This chapter provides the basic theoretical background of linear despeckle filtering techniques together with their algorithmic implementation, MATLAB^® code for selected filters and practical examples on phantom and real ultrasound images. There are three groups of filters presented in this chapter, first-order statistics filtering, local statistics filtering and homogeneous mask area filtering. Despeckle filtering was evaluated for all filters presented in this chapter on phantom ultrasound carotid artery images and real ultrasound images and videos of the common carotid artery (CCA). Furthermore, we present an evaluation and comparison of five linear despeckle filtering algorithms presented in this chapter. The evaluation is carried out on a phantom image, an artificial image and on real carotid and cardiac ultrasound images. Furthermore, findings on video despeckling are presented.

Chapter Contents:

• 7 Linear despeckle filtering
• 7.1 First-order statistics filtering (DsFlsmv, DsFwiener)
• 7.2 Local statistics filtering with higher moments (DsFlsminv1d, DsFlsmvsk2d)
• 7.3 Homogeneous mask area filtering (DsFlsminsc)
• 7.4 Despeckle filtering evaluation on an artificial carotid artery image
• 7.5 Despeckle filtering evaluation on a phantom image
• 7.6 Despeckle filtering evaluation on real ultrasound images and video
• 7.7 Summary findings on despeckle filtering evaluation
• References

Inspec keywords: cardiology; image filtering; blood vessels; video signal processing; phantoms; biomedical ultrasonics; statistical analysis; medical image processing

Other keywords: artificial image; video despeckling; first-order statistics filtering; cardiac ultrasound images; MATLAB code; linear despeckle filtering; real ultrasound images; phantom ultrasound carotid artery images; algorithmic implementation; local statistics filtering; homogeneous mask area filtering

Subjects: Other topics in statistics; Other topics in statistics; Optical, image and video signal processing; Sonic and ultrasonic applications; Video signal processing; Sonic and ultrasonic radiation (medical uses); Probability theory, stochastic processes, and statistics; Computer vision and image processing techniques; Biology and medical computing; Patient diagnostic methods and instrumentation; Sonic and ultrasonic radiation (biomedical imaging/measurement)