- Research
- Open Access

# Twofold processing for denoising ultrasound medical images

- P. V. V. Kishore†
^{1}Email authorView ORCID ID profile, - K. V. V. Kumar†
^{1}, - D. Anil kumar†
^{1}, - M. V. D. Prasad†
^{1}, - E. N. D. Goutham†
^{1}, - R. Rahul†
^{1}, - C. B. S. Vamsi Krishna†
^{1}and - Y. Sandeep†
^{1}

**Received:**13 October 2015**Accepted:**26 November 2015**Published:**14 December 2015

## Abstract

Ultrasound medical (US) imaging non-invasively pictures inside of a human body for disease diagnostics. Speckle noise attacks ultrasound images degrading their visual quality. A twofold processing algorithm is proposed in this work to reduce this multiplicative speckle noise. First fold used block based thresholding, both hard (BHT) and soft (BST), on pixels in wavelet domain with 8, 16, 32 and 64 non-overlapping block sizes. This first fold process is a better denoising method for reducing speckle and also inducing object of interest blurring. The second fold process initiates to restore object boundaries and texture with adaptive wavelet fusion. The degraded object restoration in block thresholded US image is carried through wavelet coefficient fusion of object in original US mage and block thresholded US image. Fusion rules and wavelet decomposition levels are made adaptive for each block using gradient histograms with normalized differential mean (NDF) to introduce highest level of contrast between the denoised pixels and the object pixels in the resultant image. Thus the proposed twofold methods are named as adaptive NDF block fusion with hard and soft thresholding (ANBF-HT and ANBF-ST). The results indicate visual quality improvement to an interesting level with the proposed twofold processing, where the first fold removes noise and second fold restores object properties. Peak signal to noise ratio (PSNR), normalized cross correlation coefficient (NCC), edge strength (ES), image quality Index (IQI) and structural similarity index (SSIM), measure the quantitative quality of the twofold processing technique. Validation of the proposed method is done by comparing with anisotropic diffusion (AD), total variational filtering (TVF) and empirical mode decomposition (EMD) for enhancement of US images. The US images are provided by AMMA hospital radiology labs at Vijayawada, India.

## Keywords

- Ultrasound medical image denoising
- Speckle noise
- Block processing in wavelet domain
- Hard and soft thresholding
- Medical image fusion

## Background

Ultrasound medical images save many lives by early detection of fetus differences in pregnant woman for around two decades now. Compared with tomography (CT), magnetic resonance imaging (MRI) and Positron emission technology (PET), ultrasound (US) imaging is the safest for the sensitive fetus. Fetus scanning of pregnant woman enables gynecologists to check the health of the baby and mother satisfactorily. Ultrasound scanners come in handy to perform this job at lower costs and radiation effects. But the visual quality of images from ultrasound scanners is significantly poor because of speckle noise (Touzi 2002; Dinh-Hoan Trinh et al. 2014; Sonia et al. 2012; Huang and Xiaoping 2013). Lessening the effects of speckle at device and image level is extensively researched. Device level improvements in the form of 3D (Fenster and Downey 2013) and 4D ultrasound scanners (Solberg et al. 2011) are available. These improvements come at a cost that is unbearable by hospitals in poorer countries. Therefore, cheaper alternatives for improving visually as a necessary post processing step for 2D ultrasound images. These post processing steps include speckle noise removal, contrast enhancement and edge preserving methods. Speckle noise results from multiple reflections of ultrasound waves from the hard tissues of the scanned human body. The nature of speckle is multiplicative. Therefore difficult to model in real time, so inverse filtering methods to remove noise may be effective. In the last decade, there were denoising methods influenced by the fields of computer science, signal processing, probability and artificial intelligence (Compas et al. 2014; Chernyakova and Eldar 2014; Zhang et al. 2010; Ng et al. 2006; Belaid et al. 2011). A set of algorithms under signal processing category based on spatial and frequency domains improves visibility.

Spatial domain filtering techniques such as linear, adaptive linear filtering, adaptive Wiener, median, anisotropic diffusion, constraint least mean squares and higher order filtering applied for speckle reduction (Byram et al. 2013; Loizou et al. 2012; Gavriloaia and Gavriloaia 2011; Christos and Constantinos 2008; Yeoh and Zhang 2006). These algorithms did a great job on improving ultrasound images in early days of ultrasound detections. The spatial domain filtering lessens noise inducing a blur to the objects in the ultrasound images. Filter coefficient selection is a difficulty faced by these spatial filtering algorithms.

