Noise level estimation for effective blind despeckling of medical ultrasound images. (July 2021)

• Record Type: Journal Article
• Authors: Sudharson, S.; Pratap, Turimerla; Kokil, Priyanka
• Is Part Of: Biomedical signal processing and control. Volume 68(2021)
• Publication Date: 2021-07
• Subjects: Noise level estimation -- Blind despeckling -- Speckle noise -- Feature extraction -- Support vector regression; Signal processing -- Periodicals; Biomedical engineering -- Periodicals; Signal Processing, Computer-Assisted -- Periodicals; Image Processing, Computer-Assisted -- Periodicals; Biomedical Engineering -- Periodicals; 610.28
• DOI: 10.1016/j.bspc.2021.102744
• Languages: English
• ISSNs: 1746-8094

Abstract

Highlights: A novel method for estimating the speckle noise from the ultrasound images is proposed using the noise level aware features. The presented method outperforms the existing noise level estimation (NLE) methods in terms of average absolute deviation and execution time. The proposed NLE method is incorporated in the noise level dependent despeckling filters to eliminate speckle noise effectively and presented method improves the performance of despeckling filter. The presented approach would help the radiologists and nephrologists for the easier diagnosis of diseases from the ultrasound images. Abstract: 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.