  • This command supports 8, 16-bit grayscale images and 24, 32-bit colored images.
  • This command supports signed/unsigned images.
Example
C#
Java
using Leadtools; 
using Leadtools.Codecs; 
using Leadtools.ImageProcessing.Core; 
 
public void TADAnisotropicDiffusionCommandExample() 
{ 
   // Load an image 
   RasterCodecs codecs = new RasterCodecs(); 
   codecs.ThrowExceptionsOnInvalidImages = true; 
 
   RasterImage image = codecs.Load(Path.Combine(LEAD_VARS.ImagesDir, "IMAGE3.dcm")); 
 
   // Prepare the command 
   TADAnisotropicDiffusionCommand command = new TADAnisotropicDiffusionCommand(); 
 
   // Apply 
   command.Iterations = 10; 
   command.Lambda = 14; 
   command.Kappa = 30; 
   command.Flags = TADAnisotropicDiffusionFlags.QuadraticFlux; 
   command.Run(image); 
} 
 
static class LEAD_VARS 
{ 
   public const string ImagesDir = @"C:\LEADTOOLS23\Resources\Images"; 
} 
 
import java.io.File; 
import java.io.IOException; 
 
import org.junit.*; 
import org.junit.runner.JUnitCore; 
import org.junit.runner.Result; 
import org.junit.runner.notification.Failure; 
import static org.junit.Assert.assertTrue; 
 
import leadtools.*; 
import leadtools.codecs.*; 
import leadtools.imageprocessing.core.*; 
 
 
public void tadAnisotropicDiffusionCommandExample() { 
   final String LEAD_VARS_IMAGES_DIR = "C:\\LEADTOOLS23\\Resources\\Images"; 
   // Load an image 
   RasterCodecs codecs = new RasterCodecs(); 
   codecs.setThrowExceptionsOnInvalidImages(true); 
 
   RasterImage image = codecs.load(combine(LEAD_VARS_IMAGES_DIR, "DICOM\\IMAGE3.dcm")); 
 
   // Prepare the command 
   TADAnisotropicDiffusionCommand command = new TADAnisotropicDiffusionCommand(); 
 
   // Apply 
   command.setIterations(10); 
   command.setLambda(14); 
   command.setKappa(30); 
   command.setFlags(TADAnisotropicDiffusionFlags.QUADRATIC_FLUX); 
   int change = command.run(image); 
   assertTrue(change != RasterImageChangedFlags.NONE); 
 
   codecs.save(image, combine(LEAD_VARS_IMAGES_DIR, "DICOM\\IMAGE3.dcm"), RasterImageFormat.JPEG, 24); 
   System.out.println("Command run and image saved to " + combine(LEAD_VARS_IMAGES_DIR, "DICOM\\IMAGE3.dcm")); 
} 
Requirements

Target Platforms

Help Version 23.0.2024.3.3
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Leadtools.ImageProcessing.Core Assembly

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© 1991-2023 LEAD Technologies, Inc. All Rights Reserved.