Artifacts detection-based adaptive filtering to noise reduction of strain imaging

Abstract

Strain imaging in medical ultrasound is the imaging modality of elastic properties of biological tissue. In general, strain image will suffer from artifacts noise, which degrades lesion detectability and increases the likelihood of misdiagnosis. How to both suppress artifacts effectively and preserve the structure is vital for diagnosis and also for image post-processing. The bilateral filtering can reduce artifact noise and, at the same time, maintain the tissue structure. However, the balance between noise suppression and edge preservation often makes the threshold selection difficult. This paper is to solve the problem of difficult threshold selection in bilateral filtering. The probability distribution function of amplitude modulation noise in this paper is derived from the statistics of uncompressed speckle. The statistical model of artifact formation is useful for designing an adaptive fast bilateral filter for artifact reduction in ultrasound strain imaging. Both simulation and phantom testing show that the proposed method can improve the quality of ultrasonic strain imaging. Furthermore, the elastographic signal-to-noise ratio was increased by 129.91% and 52.36% for simulated and phantom strain images. The elastographic contrast-to-noise ratio was increased by 521.42% and 218.07% for simulated and phantom strain images, respectively. As indicated by the profiles, the proposed method produces a better result for the purpose of visualization.

Keywords: Adaptive filter; Local histogram; Statistical analysis; Ultrasonic strain imaging.