Ultrasound Speckle Reduction

after Coded Excitation and Pulse Compression

Quantifying Simulation Results

To assess the performance of each filter, many metrics were computed, including contrast-to-noise ratio (CNR), comparative signal-to-noise ratio (cSNR), mean squared error (MSE), and structural similarity (SSIM).  These metrics are briefly explained at the bottom of this page.

 A table of the average results of 30 tissue-mimicking phantoms can be seen here, with the above metrics highlighted.  Each block section is of a filter type (or the eREC-FC process alone) and each column shows results from a window size (for median, homogeneous mask area, and Lee) or number of iterations (for geometric and SRAD).

Important things to note from the data:
   - SRAD can improve CNR by an average 560% over CP.
   - SRAD can decrease the noise (speckle) and increase the signal information by an average 70% over eREC-FC.
   - SRAD more resembles the original CP image than eREC-FC does, based on MSE, and has the best structural resemblance to CP of all the filtering techniques.


CNR - quantifies the degree to which a region stands out from another region.  Larger numbers show better results.

cSNR - tells the degree to which noise (speckle) was suppressed in one image over the next.  All images are compared to CP.  Larger numbers show better results.

MSE - shows the average absolute difference between two images.  All images are compared to CP.  A smaller number is desired.

SSIM - denotes how well two images resemble each other, where 1 represents high similarity and -1 means no resemblance.

The other metrics used are extensions of the four above and are defined in the final project paper, in section III-B.


Quantifying Results

Metrics used to assess filter performance.