Pixel based likelihood approaches (Zhang et al. 2007; Yu et al. 2012) such as Bayes classifier Tao Hou et al. (2010) and Gaussian mixture models (GMM) (Gavriloaia and Gavriloaia 2011) denoise algorithms set in the ultrasound scanner. Currently most real time scanners around the world employ these algorithms. Computing the probability density functions and joint probability density functions classify noisy pixels and object pixels in ultrasound images. The probability based algorithms are a little low on accuracy. The denoised ultrasound images in the ultrasound machine still have noise. Their effectiveness loses ground because of the speckle ingredient in ultrasound image varies rapidly between successive images.

Other pixel processing method that revolutionized image processing is thresholding. Thresholding reduces noise from medical ultrasound images by putting a constraint on selection of correct threshold (Achim et al. 2001). However thresholding drops the visual quality of the objects in the image (Trinh et al. 2014). The worst hit parts are edges of objects in the medical image. Edge detection and contrast enhancement are two most popular thresholding methods used on images for visual quality improvement. These processing algorithms suffer dearly when there is a slight difference in sensitivity between pixel intensities of noise and edges (Shaimaa et al. 2012; Lee et al. 2012).

Frequency domain processing of ultrasound images involves filtering of speckle noise in transformed domain (Andria et al. 2013; Wei et al. 2013, 2014). The wavelet transform is exclusively used for speckle reduction. The multiresolution filter bank approach frames computing fast 2D wavelet transform (Dantas and Costa 2007; Rabbani et al. 2008). Filter banks work well at reducing speckle in ultrasound medical images. There are quite a few problems associated with wavelet approaches such as decrease image resolution at higher levels, choice of mother wavelet and loss of edge at higher levels of decomposition (Esakkirajan et al. 2013). Different algorithms are proposed in literature to overcome these effects in recent times showing little enhancements to visual quality (Adamo et al. 2013). Artificial intelligence methods such as artificial neural networks (ANN) (Andria et al. 2012), fuzzy logic (Park and Nishimura 2007), genetic algorithms (Zhang et al. 2010) and ant bee colony algorithm deal with the speckle intelligently (Munteanu et al. 2008). ANN and Fuzzy need extensive training to perform the task on larger data sets. These algorithms give better visual quality only when trained with larger data sets. However, difficulties increase due to the continuously varying nature of speckle in the medical image.

Finally, model based techniques are introduced to produce 3D ultrasound imaging (Fenster and Downey 2013; Latifoglu 2013). This reduced the noise to large extent improving the visibility of objects in the medical image. But these improvements come at a higher price. For most of the poorer countries, it is a matter of affordability. Hence, even though 3D ultrasound model based images is exclusively used in practice it is still difficult to find in a country like India. Hence the speckle reduction in ultrasound medical images will be a major research area in the coming years.

This research paper proposes a novel two fold processing method to reduce the effect of speckle in ultrasound medical images (Huang et al. 2009; Gao and Bui 2005; Rui et al. 2007; Yu et al. 2001). The proposed method calculates the wavelet coefficients from medical image using a multiresolution filter bank approach. The coefficients scaling of amplitude is soft and hard thresholding. Wavelet based object edge reconstruction on the thresholded medical images by using fusion technique is proposed. The wavelet based fusion acts as a value addition to thresholded images to restore the edges of objects in the ultrasound image. This twofold algorithm reduces speckle noise and restores edge quality for better and faster diagnostics by doctors. Verification of the proposed method by doctors at AMMA Hospital, Vijayawada, INDIA and NRI Medical college Hospital, Guntur, INDIA were initiated.

The rest of the paper is organizes as follows. “Twofold proposed technique” gives twofold technique using wavelet transform. “Results and discussion” discusses the results of the proposed algorithm on ultrasound medical image of fetus obtained from AMMA hospital Vijayawada. “Conclusion” compares the results from the proposed algorithm with the results from standard denoising algorithms on medical images. Section 5 concludes the proposed research based on experiments conducted in the previous sections.

## Twofold proposed technique

The two fold technique proposed involves a twostep process in wavelet domain. First step is block thresholding of ultrasound medical image wavelet coefficients followed by fusion of thresholded image with the original image. Thresholding employs hard and soft wavelet thresholding on detailed wavelet coefficients (Marsousi et al. 2013). Apart from removing speckle they also blur the edges. The fusion in wavelet domain restores lost edges of objects during the thresholding. Fusion also improves the contrast of the denoised image. Here adaptive block fusion ensures correct fusion rule at a particular level preserves object properties such as edge and contrast. An ultrasound medical image *U*(*x*, *y*), where \(x,y\in Z^\dagger\) and \(U\in R^\dagger\) is convolved with a standard orthogonal 2D filter coefficients \(f_{s{1}s{2}}^L(x,y)\),where \(s_{1}\) and \(s_{2}\subset R^\dagger\) denote the scaling factors and \(L\subset Z^\dagger\) is decomposition level to produce a 2D discrete wavelet transform having approximate and detailed coefficients as in Eqs. (1) and (2).

*U*(

*x*,

*y*) is formulated as

The noise in the ultrasound images is found around a few wavelet coefficients. When compared to wavelet object coefficients in the ultrasound image, they are present in large coefficients. Edges mark the boundaries of objects in the image. Thresholding in wavelet domain is making the smaller noise coefficients negligible and larger edge coefficients important. Thresholding of wavelet coefficients reduces speckle noise. However this affects tissue edges that are objects in the denoised images. The edge appears blurred making visually difficult to understand object boundaries.

Therefore global thresholding of wavelet coefficients results in edge loss of objects in the image. Edge loss represents blurring of the edges and decrease in contrast of the ultrasound image as a whole. This can be avoided to a certain extent using the block based thresholding of wavelet coefficients. Block processing makes the thresholding local to that particular block and preserving the contrast in the ultrasound images. Two classes of thresholding algorithms are used to filter wavelet coefficients. They are Hard Thresholding (HT) and Soft Thresholding (ST) as discussed below.

### Block based hard thresholding (BHT)

### Block based soft thresholding (BST)

The proposed solution for removing blocking artifacts and blurring of region of interest objects comes from wavelet based fusion. Fusion in wavelet domain improves the visual quality of the degraded images from multiple sources. The second technique is fusion of the original ultrasound medical image and the denoised ultrasound image from the first technique in wavelet domain.

The fusion aims to combine wavelet coefficients of block denoised US image \(U^{(d)}(x,y)\) with original ultrasound medical image U(x,y).The coefficients of different blocks fuse together by selection of fusion rules and levels in wavelet for each block. Wavelet level select and fusion type are selected based on the properties of object strength present in the blocks. The object strength parameter is edge strength (Gao and Bui 2005) of each denoised block.

Adjacent blocks are checked to select the fusion level and fusion rule from a set of five fusion levels and eight fusion rules in wavelet domain.

Testing of the proposed de-noising method to remove multiplicative speckle from the onsite ultrasound medical images procured from AMMA hospital radiology department. The fetus images are obtained in consultation with their doctors by agreeing upon all legal matters as per the constitution of government of India

## Results and discussion

Testing of the proposed method for speckle reduction on ultrasound medical images has to be accomplished by measuring the visual excellence. The parameters that are trusted with this job are peak signal to noise ratio (PSNR), normalized cross correlation coefficient (NCC), edge strength, image quality index (IQI), and structural similarity index (SSIM) (Yu et al. 2001; Lanzolla et al. 2011; Wang and Bovik 2002; Wang et al. 2004).The following popular denoising algorithms from literature that are most likely used for speckle reduction are anisotropic diffusion (AD) (Farias and Akamine 2012), Total Variational Filter (TVF) (Yoon et al. 2012) and Empirical mode decomposition (EMD) (Hu and Jacob 2012). Our proposed algorithm is tested against these techniques both visually and measurably.

For experimentation on ultrasound medical images are procured from two hospitals in Vijayawada, Andhra Pradesh, India. They are AMMA hospitals and NRI medical college hospital. The doctors are consulted and legal agreements are signed as per Indian constitution for sharing ultrasound fetal medical information. And more over doctors helped in detecting and gauging the visual quality of the proposed method with remaining filtering methods in extracting the information from the ultrasound scans. Time for information extraction from filtered images is noted to find the importance of applying this method for clinical application.

Similarly, block soft thresholding (BST) reduces the speckle using eq’n (8) applied of detailed coefficients of each block. Figure 3c, d provides results of soft thresholding. Observing the resultant images in Fig. 3, show a smooth variation among pixels in case of soft thresholding compared to hard thresholding. Compared with original US images, visual improvement is noticeable in the resulting images. But, the trained doctors at AMMA hospital did not show much of an interest in the images of Fig. 3. The reason is blurring of objects of interest resulted due to higher thresholds of wavelet coefficients and not much of a difference observed for lower thresholds. A suggestion from the practicing doctors of US imaging is to improve contrast between the object of interest and the remaining portions of the image so that they can have better visual information and can help faster detection. Their point was to high light the object of interest region with less noise removal and smoothing the remaining portions resulting in a high contrast US image. Hence the second fold processing on the block thresholded denoised images is initiated.

As in Fig. 4 the 1st \(64\times 64\) block does not have edges and hence select level 5 wavelet decomposition (L = 5) with minimum value coefficients from approximate and detailed components for fusion. From Fig. 4 it can be seen that the object of interest occupies block (2, 2). The mean histogram values with neighboring blocks for this block are very high (0.983, 0.958). The information seems important to the user and hence L = 1 and approximate components from original Ultrasound image and maximum of details by comparing both the original US and Denoised US images. It is clearly observable that compared to denoised image the fusion based denoised image gives object of interest clarity.

Visually the two fold denoised images preserve objects and show good contrast between the object boundaries and the rest of the image. Figure 6 shows the denoising methods for block sizes 32, 16 and 8.

Block no | \((N_{\epsilon }^b,N_{\theta }^b)\) | Level | Fusion rule |
---|---|---|---|

1 | 0.181,0.102 | 5 | \(F(A_{min},D_{min})\) |

2 | 0.399,0.322 | 5 | \(F(A_{min},D_{min})\) |

3 | 0.229,0.213 | 5 | \(F(A_{min},D_{min})\) |

4 | 0.182,0.101 | 5 | \(F(A_{min},D_{min})\) |

5 | 0.976,0.979 | 1 | \(F(A_{min},D_{min})\) |

6 | 0.958,0.950 | 1 | \(F(A_{min},D_{min})\) |

7 | 0.949,0.922 | 2 | \(F(A_{img1},D_{min})\) |

8 | 0.637,0.620 | 3 | \(F(A_{min},D_{min})\) |

9 | 0.425,0.433 | 5 | \(F(A_{min},D_{min})\) |

10 | 0.543,0.521 | 4 | \(F(A_{min},D_{min})\) |

11 | 0.523,0.532 | 4 | \(F(A_{min},D_{min})\) |

12 | 0.282,0.221 | 5 | \(F(A_{min},D_{min})\) |

13 | 0.388,0.342 | 5 | \(F(A_{min},D_{min})\) |

14 | 0.422,0.431 | 5 | \(F(A_{min},D_{min})\) |

15 | 0.412,0.412 | 5 | \(F(A_{min},D_{min})\) |

16 | 0.199,0.195 | 5 | \(F(A_{min},D_{min})\) |

Quality metrics for test images in Fig. 2 for two fold techniques for various block

US TEST IMAGES Fig. 2 | PSNR | NCC | ES | IQI | SSIM |
---|---|---|---|---|---|

SOFT 81(S81) | 25.7301 | 0.9604 | 0.5286 | 0.8303 | 0.7421 |

SOFT 82(S82) | 33.6710 | 0.9335 | 0.5342 | 0.8287 | 0.7550 |

SOFT 83(S83) | 28.0827 | 0.9186 | 0.8909 | 0.7659 | 0.6957 |

SOFT 84(S84) | 23.2421 | 0.9526 | 0.5447 | 0.8615 | 0.7734 |

SOFT 161(S161) | 31.5440 | 0.9638 | 0.6297 | 0.7742 | 0.7449 |

SOFT 162(S162) | 32.5186 | 0.9406 | 0.6301 | 0.7822 | 0.7666 |

SOFT 163(S163) | 40.1493 | 0.9302 | 0.9992 | 0.8414 | 0.8938 |

SOFT 164(S164) | 22.7073 | 0.9535 | 0.6405 | 0.8022 | 0.7587 |

SOFT 321(S321) | 25.4860 | 0.9734 | 0.8221 | 0.7058 | 0.7948 |

SOFT 322(S322) | 31.3533 | 0.9578 | 0.8110 | 0.7113 | 0.8135 |

SOFT 323(S323) | 33.1039 | 0.9476 | 0.9825 | 0.7177 | 0.7343 |

SOFT 324(S324) | 25.2114 | 0.9605 | 0.8035 | 0.7157 | 0.7791 |

SOFT 641(S641) | 30.3625 | 0.9788 | 0.9604 | 0.6544 | 0.7988 |

SOFT 642(S642) | 39.5023 | 0.9814 | 0.8568 | 0.7480 | 0.9029 |

SOFT 643(S643) | 30.1731 | 0.9489 | 0.9717 | 0.7089 | 0.7249 |

SOFT 644(S644) | 38.5144 | 0.9742 | 0.8212 | 0.7365 | 0.8299 |

HARD 81(S81) | 39.2534 | 0.9702 | 0.5146 | 0.8841 | 0.7983 |

HARD 82(S82) | 30.4953 | 0.9452 | 0.5210 | 0.8811 | 0.8054 |

HARD 83(S83) | 27.0829 | 0.9394 | 0.8403 | 0.8655 | 0.7741 |

HARD 84(S84) | 29.9035 | 0.9604 | 0.5338 | 0.9164 | 0.8263 |

HARD 161(S161) | 40.2058 | 0.9726 | 0.6144 | 0.8048 | 0.7962 |

HARD 162(S162) | 30.6303 | 0.9520 | 0.6286 | 0.7955 | 0.7996 |

HARD 163(S163) | 28.7951 | 0.9431 | 0.9561 | 0.8211 | 0.7722 |

HARD 164(S164) | 34.4221 | 0.9665 | 0.6422 | 0.8422 | 0.8188 |

HARD 321(S321) | 32.8812 | 0.9713 | 0.8286 | 0.6834 | 0.7855 |

HARD 322(S323) | 28.8381 | 0.9510 | 0.8284 | 0.6810 | 0.7973 |

HARD 323(S324) | 37.5740 | 0.9434 | 0.9934 | 0.7719 | 0.7752 |

HARD 324(S324) | 33.4592 | 0.9658 | 0.8034 | 0.7441 | 0.8179 |

HARD 641(S641) | 43.0055 | 0.9711 | 0.9187 | 0.6183 | 0.7809 |

HARD 642(S642) | 28.5056 | 0.9566 | 0.9219 | 0.8497 | 0.9388 |

HARD 643(S643) | 29.7116 | 0.9514 | 0.9383 | 0.8129 | 0.8125 |

HARD 644(S644) | 29.7116 | 0.9616 | 0.9419 | 0.6863 | 0.8091 |

From the plots in Fig. 7, PSNR for the proposed two fold techniques give mixed results. The first two test images from Fig. 2a, b are from the same patient with a one minute delay in image capture. In the graph of Fig. 7a there is a 100 % domination of ANBF-HT and it gives good PSNR of around 40db at block sizes 8 and 16. For higher block sizes PSNR falls, but under acceptable levels. Coming to Fig. 2b and its PSNR plot in Fig. 7b, there is 50 % domination by the two methods ANBF-HT and ANBF-ST. But ANBF-HT is a clear winner at higher block sizes.

The above results point towards the characteristics of speckle in real time ultrasound imaging. The reason for variations in block sizes for hard and soft thresholding is in the object structure in the US image. Figure 2a, b, d have good solid edge boundary compared to Fig. 2c. As the two fold technique adaptively selects edge blocks for fusion, hard threshold dominates for preserving sharp discontinuities. Figure 2c is having smooth variation of pixels and hence the PSNR is dominant for soft thresholding (ANBF-ST) as in Fig. 7c. Figure 8a–d provides plots of NCC, ES, IQI and SSIM for the proposed two fold denoising methods.

NCC (Normalized Cross Correlation) is the figure telling the relativity of the denoised image with original US image. Figures close to 1 indicate high correlation. Fig. 8a–d shows a constant NCC value over the entire range of methods and block sizes. This shows that the objects in image are intact after denoising. Edge Strength (ES) is increasing with increase in block size. The reason this characteristic of ES is the presence of thick edges in the original image occupying more than 8 or 16 pixels. IQI (Image Quality Index) falls with block size increase in all the cases and at times fluctuating rapidly between blocks and thresholds as in Fig. 8c. Except for Fig. 8c, SSIM is fairly constant. Figure 2c has smooth edges which are difficult to structure out from the object and hence good SSIM.

The most famous denoising algorithms of recent times for ultrasound image denoising are Anisotropic Diffusion (AD) and Total Variational Filtering (TVF). Also included a recently proved technique for denoising is Empirical Mode Decomposition (EMD).

Let us compare our proposed algorithm with these already proved techniques for denoising. The only drawback these methods face are from their dependence on gradient and number of iterations to reach the gradient image to preserve edges while denoising. Figures 9 and 10 are competitive images of the proved techniques AD, TVF and EMD with the proposed two fold techniques ANBF-HT with block size 16 and ANBF-ST with block size 8, for two test images in Fig. 2a, c.

Figure 11a has PSNR distributions on the test images in Fig. 2 for proposed two fold techniques and the standard US denoising methods. The two fold methods lost it on PSNR compared to anisotropic diffusion (AD). Two fold techniques are showing better PSNR with respect to TVF and EMD. Figure 11b–e plots of NCC, ES, IQI and SSIM for individual test images. Close observations of the plots reveal the two fold techniques object boundary preservation compared to other models. Total variational filtering is the only method that protects object boundaries during denoising.

From Fig. 12, block size 8 based denoising methods with either HT or ST executes for 82 s. Block 16, block 32 and block 64 execute for an average of 40 sec, 20 sec and 9 sec respectively. AD and TVF are iterative gradient dependent methods and hence took 88 and 98 s for 40 iterations. Good denoised US images are generated by having a large number of iterations, which in turn slows the execution process. Same is the case with EMD.

## Conclusion

This paper proposes a twofold processing algorithm to reduce multiplicative speckle noise in ultrasound medical images for better visual quality. First fold reduces noise with wavelet block based thresholding, which affects image object boundaries and texture, thereby reducing the visual quality of objects in the image. The second fold restores object boundaries and texture from original ultrasound image through wavelet block fusion. Fusion rules and wavelet decomposition level selection between blocks of original US and threshold denoised US image is achieved using gradient histogram based Normalized Differential Mean (NDM) valve for adjacent blocks. Object blocks having boundary and texture are restored at lowest level from original US image and non-object regions from thresholded US image from the first fold. Hard and soft wavelet thresholding methods are incorporated in the first fold. The two fold methods are Adaptive Normalized Diffusion Mean Block Fusion - Hard Thresholding (ANBF-HT) and Adaptive Normalized Diffusion Mean Block Fusion - Soft Thresholding (ANBF-ST) for different block sizes. Four different block sizes are selected for testing such as 8, 16, 32 and 64 for both thresholding and fusion. The results are encouraging for clinical application, when compared to other popular methods. Quality metrics show a high degree of relativity with existing proven techniques for ultrasound image denoising such as anisotropic diffusion, total variational filtering and empirical mode decomposition.

## Notes

## Declarations

### Authors’ contributions

Substantial contributions to the conception or design of the work, ideas, theory formulation and mathematical analysis PVVK. The Ultrasound Image Acquisition KVVK. First fold analysis—MVDP, KVVK, DAK, RR. Second fold analysis—KVVK, CBSVK, EGD, YS. Result formulations—Dr. PVVK. Testing and plotting—KVVK, MVDP, DAK Interpretation of data for work—PVVK. Drafting the work and revising it critically for important intellectual content—Dr. PVVK. All authors read and approved the final manuscript.

### Acknowledgements

We thank Amma Hospital radiology department for providing Ultrasound images for analysis and helping in determining the quality of processed images for evaluation.

### Competing interests

The authors declare that they have no competing interests.

**Open Access**This article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made.

## Authors’ Affiliations

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