Method for automated window-level settings for magnetic resonance images

1. A method of automatically determining window-level settings in a digital image of a subject, comprising:

accessing the digital image;

obtaining body part or coil type information;

determining a region of interest (ROI) within the digital image;

producing and processing a ROI-histogram;

defining a W-L value for a linear or non-linear image intensity range re-mapping; and

linear or non-linear re-mapping the image intensity range,

wherein the step of obtaining coil type information comprises image symmetry analysis.

2. The method of claim 1 , wherein the image symmetry analysis comprises the steps of:

calculating a vector of row mean values;

determining a maximal deviation of the row vector from an image global mean value;

calculating a vector of column mean values;

determining a maximal deviation of the column vector from the image global mean value; and

employing a predetermined value image asymmetry factor.

3. The method of claim 2 , wherein the predetermined value image asymmetry factor has a value of about 2.

4. The method of claim 1 , wherein the step of determining a region of interest (ROI) comprises image segmentation in a spatial domain.

5. The method of claim 4 , wherein the image segmentation comprises the steps of:

determining pixels of interest (POI) employing an image global mean value (GMV) as a threshold;

grouping the POI by defining an external boundary (EB); and

morphologically filtering the region within the EB to define the ROI.

6. The method of claim 1 , wherein the step of producing and processing a ROI-histogram comprises the steps of:

calculating a histogram within the ROI; and

smoothing the ROI-histogram with a lowpass, morphological or Gaussian filter.

7. The method of claim 1 , wherein the step of defining a W-L value comprises the step of thresholding and adjusting the ROI-histogram.

8. The method of claim 1 , wherein the step of non-linear re-mapping comprises the steps of:

calculating a non-linear intensity transfer function;

calculating non-linear weighting function coefficients; and

non-linear transforming of input image pixels.

9. The method of claim 8 , wherein the step of calculating a non-linear intensity transfer function is based on a cumulative distribution function (CDF) calculation from a smoothed, thresholded and adjusted ROI-histogram within a defined window for non-linear image intensity range re-mapping.

10. The method of claim 1 , wherein the digital image is a magnetic resonance image acquired using a magnetic resonance imaging (MRI) system.

11. The method of claim 1 , further comprising the step of displaying, archiving, or storing the digital image.

12. The method of claim 1 , further comprising the step of displaying, archiving, or storing the W-L value.

13. A method of automatically determining window-level settings in a digital image of a subject, comprising:

accessing the digital image;

obtaining body part or coil type information;

determining a region of interest (ROI) within the digital image;

producing and processing a ROI-histogram;

defining a W-L value for a linear or non-linear image intensity range re-mapping; and

linear or non-linear re-mapping the image intensity range,

wherein the step of defining a W-L value comprises the step of thresholding and adjusting the ROI-histogram, and

wherein the step of thresholding is accomplished by employing a threshold having a value different for the linear image intensity range re-mapping than for the non-linear image intensity range re-mapping.

14. The method of claim 13 , wherein the step of adjusting is accomplished by the steps of attaching valuable portions of the thresholded ROI-histogram for the non-linear image intensity range re-mapping to its widest portion when the valuable portions are separated from the widest one by gaps in intensity.

15. The method of claim 13 , wherein the step of defining a W-L value for a non-linear image intensity range re-mapping comprises the steps of:

smoothing, thresholding, and adjusting the ROI-histogram;

defining a window width as the difference between a last and a first non-zero value of the smoothed, thresholded and adjusted ROI-histogram; and

defining a window level as a middle point between the last and first non-zero values of the smoothed, thresholded and adjusted ROI-histogram.

16. The method of claim 13 , wherein the step of non-linear re-mapping the image intensity range comprises the step of windowing the input image pixels by setting all input image pixel values to black/white for image pixels that are less/greater than a predetermined lower/upper window limit, respectively.

17. A method of automatically determining window-level settings in a digital image of a subject, comprising:

accessing the digital image;

obtaining body part or coil type information;

determining a region of interest (ROI) within the digital image;

producing and Processing a ROI-histogram;

defining a W-L value for a linear or non-linear image intensity range re-mapping; and

linear or non-linear re-mapping the image intensity range,

wherein the step of non-linear re-mapping comprises the steps of:

calculating a non-linear intensity transfer function;

calculating non-linear weighting function coefficients; and

non-linear transforming of input image pixels, and

wherein the non-linear weighting function coefficients W 1 are calculated by the relationship:

W i =1+CDF i −( I i −I L )/ΔI

where i is an integer, CDF i , is a normalized CDF value, I i is an image pixel value, I L is a window lower limit, and ΔI is a defined window width.

18. A method of automatically determining window-level settings in a digital image of a subject, comprising:

accessing the digital image;

obtaining body part or coil type information;

determining a region of interest (ROI) within the digital image;

producing and processing a ROI-histogram;

defining a W-L value for a linear or non-linear image intensity range re-mapping; and

linear or non-linear re-mapping the image intensity range,

wherein the step of non-linear re-mapping comprises the steps of:

calculating a non-linear intensity transfer function;

calculating non-linear weighting function coefficients; and

non-linear transforming of input image pixels, and

wherein the step of non-linear transforming is accomplished according to the relationship:

I′=W ( I )· I w

where I′ are transformed image pixel values, W(I) is a weighting function, I w are windowed input image pixel values.