10. Structural Image Bias Field Correction

At higher field strengths, sometimes structural images acquire an intensity gradient across the image making some parts of the image brighter than others. This intensity gradient can influence segmentation algorithms erroneously, therefore a method has been developed to remove this intensity gradient from the image, it is known as bias field correction.

  1. Choose (Segmentation | Brain Extraction Tool) .  A new Segmentation Control window will appear.  Put the crosshairs in the center of the brain above the Corpus callosum along the midline.
  2. On the Segmentation Control Window, set the Fractional Intensity Threshold to 0.6 and the Gradient Threshold to 0.0
  3. On the Segmentation Control Window, click use current crosshairs (make the box to the left, red in color).  Click execute brain extraction tool - BioImageSuite will automatically save new stripped brain as [filename]_stripped.hdr.  Click Display Under Brain/Skull Overlay to check if any tissue is erroneously included or excluded.
  4. Once a satisfactory stripping of the brain has been achieved, click the Math Morphology tab of the Segmentation Control Window.
  5. On the (Segmentation Control |  Math Morphology) tab click Generate Mask from Brain Extract Tool Output.  This will create a white mask of the stripped brain.  
  6. On the (Segmentation Control |  Math Morphology) tab click Dilate to increase the size of the mask slightly.
  7. On the Segmentation Control Window click the Bias Field Correction tab.
  8. On the (Segmentation Control | Bias Field Correction) tab click Grab Morph to input the previous mask into the bias field correction algorithm.
  9. On the Viewer window choose (File |  Load)
  10. Choose the stripped version of the brain image.
  11. On the (Segmentation Control | Bias Field Correction) tab click Optimize.  This will generate the bias filed correction and apply it to the image.
  12. The results shown in (Figure 10, image II), show that before the correction the histogram did not differentiate between gray and white matter, while after there are two clear peaks in the histogram.  Also, a simple segmentation is more precise after the correction rather than before.