Multiresolution generalized N dimension PCA for ultrasound image denoising

BioMedical Engineering OnLine

Danni Ai¹  Jian Yang¹  Yang Chen²  Weijian Cong¹  Jingfan Fan¹  Yongtian Wang¹ 

[1] Beijing Engineering Research Center of Mixed Reality and Advanced Display, School of Optics and Electronics, Beijing Institute of Technology, Beijing 100081, China

[2] Key Laboratory of Computer Network and Information Integration, Ministry of Education of China, Laboratory of Image Science and Technology, Southeast University, Nanjing 210096, China

关键词: Noise reduction;    Multilinear subspace learning;    Multiresolution;   

Others  :  1084570
DOI  :  10.1186/1475-925X-13-112

 received in 2014-06-03, accepted in 2014-07-22,  发布年份 2014

【 摘 要 】

Background

Ultrasound images are usually affected by speckle noise, which is a type of random multiplicative noise. Thus, reducing speckle and improving image visual quality are vital to obtaining better diagnosis.

Method

In this paper, a novel noise reduction method for medical ultrasound images, called multiresolution generalized N dimension PCA (MR-GND-PCA), is presented. In this method, the Gaussian pyramid and multiscale image stacks on each level are built first. GND-PCA as a multilinear subspace learning method is used for denoising. Each level is combined to achieve the final denoised image based on Laplacian pyramids.

Results

The proposed method is tested with synthetically speckled and real ultrasound images, and quality evaluation metrics, including MSE, SNR and PSNR, are used to evaluate its performance.

Conclusion

Experimental results show that the proposed method achieved the lowest noise interference and improved image quality by reducing noise and preserving the structure. Our method is also robust for the image with a much higher level of speckle noise. For clinical images, the results show that MR-GND-PCA can reduce speckle and preserve resolvable details.

【 授权许可】

   
2014 Ai et al.; licensee BioMed Central Ltd.

【 图 表 】

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