Echocardiography noise reduction using sparse representation. (July 2016)

• Record Type: Journal Article
• Authors: Gifani, Parisa; Behnam, Hamid; Haddadi, Farzan; Sani, Zahra Alizadeh; Gifani, Peyman
• Is Part Of: Computers & electrical engineering. Volume 53(2016)
• Page Start: 301
• Page End: 318
• Publication Date: 2016-07
• Subjects: Echocardiographic images -- Noise reduction -- Temporal information -- Sparse representation -- Adaptive thresholding; Computer engineering -- Periodicals; Electrical engineering -- Periodicals; Electrical engineering -- Data processing -- Periodicals; Ordinateurs -- Conception et construction -- Périodiques; Électrotechnique -- Périodiques; Électrotechnique -- Informatique -- Périodiques; Computer engineering; Electrical engineering; Electrical engineering -- Data processing; Periodicals; Electronic journals; 621.302854
• DOI: 10.1016/j.compeleceng.2015.12.008
• Languages: English
• ISSNs: 0045-7906

Abstract

Highlights: Noise reduction in echocardiography images is proposed. Filtering framework is based on temporal information and sparse representation. Proposed method consists of smoothing intensity variation time curves assessed in each pixel. A smooth version of signal can be reconstructed by using a proper sparse recovery which is followed by an adaptive thresholding method to locate the most important atoms. After a comprehensive comparison of sparse recovery algorithms, three were selected for our method: Bayesian Compressive Sensing (BCS), Bregman Iterative algorithm, and Orthogonal Matching Pursuit (OMP). The proposed method preserves the edges and rapidly moving structures. Abstract: The clarity and accuracy of echocardiography images are greatly reduced by speckle noise. Noise suppression, however, is difficult to achieve without also obscuring both rapidly moving structures and object edges. This research seeks to address these challenges by introducing a novel filtering framework based on temporal information and sparse representation. The proposed method involves smoothing intensity variation time curves (IVTCs) assessed in each pixel. Using an over-complete dictionary that contains prototype signal-atoms, IVTCs can be reconstructed as linear combinations of a few of these atoms. After a comprehensive comparison of sparse recovery algorithms, three were selected for our method: Bayesian Compressive Sensing (BCS), the Bregman iterative algorithm, and Orthogonal Matching Pursuit (OMP). The performance of the proposed method was then evaluated and compared with other speckle reduction filters. The experimental results demonstrate that the proposed algorithm can be used to achieve better-preserved edges and reduce blurring. Graphical abstract: