3D Order Statistics Filters for Ultrasound Imaging

V. Ponomaryov, R. Sensores-Pech, and F. Gallegos-Funes (Mexico)


3D Processing, ultrasound imaging, noise suppression, small-size detail preservation.


Usually, 3D images in such application as medical, sequence video processing, etc., are contaminated by a noise that can be modeled as an impulsive, Gaussian, or may be speckle one. In here we present the implementation of the three dimensional (3D) filters applied for ultrasound (US) medical imaging. The modified order statistics algorithms suppressing noise and preserving the small-size image details have been used. The performance of the presented filters has been demonstrated for 3D objects by means of use the objective criteria PSNR and MAE, and subjective one, analyzing an error image. Alfa Trimmed Mean and ROM filters have presented the better results, and the MM KNN filter obtained good results for small values of contamination. MMKNN filter was the most efficient in impulsive noise suppression for higher level of contamination, also reducing the speckle noise that is natural for coherent US transducer. The DSP TMS320C6711 was employed for implementation in 3D imaging. The processing times for Alfa Trimmed Mean filter present sufficiently high values. The time values for RM-KNN filters are larger in comparison with other filters, but their performance is sufficiently better, and by selecting an adequate configuration of the voxels into a sliding cube, the time values can be reduced significantly without losing of good filter quality.

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