Method and apparatus for pre-processing scans by combining enhancement and denoising as a preparation for segmenting the same

1. A non-transitory computer-implemented method for pre-processing image data of a three-dimensional volume comprising:

capturing the image data representing a vessel by a Magnetic Resonance (MR) scanner;

applying a super-sampling filter to the image data to generate super-sampled image data having an increased resolution as compared to the image data;

applying an unsharp masking filter to the super-sampled image data for increasing contrast of an edge of the vessel;

applying a de-noising filter for removing noise surrounding the edge after applying the unsharp masking filter; and

storing the image data after applying the de-noising filter thereto.

2. The computer-implemented method of claim 1 , wherein applying the super-sampling filter comprises:

upsampling the image data for increasing the resolution of the image data;

determining a Fourier transform of the image data after upsampling;

applying a low-pass filter to the Fourier transform;

multiplying voxel values of the image data by a Fourier gain after applying the low-pass filter;

applying an inverse Fourier transform (FT) to the image data to determine an interpolated image after multiplying voxel values; and

outputting the interpolated image, wherein the interpolated image is the super-sampled image data.

3. The computer-implemented method of claim 2 , wherein upsampling comprises inserting a zero value between each pair of adjacent voxels of the image data.

4. The computer-implemented method of claim 1 , wherein applying the unsharp masking filter comprises filtering the image data by:

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where a negative of a Laplacian filter α controls a shape of a Laplacian, with a default α=0.2.

5. The computer-implemented method of claim 1 , wherein the de-noising filter is edge-preserving.

6. The computer-implemented method of claim 1 further comprising segmenting stored image data.

7. A non-transitory computer readable medium embodying a program of instructions executable by a processor to perform method steps for pre-processing image data of a three-dimensional volume, the method steps comprising:

providing the image data of a vessel;

applying a super-sampling filter to the image data to generate super-sampled image data having an increased resolution as compared to the image data;

applying an unsharp masking filter to the super-sampled image data for increasing contrast of an edge of the vessel;

applying a de-noising filter for removing noise surrounding the edge after applying the unsharp masking filter; and

storing the image data after applying the de-noising filter thereto.

8. The computer readable medium of claim 7 , wherein applying the super-sampling filter comprises:

upsampling the image data for increasing the resolution of the image data; determining a Fourier transform of the image data after upsampling;

applying a low-pass filter to the Fourier transform;

multiplying voxel values of the image data by a Fourier gain after applying the low-pass filter;

applying an inverse Fourier transform (FT) to the image data to determine an interpolated image after multiplying voxel values; and

outputting the interpolated image, wherein the interpolated image is the super-sampled image data.

9. The computer readable medium of claim 8 , wherein upsampling comprises inserting a zero value between each pair of adjacent voxels of the image data.

10. The computer readable medium of claim 7 , wherein applying the unsharp masking filter comprises filtering the image data by:

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α

+

1

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where a negative of a Laplacian filter α controls a shape of a Laplacian, with a default α=0.2.

11. The computer readable medium of claim 7 , wherein the de-noising filter is edge-preserving.

12. The computer readable medium of claim 7 , further comprising segmenting stored image data.

13. A computer-implemented method for pre-processing Magnetic Resonance (MR) image data comprising:

capturing the MR image data representing a vessel by an MR scanner;

super-sampling the MR image data to generate super-sampled MR image data, wherein super-sampling comprises:

upsampling the MR image data by inserting a zero value between each pair of voxels of the MR image data;

determining a Fourier transform of the MR image data after upsampling;

applying a low-pass filter to the Fourier transform;

multiplying voxel values of the MR image data by a gain after applying the low-pass filter;

applying an inverse Fourier transform (FT) to the MR image data to determine an interpolated image after multiplying voxel values; and

outputting the interpolated image, wherein the interpolated image is the super-sampled MR image data;

increasing contrast of an edge of the vessel in the super-sampled MR image data;

removing noise about the edge of the vessel, without interpolating across the edge of the vessel, to generate pre-processed MR image data; and

segmenting the pre-processed MR image data.