Yeow, JohnBen Daya, IbrahimCHEN, ALBERT I-HSIANGShafiee, Mohammad JavadWong, Alexander2017-02-102017-02-102015-12-11http://dx.doi.org/10.1371/journal.pone.0142817http://hdl.handle.net/10012/11306Ben Daya I, Chen AIH, Shafiee MJ, Wong A, Yeow JTW (2015) Compensated Row-Column Ultrasound Imaging System Using Fisher Tippett Multilayered Conditional Random Field Model. PLoS ONE 10(12): e0142817. doi:10.1371/journal.pone.01428173-D ultrasound imaging offers unique opportunities in the field of non destructive testing that cannot be easily found in A-mode and B-mode images. To acquire a 3-D ultrasound image without a mechanically moving transducer, a 2-D array can be used. The row column technique is preferred over a fully addressed 2-D array as it requires a significantly lower number of interconnections. Recent advances in 3-D row-column ultrasound imaging systems were largely focused on sensor design. However, these imaging systems face three intrinsic challenges that cannot be addressed by improving sensor design alone: speckle noise, sparsity of data in the imaged volume, and the spatially dependent point spread function of the imaging system. In this paper, we propose a compensated row-column ultrasound image reconstruction system using Fisher-Tippett multilayered conditional random field model. Tests carried out on both simulated and real row-column ultrasound images show the effectiveness of our proposed system as opposed to other published systems. Visual assessment of the results show our proposed system’s potential at preserving detail and reducing speckle. Quantitative analysis shows that our proposed system outperforms previously published systems when evaluated with metrics such as Peak Signal to Noise Ratio, Coefficient of Correlation, and Effective Number of Looks. These results show the potential of our proposed system as an effective tool for enhancing 3-D row-column imaging.enAttribution 4.0 InternationalUltrasound imagingData acquisitionSimulation and modelingOptimizationQuantiative analysisRandom variablesImage processingSignal to noise ratioArticle