BioMedical Engineering OnLine | |
Reconstruction of freehand 3D ultrasound based on kernel regression | |
Xiankang Chen1  Tiexiang Wen2  Xingmin Li1  Wenjian Qin2  Donglai Lan2  Weizhou Pan1  Jia Gu2  | |
[1] School of Computer, South China Normal University, 510631 Guangzhou, China | |
[2] The Shenzhen Key Laboratory for Low-cost Healthcare, 518055 Shenzhen, China | |
关键词: Nonparametric statistics; Interpolation; Reconstruction; Kernel regression; Freehand ultrasound; | |
Others : 1084477 DOI : 10.1186/1475-925X-13-124 |
|
received in 2014-03-04, accepted in 2014-08-05, 发布年份 2014 | |
【 摘 要 】
Introduction
Freehand three-dimensional (3D) ultrasound has the advantages of flexibility for allowing clinicians to manipulate the ultrasound probe over the examined body surface with less constraint in comparison with other scanning protocols. Thus it is widely used in clinical diagnose and image-guided surgery. However, as the data scanning of freehand–style is subjective, the collected B-scan images are usually irregular and highly sparse. One of the key procedures in freehand ultrasound imaging system is the volume reconstruction, which plays an important role in improving the reconstructed image quality.
System and methods
A novel freehand 3D ultrasound volume reconstruction method based on kernel regression model is proposed in this paper. Our method consists of two steps: bin-filling and regression. Firstly, the bin-filling step is used to map each pixel in the sampled B-scan images to its corresponding voxel in the reconstructed volume data. Secondly, the regression step is used to make the nonparametric estimation for the whole volume data from the previous sampled sparse data. The kernel penalizes distance away from the current approximation center within a local neighborhood.
Experiments and results
To evaluate the quality and performance of our proposed kernel regression algorithm for freehand 3D ultrasound reconstruction, a phantom and an in-vivo liver organ of human subject are scanned with our freehand 3D ultrasound imaging system. Root mean square error (RMSE) is used for the quantitative evaluation. Both of the qualitative and quantitative experimental results demonstrate that our method can reconstruct image with less artifacts and higher quality.
Conclusion
The proposed kernel regression based reconstruction method is capable of constructing volume data with improved accuracy from irregularly sampled sparse data for freehand 3D ultrasound imaging system.
【 授权许可】
2014 Chen et al.; licensee BioMed Central Ltd.
【 预 览 】
Files | Size | Format | View |
---|---|---|---|
20150113161956404.pdf | 2343KB | download | |
Figure 7. | 109KB | Image | download |
Figure 6. | 162KB | Image | download |
Figure 5. | 200KB | Image | download |
Figure 4. | 167KB | Image | download |
Figure 3. | 30KB | Image | download |
Figure 2. | 35KB | Image | download |
Figure 1. | 89KB | Image | download |
【 图 表 】
Figure 1.
Figure 2.
Figure 3.
Figure 4.
Figure 5.
Figure 6.
Figure 7.
【 参考文献 】
- [1]Wen T, Zhu Q, Qin W, Li L, Yang F, Xie Y, Gu J: An accurate and effective fmm-based approach for freehand 3d ultrasound reconstruction. Biomed Signal Process Control 2013, 8(6):645-656.
- [2]Huang Q-H, Zheng Y-P, Lu M-H, Chi Z: Development of a portable 3d ultrasound imaging system for musculoskeletal tissues. Ultrasonics 2005, 43(3):153-163.
- [3]Toonkum P, Suwanwela NC, Chinrungrueng C: Reconstruction of 3d ultrasound images based on cyclic regularized savitzky–golay filters. Ultrasonics 2011, 51(2):136-147.
- [4]Solberg OV, Lindseth F, Torp H, Blake RE, Nagelhus Hernes TA: Freehand 3d ultrasound reconstruction algorithms-a review. Ultrasound Med Biol 2007, 33(7):991-1009.
- [5]Sherebrin S, Fenster A, Rankin RN, Spence D: Freehand three-dimensional ultrasound: implementation and applications. In Medical Imaging 1996. Bellingham: International Society for Optics and Photonics; 1996:296-303.
- [6]Rohling R, Gee A, Berman L: A comparison of freehand three-dimensional ultrasound reconstruction techniques. Med Image Anal 1999, 3(4):339-359.
- [7]Huang Q-H, Zheng Y-P: Volume reconstruction of freehand three-dimensional ultrasound using median filters. Ultrasonics 2008, 48(3):182-192.
- [8]Huang Q-H, Zheng Y-P: An adaptive squared-distance-weighted interpolation for volume reconstruction in 3d freehand ultrasound. Ultrasonics 2006, 44:73-77.
- [9]Huang Q, Lu M, Zheng Y, Chi Z: Speckle suppression and contrast enhancement in reconstruction of freehand 3d ultrasound images using an adaptive distance-weighted method. Appli Acoustics 2009, 70(1):21-30.
- [10]Huang Q, Zheng Y, Lu M, Wang T, Chen S: A new adaptive interpolation algorithm for 3d ultrasound imaging with speckle reduction and edge preservation. Comput Med Imaging Graph 2009, 33(2):100-110.
- [11]Coupé P, Hellier P, Morandi X, Barillot C: Probe trajectory interpolation for 3d reconstruction of freehand ultrasound. Med Image Anal 2007, 11(6):604-615.
- [12]Nelson TR, Pretorius DH: Interactive acquisition, analysis, and visualization of sonographic volume data. Int J Imaging Syst Technol 1997, 8(1):26-37.
- [13]Gobbi D, Peters T: Interactive intra-operative 3d ultrasound reconstruction and visualization. Med Image Comput Computer-Assisted Intervention-MICCAI 2002, 2489:156-163.
- [14]Ohbuchi R, Chen D, Fuchs H: Incremental volume reconstruction and rendering for 3-d ultrasound imaging. In Visualization in Biomedical Computing. Bellingham: International Society for Optics and Photonics; 1992:312-323.
- [15]Trobaugh JW, Trobaugh DJ, Richard WD: Three-dimensional imaging with stereotactic ultrasonography. Comput Med Imaging Graph 1994, 18(5):315-323.
- [16]Estépar R, Martín-Fernández M, Alberola-López C, Ellsmere J, Kikinis R, Westin C-F: Freehand ultrasound reconstruction based on roi prior modeling and normalized convolution. Medic Image Comput Computer-Assisted Intervention-MICCAI 2003, 6(Pt 2):382-390.
- [17]Dewi D, Wilkinson M, Mengko T, Purnama I, van Ooijen P, Veldhuizen A, Maurits N, Verkerke G: 3d ultrasound reconstruction of spinal images using an improved olympic hole-filling method. In Instrumentation, Communications, Information Technology, and Biomedical Engineering (ICICI-BME), 2009 International Conference On. Bandung: IEEE; 2009:1-5.
- [18]Scheipers U, Koptenko S, Remlinger R, Falco T, Lachaine M: 3-d ultrasound volume reconstruction using the direct frame interpolation method. Ultrason Ferroelectr Freq Control IEEE Trans 2010, 57(11):2460-2470.
- [19]Sanches JM, Marques JS: A rayleigh reconstruction/interpolation algorithm for 3d ultrasound. Pattern Recognit Lett 2000, 21(10):917-926.
- [20]Fan J: Local linear regression smoothers and their minimax efficiencies. Annals Stat 1993, 21(1):196-216.
- [21]Takeda H, Farsiu S, Milanfar P: Kernel regression for image processing and reconstruction. Image Process IEEE Trans 2007, 16(2):349-366.
- [22]López-Rubio E, Florentín-Núñez MN: Kernel regression based feature extraction for 3d mr image denoising. Med Image Anal 2011, 15(4):498-513.
- [23]Triple Modality 3D Abdominal Phantom Model 057 [ http://www.cirsinc.com/products/all/65/triple-modality-3d-abdominal-phantom/ webcite]
- [24]Hardle W: Applied Nonparametric Regression, vol. 5. Cambridge England and New York: Cambridge University Press; 1990.
- [25]Härdle W: Smoothing Techniques: with Implementation in S. New York: Springer; 1991.
- [26]Wand MMP, Jones MC: Kernel Smoothing, vol. 60. UK: Crc Press; 1995.
- [27]Hardle W: Nonparametric and semiparametric models. Springer, Verlag Berlin and Heidelberg GmbH & Co. K; 2012.
- [28]Wang Z, Bovik AC: A universal image quality index. Signal Process Lett IEEE 2002, 9(3):81-84.