- Email: [email protected]
Speckle Noise Reduction of Ultrasound Images Using BFO Cascaded with Wiener Filter and Discrete Wavelet Transform in Homomorphic Region
Available online at www.sciencedirect.com
ScienceDirect
Available online at www.sciencedirect.com Procedia Computer Science 00 (2018) 000–000
ScienceDirect
www.elsevier.com/locate/procedia
Procedia Computer Science 132 (2018) 1543–1551
International Conference on Computational Intelligence and Data Science (ICCIDS 2018)
Speckle Noise Reduction of Ultrasound Images Using BFO Cascaded with Wiener Filter and Discrete Wavelet Transform in Homomorphic Region Rajeshwar Dass
Department of Electronics and Communication Engineering Deenbandhu Chhotu Ram University of Science & Technology, Murthal, Sonepat-(India) Abstract
Speckle noise removal is a key issue in ultrasound image processing used for getting important diagnostic information for human body. Speckle noise degrades the visual evaluation of ultrasound images. The main challenge of despeckling is to preserve all the fine details and the edges of the ultrasonographic images. From various speckle removable methods, there is a type of method which converts the multiplicative behaviour of speckle noise in to additive by using log transform. The additive noise removal is easy as compared to multiplicative noise. Here a new approach for denoising of highly distorted images affected by speckle noise is proposed. The proposed method is realized using bacterial foraging optimization (BFO) cascaded with wavelet transform and wiener filter in a homomorphic framework. The wavelet packet decomposition is used to identify and remove the noise from affected pixels. Wiener filter is used for pre-processing purpose. The BFO algorithm is used to reduce the amount of error between the speckled image and the Despeckled output image from homomorphic framework after processing. It is used to maintain the finer details and the error percentage considered is 0.0001.The proposed technique provides superior results in comparison to other techniques in form of peak signal to noise ratio (PSNR), Mean Absolute Error(MAE), clarity andpreservation of finer details. © Published by by Elsevier Elsevier Ltd. © 2018 2018 The The Authors. Authors. Published B.V. This is an open access article under the CC BY-NC-ND license (https://creativecommons.org/licenses/by-nc-nd/3.0/) Peer-review under responsibility of the scientific committee of the International Conference on Computational Intelligence and Peer-review under responsibility of the scientific committee of the International Conference on Computational Intelligence and Data Science Science (ICCIDS (ICCIDS 2018). Data 2018). Keywords:BFO,
Homomorphic framework, peak signal to noise ratio, speckle noise, wiener filter,wavelet transform.
1. Introduction A number of imaging methods are available in market for the diagnosing purpose. Ultrasound (US) imaging is one of them and it is popular because of it’s features: harmless for the human body as well as it is cost effective, portrait, non-invasive and non-radiant in nature. The acoustic waveforms are used in US imaging modality. The reflected Corresponding author: E-mail: [email protected] 1877-0509© 2018 The Authors. Published by Elsevier B.V.
Peer-review under responsibility of the scientific committee of the International Conference on Computational Intelligence and Data Science (ICCIDS 2018). 1877-0509 © 2018 The Authors. Published by Elsevier Ltd. This is an open access article under the CC BY-NC-ND license (https://creativecommons.org/licenses/by-nc-nd/3.0/) Peer-review under responsibility of the scientific committee of the International Conference on Computational Intelligence and Data Science (ICCIDS 2018). 10.1016/j.procs.2018.05.118
1544
Rajeshwar / Procedia Computer Science 132000–000 (2018) 1543–1551 Rajeshwar Dass/Dass Procedia Computer Science 00 (2018)
signals returned from the heterogeneities of the patient under inspection are combined to build the US image. These reflected signalscome back with variable phases and amplitude results increase in interference pattern which is recognized as speckle noise. Speckle noise mask the important detail required for diagnosing purpose[1]. It is observed in literature that the speckle noise creates difficultyfor detection of lesion with a factor of eight[2].So despeckling is an important task for US image processing. Various methods are available for despeckling in literature [3-5]. The various methods available in literature remove such type of noise at the cost of blurring the image detail. Jain[6] proposed a technique for speckle noise removal where multiplicative speckle model is changed to an additive model by taking log transform of the image.After this additive noise is removed using wiener filter. In the last, the exponential transformation istaken to obtain the speckle free image. By inspiringfrom the better results using wavelet transformsfor despeckling of US images,variousauthors haveproposed wavelet based novel techniques for despeckling ofUS images[7-12]. In [7] Oleg.V.Michailorich proposed a homomorphic approach with assumptions that the noise in the log transform domain lookssameas that of white Gaussian noise. Wavelet lifting for despeckling is proposed in [8]. This technique uses wavelet transform and cross-validation thresholding techniques. In [9] Achim et al. gave an homomorphic technique.Here the US
images are denoised by mean of Bayesian estimator. In[10] the authors utilized Gaussian distribution for signal coefficient modelling and atechnique for compression and speckle noise removal proposed.In [11] an adaptive pre-
processing filter used for approximation of the behaviour of speckle noise with white Gaussian noise. In [12] Arash Vosoughi proposed a despeckling method by making the use of M band DWT and wiener filter by employing adaptive thresholding based on weighted variance method for speckle noise suppression.Recently various furtherstudies have been done by scholars for despeckling of US image [13-19]. In this paper the multiplicative noise (speckle)first converted into the additive type by taking log transform of the original image. Log transformed image is passed through wiener filter and nonlinear filtering of wavelet packet
coefficients done. The output of log transformed image is given to wiener filter input. Wiener filter’s output is passed through the discrete wavelet for packet decomposition and at the same time other image is obtained by subtracting the wiener filter’s output image from the log transformed image. The resultant output image from subtractor is also passed through discrete wavelet transforms. By adding the resultant images, exponential transformation applied, the obtained result is passed through the BFO.To test the proposed method,the speckled US images with5 to 30%noise densities applied as input.The result shows that the proposed technique filters the speckle noise from US images and thus gives better PSNR and least MAEat high level of noise density also. The proposed method’s results compared with other existing speckle denoising methods and it was found that proposed methodology gives better results and also takes care of the edge information in the image. The remaining part of the paper is organised as follow; section 2 describes homomorphic framework and speckle noise model, Section 3 illustrates the BFO. In section 4, an illustration of proposed technique is elaborated. In section 5 experimental results and discussion is given.In the last the conclusion & future scope is presented at section 6. 2. Homomorphic framework and speckle noise model Assume that
is the noised image and the
is the original US image. Then according to Jain[6] the
Rajeshwar DassProcedia / Procedia Computer Science 132 (2018) 1543–1551 Rajeshwar Dass/ Computer Science 00 (2018) 000–000
general image is represented as : Where
shows multiplicative noise component.
15453
(1) is additive noise. In US imaging the
consequences of additive noisesare considered very small incomparison to the multiplicative noise components. So is considered as zero. Here the multiplicative noise is called as speckle noise. The presence of speckle noise hinders a human observer from analyzing the ultrasound image. The one source of speckle is interference of scattered signal which is caused by tissue imhomoginety. The other sources includes the type of probe used, the part of body imaged and the discontinuities caused by body disease[20]. It is very difficult to remove the multiplicative noise. It can be converted to additive one by using homomorphic framework. This is well known illumination reflection model[21] proposed by Stockham in 1972. In this framework the logarithmic transform as shown in equation 2 and exponential operations are followed sequentially to yield a suitable version of original image. (2) is known as speckle noise can be removed by using different standard noise removing Now this techniques on this logarithmic function. After noise removal, inverse log transform function is applied to get noise free image of the noisy observations. This process of denoising is also known as homomorphic filtering. Here in this paper wavelet packet decomposition&wiener filtering cascaded with BFO to reduce the speckle noise. 3. Bacterial foraging optimization (BFO) Generally, in engineering design, thefocus remains onoptimization of the desired goal. For optimization of solution, the algorithms used are known as optimization algorithms. The BFO is one of the optimization algorithm given by K.M Passino[22]. BFO uses Escheria Coli bacterium cell’s foraging strategies[23]. The E. Coli bacterium contains a plasma membrane, cell wall & capsules which have the cytoplasm and nucleoid. The E. Coli bacteria have a foraging strategy and it is followed by four steps: Swimming & Tumbling (Chemotaxis), Swarming, Reproduction & Elimination and Dispersal. An E.Coli bacterium has the capability to move by two ways i.e by swimming or by tumbling[23]. The search strategy may be of any type out of cruise, salutatory and ambush search. Swimming takes place for fixed distance and then to change the direction, tumbling is done.One tumble and swim forms one chemotaxis step. When the concentration is more at next step then they progressone more step,else tumbling for searchingmore quantity of food in other directions starts. The nature of searching depends on the location of the bacteria. The bacteria may be in neutral substance, non gradient serine or in serine substance. The chemotaxis steps goes on up to the end of the bacteria. The life of the bacteria is defined by the nutrients consumption and the other influencing processes. At the end of the life time of bacteria which has gathered good health divides into two cells. For next reproduction step,further bacteria generation starts from a healthy position and other half eliminated. This causes the strength of bacteria to remain constant. This behaviour of E. Coli bacterium has inspired researchers to use it for optimization process. 4. Proposed method
Rajeshwar Dass / Procedia Computer Science 132 (2018) 1543–1551 Rajeshwar Dass/ Procedia Computer Science 00 (2018) 000–000
1546
In this proposed technique, the multiplicative nature of speckle noise is converted in to additive nature by using equation 2[21]. First, an ultrasonographic image is taken.It was corrupted by speckle of varied density and a noisy image f(x, y) is created as shown in fig. 1. In second step, the log transform of the observation image is taken say resultant image is f1. Now in third step f1 is passed through the wiener filter to get I1 image.Simultaneously I2 image
produced by using (f1-I1). The wiener filter is more efficient in removal of speckle noise[24] but its performance decreses at brighter areas.Now pass both the images (I1& I2) through Discrete Wavelet Transform (DWT) for
wavelet packet decomposition. In the next step speckle is suppressed from decomposed wavelet packets of images I1& I2by using adaptive soft thresholding method. Note that image I1 which is output of wiener filter have information about signal while I2 gives the more idea about noise. That is why adaptive type of thresholding
function (bayes shrink) [12] is used here which depends upon the density of noise and the signal. Now inverse of DWT is applied to these denoised images and
images are obtained as shown in figure 1. Both
images are added and passed through the antilog filter so thatimage g(x,y)is obtained. f x, y Speckled Image
LOG
f1
I1
Wiener filter
DWT(packet decomposition)
Soft thresholding
IDWT
g x, y
Iˆ1 EXP
BFO
Despeckled Image
DWT(packet decomposition)
Soft thresholding
IDWT
Iˆ2
I2 Fig. 1. Basic Block Diagram of Proposed Methodology
In next stage to remove the error between original US image and noisy US image BFO is used. This optimization technique minimizes the mean square error (MSE) between recovered US image g(x,y) and the noisy US image.
The Mean Square Error as given in [25] is considered as cost function for BFO to optimize Peak Signal to Noise
Ratio(PSNR). The different parameters selected for BFO are : 1) Total bacteria utilized = RowX Column(image).
2) Total iterationsused for chemotaxisprocess = 3 3) Total Swimming steps utilized = 2 4) Total reproduction used = 2
5) Total elimination and dispersalused = 2
6) Elimination and dispersal’s probabilityconsidered = 0.25 The overall performance of proposed method is calculated on the basis of performance evaluation parameters i.e mean absolute error (MAE) and peak signal to noise ratio (PSNR)[27-28].
Rajeshwar Dass/ Procedia Computer Science 00 (2018) 000–000 Rajeshwar Dass / Procedia Computer Science 132 (2018) 1543–1551
5 1547
5. Experimental results & discussions The proposed method tested with various ultrasound images. To analyse the performance, the actual original ultrasound images have been contaminated with different speckle noise densities. The outcomes of proposed method have been compared with results of some available techniques used for despeckling i.e Adaptive MedianFilter (AMF)[6], Wiener filter[26], DWT & Wiener filter in homomorphic region[12] and Bacterial Foraging Optimization with Adaptive Median (BFO & AMF) filter[25] with varying noise density from 5 to 30% as shown in table 1. The adaptive median filter taken for comparasion with proposed method because it works better as compared to other filters for speckle removal [29] while the wiener filter taken for comparision due to its preservation property of resolution of US images [24] with speckle removal. The combination of DWT and wiener filter gives better results in form of information preservation of the image during the noise removal from US images[30]. The combination of BFO and AMF gives better visual and statistical results[28]. Table 1 shows the values of performance analysis parameters i.e PSNR and MAE of all the techniques used for speckle noise removal from US images of varying noise densities from 10% to 30%. Table 2 shows the comparative results of proposed method with DWT & Wiener filter in homomorphic frameworkfor US Image 2 of liver in from of PSNR. it is clear from table 1 that proposed method gives better results in form of PSNR and MAE while table 2 gives the idea about enhancement in PSNR due to proposed method in comparision with DWT & Wiener filter in homomorphic framework. This shows that the BFO enhances the performance of DWT & Wiener filter in homomorphic framework. The results of proposed method were also compared with BFO & AMF technique as shown in table 3. It was observed that proposed method gives better results in form of PSNR and MAE. Table 1. Comparison of PSNR & MAE of proposed technique with available methods for an Ultrasound Image2 of liver Filter PSNR MAE AMF
σ =0.1 20.42
σ =0.2 17.69
σ =0.3 16.17
σ =0.1 0.0648
σ =0.2 0.0877
σ =0.3 0.1042
Wiener Filter
22.56
19.89
18.19
0.0491
0.0638
0.0767
DWT & Wiener filter
23.76
21.03
18.29
0.0167
0.0218
0.0300
BFO & AMF
68.22
65.98
64.51
0.0674
0.0861
0.1002
Proposed Method
78.68
74.24
70.45
0.0195
0.0342
0.0541
In homomorphic region
Table 2.Comparative Result of Proposed technique with DWT & Wiener filter in Homomorphic Frameworkfor an US Image 2 of liver Noise Density(%)
PSNR (DWT & Wiener Filter in Homomorphic Framework)
PSNR (Proposed method)
MAE (DWT & Wiener Filter in Homomorphic Framework)
MAE (Proposed method)
Enhancement of PSNR(dB)
05
25.517
81.161
0.0143
0.0137
55.644
10
23.759
78.688
0.0167
0.0195
54.929
15
22.548
75.952
0.0200
0.0270
53.404
20
21.032
74.244
0.0218
0.0342
53.212
25
19.697
72.215
0.02156
0.0438
52.518
30
18.289
70.458
0.0300
0.0541
52.169
Figure2(a) & (b) shows the graphical representation of the comparative results in form of PSNR and MAE respectively.
Rajeshwar Dass/ Procedia Computer Science 00 (2018) 000–000 Rajeshwar Dass / Procedia Computer Science 132 (2018) 1543–1551
1548
0.11
90
0.1
80
0.09 70
0.08 proposedmethod BFOAMF AMF wiener dwtwienerhomorphic
50 40
0.07 MAE
PSNR
60
proposedmethod BFOAMF AMF wiener dwtwienerhomorphic
0.06 0.05 0.04
30
0.03 20 10 0.05
0.02 0.1
0.15 0.2 Noisevariance
0.25
0.3
0.01 0.05
2(a)
0.1
0.15 0.2 Noisevariance
0.25
0.3
2(b)
Fig. 2. (a)Comparison results in PSNR of various algorithms for ultrasound image1(foetus); (b)Comparison results in MAE of various algorithms for ultrasound image 2(liver). Table 3. Comparative Results of Proposed Technique with BFO &AMF for an US Image 2 of liver Noise PSNR PSNR MAE MAE Density( (BFO & AMF) (Proposed Method) (BFO & AMF) (Proposed Method) %)
Enhancement of PSNR (dB)
05
70.544
81.161
0.0503
0.0137
11.017
10
68.224
78.688
0.0674
0.0195
10.464
15
66.982
75.952
0.0774
0.0270
8.97
20
65.984
74.244
0.0861
0.0342
8.26
25
64.869
72.215
0.0990
0.0438
7.346
30
64.506
70.458
0.1002
0.0541
5.952
The original foetus and liver images are shown in figure 3(a) and respective corrupted US images with 30% noise density are shown in fig.3(b). The Despeckled US images using various methods are shown in figure 3(c)-(g). Figure 3 (c) shows the output US despeckled images due to AMF filter. Figure 3(d) shows the output despeckled US images due to Wiener filter while figure 3(e) shows the output despeckled US images when DWT & wiener filter in homomorphic region were used for speckle noise removal. figure 3(f) shows the output despeckled US images of BFO & AMF filter while figure 3(g) shows the output despeckled US images when proposed method is used for despeckling of respective US images. It is clear from the table 1, 2, 3 and figures 3(c)-(g) that proposed method gives better results statistically as well as visually.
Rajeshwar Dass / Procedia Computer Science 132 (2018) 1543–1551 Rajeshwar Dass/ Procedia Computer Science 00 (2018) 000–000
Fig. 3. (a) Ultrasound images (image 1 is of foetus & image 2 is of liver
Fig. 3. (c) Adaptive median filter(AMF) output
Fig. 3 (e) DWT & wiener filter in Homomorphic region output
Fig 3(g) Restored images using Proposed method
Fig. 3. (b) Images with 30% noise density
Fig. 3. (d) Wiener filter output;
Fig. 3 (f) BFO&AMF output
1549
7
1550
Rajeshwar Dass / Procedia Computer Science 132 (2018) 1543–1551 Rajeshwar Dass/ Procedia Computer Science 00 (2018) 000–000
6. Conclusion and Future Scope This paper, presented a new technique to despeckle the ultrasound images. Proposed method gives recognizable and acceptable restoration of ultrasound images in presence of speckle noise with varied noise densities from 5% -30%. The visual test reports were also generated with statistical performance evaluation parameters for approval from radiologists/experts. The comparison of the results obtained with other despeckling techniques shows that the proposed technique performs better as compared to other existing techniques, statistically as well as visually. The proposed method have some limitations also. The performance of the system depends upon the parameter selection of BFO algorithm. In this paper only PSNR and MAE parameters taken for performance evalution of the proposed method. In future other parameters may be calculated. The performance of the proposed method starts decreasing as the density of speckle noise increses in the image. Thus it is still a challenge to despeckle highly distorted US images. 7. References
[1] Saraswathi J.S, and Mary Brinda (2011)“Denoising of mixed noise in ultrasound images.”IJCSI8 (4): 517-523. [2] J.C Bamber and C. Daft (1986)“Adaptive filtering for reduction of speckling ultrasonic pulse echo images.”ultrasonics:41-44. [3] T.Loupas,W.N Mcdiken, and P.L Allan (1989) “An adaptive weighted median filter for speckle suppression inmedical ultrasonic images.’’IEEE Trans. circuits and systems36:129-35. [4] J.S Lee and I.Jukevich (1994)“Speckle filtering of synthetic aperture radar images:A review.”Remote sensing Reviews8: 313-340. [5] Rajeshwar Dass, Swapna Devi and Priyanka (2011)“Speckle noise reduction techniques.’’International Journal of Electronics and Electrical Engineering(IJEEE) 16(1): 47-57. [6] A.K.Jain(1989) “Fundamental of digital image processing.”Prentice Hall. [7] Oleg V. Michailovich and Allen Tannenbaum(2006)“Despeckling of medical ultrasound images.” IEEE Trans. Ultrasonic’s, Ferroelectrics and frequency control 53(1). [8] Yuan Chen, and Amar Reheja(2005)“Wavelet lifting for speckle noise reduction in ultrasound images.”IEEE, Proceedings Engineering in Medicine and Biology, Shanghai, China. [9] A. Anchim, P. Tsakalides, and A. Bezarianos (2001)“Novel bayesian multiscale method for speckle removal in medical ultrasound images.” IEEE Trans. on Medical Imaging20:772-783. [10] N.Gupta, M.N.S. Swamy, and E.I. Plotkin (2005) “Despeckling of medical ultrasound images using data and rate adaptive lossy compression.” IEEE Trans. on Medical Imaging24:743-754. [11] R.K. Mukkavilli, J.S. Sahambi and P.K. Bora (2008) “Modified homomorphic wavelet based despeckling of Medical Ultrasound Images.“ IEEE Trans:887-890. [12] ArashVosoughi and Mohammad.B. Shamsollahi (2008)“Noise reduction of ultrasound images using M-band wavelet transform and wiener filter in a homomorphic framework.” IEEE Trans BMEI:510-515. [13] J. Huang, and X. Yang(2013) “Fast reduction of speckle noise in real ultrasound images.” Signal Processing 93(4): 684-694. [14] D. Gupta, R. S. Anand, and B. Tyagi (2014) “Speckle filtering of ultrasound images using a modified non-linear diffusion model in nonsubsampled shearlet domain” IET Image Processing 9(2):107-117. [15] D. Gupta, R. S. Anand, and B. Tyagi (2015) “Despeckling of ultrasound medical images using ripplet domain nonlinear filtering .” Signal, Image and Video Processing 9(5):1093-1111. [16] H. H. Choi, J. H. Lee, S. M. Kim, and S. Y. Park (2015) “Speckle noise reduction in ultrasound images using a discrete wavelet transformbased image fusion technique” Bio-Medical Materials and Engineering 26(1):1587-S1597. [17] J. Yang, J. Fan, D. Ai, X. Wang, Y. Zheng, S. Tang, and Y. Wang (2016) “Local statistics and non-local mean filter for speckle noise reduction in medical ultrasound image.” Neurocomputing 195: 88-95. [18] P. V. Sudeep, P. Palanisamy, J. Rajan, H. Baradaran, L. Saba, A. Gupta, and J. S. Suri (2016) “Speckle reduction in medical u ltrasound images using an unbiased non-local means method” Biomedical Signal Processing and Control, 28: 1-8. [19] Nagashettappa Biradar, Mohan Lal Dewal and Manoj Kumar Rohit (2015) “Speckle noise reduction in B-made echocardiographic images: A comparision.” IET Technical Review, Taylor & Francis:1-19. [20] S.Chang, B.Yu and M.Vetterli (2000)“Adaptive wavelet thresholding for image denoising and compression.” IEEE Trans. Image Processing9: 1532-1546. [21] Nusrat Ahmed Surobhi and Md. Ruhul Amin (2006) “employment of modified homomorphic filters in medical imaging. ”Daftdil International University Journal of science and Technology1(1). [22] Y.Liu, K.M Passino and M.A Simaan (2002)“Biomimiray of Social Foraging Bacteria for distributed optimization: Models. Principles and Emergent Behaviors.” Journal of optimization theory and applications115(3):603-28. [23] K.M Passino (2002)“Biomimiray of bacterial foraging for distributed optimization and control.”IEEE control systems magazine: 53-67. [24] Tanpia Tasnim, Md. Maruf Hossain Shuvo, and Sabbir Hasan (2017) “Study of Speckle Noise Reduction from Ultrasound B-mode Images using Different Filtering Techniques.” International Conference on Advances in Electrical Engineering, Dhaka, Bangladesh: 229-234. [25] K.M Backwad, S.S Pattnaik, B.S Sohi, S.Devi, B.K Panigrahi, Sastri and V.R.S Gollapudi(2009)“Bacterial foraging optimization technique cascaded with adaptive filter to enhance peak signal to noise ratio from single image.”IETE Journal of Research55(4) :173-179. [26] Cris Solomon and Toby Breekon (2011) “Fundamental of digital image processing.” Willey Blackwell, U.K. [27] Rajeshwar Dass, Swapna Devi, Priyanka and Sunil Kumar(2012)“Image Quality Assessment Techniques.” National Conference on Advances in Video, Cyber Learning and Electronics, NITTTR Chandigarh:234-240. [28] Rajeshwar Dass, Swapna Devi and Priyanka (2012)“Effect of Wiener-Helstrom Filtering Cascaded with Bacterial Foraging Optimization to Despeckle the Ultrasound Images.”IJCSI International Journal of Computer Science 4(2):372-380. [29] Dina Arifatul Khusna, Hanung Adi Nugroho, Indah Soesanti(2015) “Performance analysis of edge and detailed preserved speckle noise
Rajeshwar Dass / Procedia Computer Science 132 (2018) 1543–1551 Rajeshwar Dass/ Procedia Computer Science 00 (2018) 000–000
1551 9
reduction filters for breast ultrasound images.” Int. Conference on Information Technology, Computer and Electrical Engineering, Indonesia:76-80. [30] A.Bovik(2000) “Handbookof image and video processing” New York: Academic.
ScienceDirect
Available online at www.sciencedirect.com Procedia Computer Science 00 (2018) 000–000
ScienceDirect
www.elsevier.com/locate/procedia
Procedia Computer Science 132 (2018) 1543–1551
International Conference on Computational Intelligence and Data Science (ICCIDS 2018)
Speckle Noise Reduction of Ultrasound Images Using BFO Cascaded with Wiener Filter and Discrete Wavelet Transform in Homomorphic Region Rajeshwar Dass
Department of Electronics and Communication Engineering Deenbandhu Chhotu Ram University of Science & Technology, Murthal, Sonepat-(India) Abstract
Speckle noise removal is a key issue in ultrasound image processing used for getting important diagnostic information for human body. Speckle noise degrades the visual evaluation of ultrasound images. The main challenge of despeckling is to preserve all the fine details and the edges of the ultrasonographic images. From various speckle removable methods, there is a type of method which converts the multiplicative behaviour of speckle noise in to additive by using log transform. The additive noise removal is easy as compared to multiplicative noise. Here a new approach for denoising of highly distorted images affected by speckle noise is proposed. The proposed method is realized using bacterial foraging optimization (BFO) cascaded with wavelet transform and wiener filter in a homomorphic framework. The wavelet packet decomposition is used to identify and remove the noise from affected pixels. Wiener filter is used for pre-processing purpose. The BFO algorithm is used to reduce the amount of error between the speckled image and the Despeckled output image from homomorphic framework after processing. It is used to maintain the finer details and the error percentage considered is 0.0001.The proposed technique provides superior results in comparison to other techniques in form of peak signal to noise ratio (PSNR), Mean Absolute Error(MAE), clarity andpreservation of finer details. © Published by by Elsevier Elsevier Ltd. © 2018 2018 The The Authors. Authors. Published B.V. This is an open access article under the CC BY-NC-ND license (https://creativecommons.org/licenses/by-nc-nd/3.0/) Peer-review under responsibility of the scientific committee of the International Conference on Computational Intelligence and Peer-review under responsibility of the scientific committee of the International Conference on Computational Intelligence and Data Science Science (ICCIDS (ICCIDS 2018). Data 2018). Keywords:BFO,
Homomorphic framework, peak signal to noise ratio, speckle noise, wiener filter,wavelet transform.
1. Introduction A number of imaging methods are available in market for the diagnosing purpose. Ultrasound (US) imaging is one of them and it is popular because of it’s features: harmless for the human body as well as it is cost effective, portrait, non-invasive and non-radiant in nature. The acoustic waveforms are used in US imaging modality. The reflected Corresponding author: E-mail: [email protected] 1877-0509© 2018 The Authors. Published by Elsevier B.V.
Peer-review under responsibility of the scientific committee of the International Conference on Computational Intelligence and Data Science (ICCIDS 2018). 1877-0509 © 2018 The Authors. Published by Elsevier Ltd. This is an open access article under the CC BY-NC-ND license (https://creativecommons.org/licenses/by-nc-nd/3.0/) Peer-review under responsibility of the scientific committee of the International Conference on Computational Intelligence and Data Science (ICCIDS 2018). 10.1016/j.procs.2018.05.118
1544
Rajeshwar / Procedia Computer Science 132000–000 (2018) 1543–1551 Rajeshwar Dass/Dass Procedia Computer Science 00 (2018)
signals returned from the heterogeneities of the patient under inspection are combined to build the US image. These reflected signalscome back with variable phases and amplitude results increase in interference pattern which is recognized as speckle noise. Speckle noise mask the important detail required for diagnosing purpose[1]. It is observed in literature that the speckle noise creates difficultyfor detection of lesion with a factor of eight[2].So despeckling is an important task for US image processing. Various methods are available for despeckling in literature [3-5]. The various methods available in literature remove such type of noise at the cost of blurring the image detail. Jain[6] proposed a technique for speckle noise removal where multiplicative speckle model is changed to an additive model by taking log transform of the image.After this additive noise is removed using wiener filter. In the last, the exponential transformation istaken to obtain the speckle free image. By inspiringfrom the better results using wavelet transformsfor despeckling of US images,variousauthors haveproposed wavelet based novel techniques for despeckling ofUS images[7-12]. In [7] Oleg.V.Michailorich proposed a homomorphic approach with assumptions that the noise in the log transform domain lookssameas that of white Gaussian noise. Wavelet lifting for despeckling is proposed in [8]. This technique uses wavelet transform and cross-validation thresholding techniques. In [9] Achim et al. gave an homomorphic technique.Here the US
images are denoised by mean of Bayesian estimator. In[10] the authors utilized Gaussian distribution for signal coefficient modelling and atechnique for compression and speckle noise removal proposed.In [11] an adaptive pre-
processing filter used for approximation of the behaviour of speckle noise with white Gaussian noise. In [12] Arash Vosoughi proposed a despeckling method by making the use of M band DWT and wiener filter by employing adaptive thresholding based on weighted variance method for speckle noise suppression.Recently various furtherstudies have been done by scholars for despeckling of US image [13-19]. In this paper the multiplicative noise (speckle)first converted into the additive type by taking log transform of the original image. Log transformed image is passed through wiener filter and nonlinear filtering of wavelet packet
coefficients done. The output of log transformed image is given to wiener filter input. Wiener filter’s output is passed through the discrete wavelet for packet decomposition and at the same time other image is obtained by subtracting the wiener filter’s output image from the log transformed image. The resultant output image from subtractor is also passed through discrete wavelet transforms. By adding the resultant images, exponential transformation applied, the obtained result is passed through the BFO.To test the proposed method,the speckled US images with5 to 30%noise densities applied as input.The result shows that the proposed technique filters the speckle noise from US images and thus gives better PSNR and least MAEat high level of noise density also. The proposed method’s results compared with other existing speckle denoising methods and it was found that proposed methodology gives better results and also takes care of the edge information in the image. The remaining part of the paper is organised as follow; section 2 describes homomorphic framework and speckle noise model, Section 3 illustrates the BFO. In section 4, an illustration of proposed technique is elaborated. In section 5 experimental results and discussion is given.In the last the conclusion & future scope is presented at section 6. 2. Homomorphic framework and speckle noise model Assume that
is the noised image and the
is the original US image. Then according to Jain[6] the
Rajeshwar DassProcedia / Procedia Computer Science 132 (2018) 1543–1551 Rajeshwar Dass/ Computer Science 00 (2018) 000–000
general image is represented as : Where
shows multiplicative noise component.
15453
(1) is additive noise. In US imaging the
consequences of additive noisesare considered very small incomparison to the multiplicative noise components. So is considered as zero. Here the multiplicative noise is called as speckle noise. The presence of speckle noise hinders a human observer from analyzing the ultrasound image. The one source of speckle is interference of scattered signal which is caused by tissue imhomoginety. The other sources includes the type of probe used, the part of body imaged and the discontinuities caused by body disease[20]. It is very difficult to remove the multiplicative noise. It can be converted to additive one by using homomorphic framework. This is well known illumination reflection model[21] proposed by Stockham in 1972. In this framework the logarithmic transform as shown in equation 2 and exponential operations are followed sequentially to yield a suitable version of original image. (2) is known as speckle noise can be removed by using different standard noise removing Now this techniques on this logarithmic function. After noise removal, inverse log transform function is applied to get noise free image of the noisy observations. This process of denoising is also known as homomorphic filtering. Here in this paper wavelet packet decomposition&wiener filtering cascaded with BFO to reduce the speckle noise. 3. Bacterial foraging optimization (BFO) Generally, in engineering design, thefocus remains onoptimization of the desired goal. For optimization of solution, the algorithms used are known as optimization algorithms. The BFO is one of the optimization algorithm given by K.M Passino[22]. BFO uses Escheria Coli bacterium cell’s foraging strategies[23]. The E. Coli bacterium contains a plasma membrane, cell wall & capsules which have the cytoplasm and nucleoid. The E. Coli bacteria have a foraging strategy and it is followed by four steps: Swimming & Tumbling (Chemotaxis), Swarming, Reproduction & Elimination and Dispersal. An E.Coli bacterium has the capability to move by two ways i.e by swimming or by tumbling[23]. The search strategy may be of any type out of cruise, salutatory and ambush search. Swimming takes place for fixed distance and then to change the direction, tumbling is done.One tumble and swim forms one chemotaxis step. When the concentration is more at next step then they progressone more step,else tumbling for searchingmore quantity of food in other directions starts. The nature of searching depends on the location of the bacteria. The bacteria may be in neutral substance, non gradient serine or in serine substance. The chemotaxis steps goes on up to the end of the bacteria. The life of the bacteria is defined by the nutrients consumption and the other influencing processes. At the end of the life time of bacteria which has gathered good health divides into two cells. For next reproduction step,further bacteria generation starts from a healthy position and other half eliminated. This causes the strength of bacteria to remain constant. This behaviour of E. Coli bacterium has inspired researchers to use it for optimization process. 4. Proposed method
Rajeshwar Dass / Procedia Computer Science 132 (2018) 1543–1551 Rajeshwar Dass/ Procedia Computer Science 00 (2018) 000–000
1546
In this proposed technique, the multiplicative nature of speckle noise is converted in to additive nature by using equation 2[21]. First, an ultrasonographic image is taken.It was corrupted by speckle of varied density and a noisy image f(x, y) is created as shown in fig. 1. In second step, the log transform of the observation image is taken say resultant image is f1. Now in third step f1 is passed through the wiener filter to get I1 image.Simultaneously I2 image
produced by using (f1-I1). The wiener filter is more efficient in removal of speckle noise[24] but its performance decreses at brighter areas.Now pass both the images (I1& I2) through Discrete Wavelet Transform (DWT) for
wavelet packet decomposition. In the next step speckle is suppressed from decomposed wavelet packets of images I1& I2by using adaptive soft thresholding method. Note that image I1 which is output of wiener filter have information about signal while I2 gives the more idea about noise. That is why adaptive type of thresholding
function (bayes shrink) [12] is used here which depends upon the density of noise and the signal. Now inverse of DWT is applied to these denoised images and
images are obtained as shown in figure 1. Both
images are added and passed through the antilog filter so thatimage g(x,y)is obtained. f x, y Speckled Image
LOG
f1
I1
Wiener filter
DWT(packet decomposition)
Soft thresholding
IDWT
g x, y
Iˆ1 EXP
BFO
Despeckled Image
DWT(packet decomposition)
Soft thresholding
IDWT
Iˆ2
I2 Fig. 1. Basic Block Diagram of Proposed Methodology
In next stage to remove the error between original US image and noisy US image BFO is used. This optimization technique minimizes the mean square error (MSE) between recovered US image g(x,y) and the noisy US image.
The Mean Square Error as given in [25] is considered as cost function for BFO to optimize Peak Signal to Noise
Ratio(PSNR). The different parameters selected for BFO are : 1) Total bacteria utilized = RowX Column(image).
2) Total iterationsused for chemotaxisprocess = 3 3) Total Swimming steps utilized = 2 4) Total reproduction used = 2
5) Total elimination and dispersalused = 2
6) Elimination and dispersal’s probabilityconsidered = 0.25 The overall performance of proposed method is calculated on the basis of performance evaluation parameters i.e mean absolute error (MAE) and peak signal to noise ratio (PSNR)[27-28].
Rajeshwar Dass/ Procedia Computer Science 00 (2018) 000–000 Rajeshwar Dass / Procedia Computer Science 132 (2018) 1543–1551
5 1547
5. Experimental results & discussions The proposed method tested with various ultrasound images. To analyse the performance, the actual original ultrasound images have been contaminated with different speckle noise densities. The outcomes of proposed method have been compared with results of some available techniques used for despeckling i.e Adaptive MedianFilter (AMF)[6], Wiener filter[26], DWT & Wiener filter in homomorphic region[12] and Bacterial Foraging Optimization with Adaptive Median (BFO & AMF) filter[25] with varying noise density from 5 to 30% as shown in table 1. The adaptive median filter taken for comparasion with proposed method because it works better as compared to other filters for speckle removal [29] while the wiener filter taken for comparision due to its preservation property of resolution of US images [24] with speckle removal. The combination of DWT and wiener filter gives better results in form of information preservation of the image during the noise removal from US images[30]. The combination of BFO and AMF gives better visual and statistical results[28]. Table 1 shows the values of performance analysis parameters i.e PSNR and MAE of all the techniques used for speckle noise removal from US images of varying noise densities from 10% to 30%. Table 2 shows the comparative results of proposed method with DWT & Wiener filter in homomorphic frameworkfor US Image 2 of liver in from of PSNR. it is clear from table 1 that proposed method gives better results in form of PSNR and MAE while table 2 gives the idea about enhancement in PSNR due to proposed method in comparision with DWT & Wiener filter in homomorphic framework. This shows that the BFO enhances the performance of DWT & Wiener filter in homomorphic framework. The results of proposed method were also compared with BFO & AMF technique as shown in table 3. It was observed that proposed method gives better results in form of PSNR and MAE. Table 1. Comparison of PSNR & MAE of proposed technique with available methods for an Ultrasound Image2 of liver Filter PSNR MAE AMF
σ =0.1 20.42
σ =0.2 17.69
σ =0.3 16.17
σ =0.1 0.0648
σ =0.2 0.0877
σ =0.3 0.1042
Wiener Filter
22.56
19.89
18.19
0.0491
0.0638
0.0767
DWT & Wiener filter
23.76
21.03
18.29
0.0167
0.0218
0.0300
BFO & AMF
68.22
65.98
64.51
0.0674
0.0861
0.1002
Proposed Method
78.68
74.24
70.45
0.0195
0.0342
0.0541
In homomorphic region
Table 2.Comparative Result of Proposed technique with DWT & Wiener filter in Homomorphic Frameworkfor an US Image 2 of liver Noise Density(%)
PSNR (DWT & Wiener Filter in Homomorphic Framework)
PSNR (Proposed method)
MAE (DWT & Wiener Filter in Homomorphic Framework)
MAE (Proposed method)
Enhancement of PSNR(dB)
05
25.517
81.161
0.0143
0.0137
55.644
10
23.759
78.688
0.0167
0.0195
54.929
15
22.548
75.952
0.0200
0.0270
53.404
20
21.032
74.244
0.0218
0.0342
53.212
25
19.697
72.215
0.02156
0.0438
52.518
30
18.289
70.458
0.0300
0.0541
52.169
Figure2(a) & (b) shows the graphical representation of the comparative results in form of PSNR and MAE respectively.
Rajeshwar Dass/ Procedia Computer Science 00 (2018) 000–000 Rajeshwar Dass / Procedia Computer Science 132 (2018) 1543–1551
1548
0.11
90
0.1
80
0.09 70
0.08 proposedmethod BFOAMF AMF wiener dwtwienerhomorphic
50 40
0.07 MAE
PSNR
60
proposedmethod BFOAMF AMF wiener dwtwienerhomorphic
0.06 0.05 0.04
30
0.03 20 10 0.05
0.02 0.1
0.15 0.2 Noisevariance
0.25
0.3
0.01 0.05
2(a)
0.1
0.15 0.2 Noisevariance
0.25
0.3
2(b)
Fig. 2. (a)Comparison results in PSNR of various algorithms for ultrasound image1(foetus); (b)Comparison results in MAE of various algorithms for ultrasound image 2(liver). Table 3. Comparative Results of Proposed Technique with BFO &AMF for an US Image 2 of liver Noise PSNR PSNR MAE MAE Density( (BFO & AMF) (Proposed Method) (BFO & AMF) (Proposed Method) %)
Enhancement of PSNR (dB)
05
70.544
81.161
0.0503
0.0137
11.017
10
68.224
78.688
0.0674
0.0195
10.464
15
66.982
75.952
0.0774
0.0270
8.97
20
65.984
74.244
0.0861
0.0342
8.26
25
64.869
72.215
0.0990
0.0438
7.346
30
64.506
70.458
0.1002
0.0541
5.952
The original foetus and liver images are shown in figure 3(a) and respective corrupted US images with 30% noise density are shown in fig.3(b). The Despeckled US images using various methods are shown in figure 3(c)-(g). Figure 3 (c) shows the output US despeckled images due to AMF filter. Figure 3(d) shows the output despeckled US images due to Wiener filter while figure 3(e) shows the output despeckled US images when DWT & wiener filter in homomorphic region were used for speckle noise removal. figure 3(f) shows the output despeckled US images of BFO & AMF filter while figure 3(g) shows the output despeckled US images when proposed method is used for despeckling of respective US images. It is clear from the table 1, 2, 3 and figures 3(c)-(g) that proposed method gives better results statistically as well as visually.
Rajeshwar Dass / Procedia Computer Science 132 (2018) 1543–1551 Rajeshwar Dass/ Procedia Computer Science 00 (2018) 000–000
Fig. 3. (a) Ultrasound images (image 1 is of foetus & image 2 is of liver
Fig. 3. (c) Adaptive median filter(AMF) output
Fig. 3 (e) DWT & wiener filter in Homomorphic region output
Fig 3(g) Restored images using Proposed method
Fig. 3. (b) Images with 30% noise density
Fig. 3. (d) Wiener filter output;
Fig. 3 (f) BFO&AMF output
1549
7
1550
Rajeshwar Dass / Procedia Computer Science 132 (2018) 1543–1551 Rajeshwar Dass/ Procedia Computer Science 00 (2018) 000–000
6. Conclusion and Future Scope This paper, presented a new technique to despeckle the ultrasound images. Proposed method gives recognizable and acceptable restoration of ultrasound images in presence of speckle noise with varied noise densities from 5% -30%. The visual test reports were also generated with statistical performance evaluation parameters for approval from radiologists/experts. The comparison of the results obtained with other despeckling techniques shows that the proposed technique performs better as compared to other existing techniques, statistically as well as visually. The proposed method have some limitations also. The performance of the system depends upon the parameter selection of BFO algorithm. In this paper only PSNR and MAE parameters taken for performance evalution of the proposed method. In future other parameters may be calculated. The performance of the proposed method starts decreasing as the density of speckle noise increses in the image. Thus it is still a challenge to despeckle highly distorted US images. 7. References
[1] Saraswathi J.S, and Mary Brinda (2011)“Denoising of mixed noise in ultrasound images.”IJCSI8 (4): 517-523. [2] J.C Bamber and C. Daft (1986)“Adaptive filtering for reduction of speckling ultrasonic pulse echo images.”ultrasonics:41-44. [3] T.Loupas,W.N Mcdiken, and P.L Allan (1989) “An adaptive weighted median filter for speckle suppression inmedical ultrasonic images.’’IEEE Trans. circuits and systems36:129-35. [4] J.S Lee and I.Jukevich (1994)“Speckle filtering of synthetic aperture radar images:A review.”Remote sensing Reviews8: 313-340. [5] Rajeshwar Dass, Swapna Devi and Priyanka (2011)“Speckle noise reduction techniques.’’International Journal of Electronics and Electrical Engineering(IJEEE) 16(1): 47-57. [6] A.K.Jain(1989) “Fundamental of digital image processing.”Prentice Hall. [7] Oleg V. Michailovich and Allen Tannenbaum(2006)“Despeckling of medical ultrasound images.” IEEE Trans. Ultrasonic’s, Ferroelectrics and frequency control 53(1). [8] Yuan Chen, and Amar Reheja(2005)“Wavelet lifting for speckle noise reduction in ultrasound images.”IEEE, Proceedings Engineering in Medicine and Biology, Shanghai, China. [9] A. Anchim, P. Tsakalides, and A. Bezarianos (2001)“Novel bayesian multiscale method for speckle removal in medical ultrasound images.” IEEE Trans. on Medical Imaging20:772-783. [10] N.Gupta, M.N.S. Swamy, and E.I. Plotkin (2005) “Despeckling of medical ultrasound images using data and rate adaptive lossy compression.” IEEE Trans. on Medical Imaging24:743-754. [11] R.K. Mukkavilli, J.S. Sahambi and P.K. Bora (2008) “Modified homomorphic wavelet based despeckling of Medical Ultrasound Images.“ IEEE Trans:887-890. [12] ArashVosoughi and Mohammad.B. Shamsollahi (2008)“Noise reduction of ultrasound images using M-band wavelet transform and wiener filter in a homomorphic framework.” IEEE Trans BMEI:510-515. [13] J. Huang, and X. Yang(2013) “Fast reduction of speckle noise in real ultrasound images.” Signal Processing 93(4): 684-694. [14] D. Gupta, R. S. Anand, and B. Tyagi (2014) “Speckle filtering of ultrasound images using a modified non-linear diffusion model in nonsubsampled shearlet domain” IET Image Processing 9(2):107-117. [15] D. Gupta, R. S. Anand, and B. Tyagi (2015) “Despeckling of ultrasound medical images using ripplet domain nonlinear filtering .” Signal, Image and Video Processing 9(5):1093-1111. [16] H. H. Choi, J. H. Lee, S. M. Kim, and S. Y. Park (2015) “Speckle noise reduction in ultrasound images using a discrete wavelet transformbased image fusion technique” Bio-Medical Materials and Engineering 26(1):1587-S1597. [17] J. Yang, J. Fan, D. Ai, X. Wang, