Noise level estimation for effective blind despeckling of medical ultrasound images

Despeckling of medical ultrasound images is an essential pre-processing step in automated diagnosis systems. The performance of certain image despeckling methods is dependent on the noise level of an input image. In the design of despeckling algorithms, the noise level is assumed to be known. Therefore, it is essential to estimate the noise level from input ultrasound image for effective blind despeckling. In this paper, a novel noise level estimation (NLE) technique is proposed to estimate the speckle (signal-dependent) noise level from the noisy ultrasound images. The presented NLE technique uses noise level aware features obtained from the high-pass filtered noisy image. The extracted features are then used to train the support vector regression (SVR) model. The proposed feature extraction technique followed by the SVR model results in accurate NLE. In order to ensure the effectiveness, the proposed NLE technique is incorporated and validated with the NLE-dependent state-of-the-art despeckling methods. The combination of state-of-the-art despeckling methods with the proposed NLE technique shows superior despeckling performance when compared to existing NLE techniques. In this work, block matching based despeckling method is recommended in combination with the proposed NLE technique for better despeckling performance in the ultrasound images. The experimental results demonstrate that the proposed NLE technique shows better performance than existing NLE techniques in terms of average absolute deviation and execution time.

Automated summary

This paper presents a novel noise level estimation technique for speckle removal in ultrasound images, which accurately estimates the noise level by extracting noise level aware features and training a support vector regression model. Incorporating this technique with advanced despeckling methods results in superior despeckling performance in ultrasound images.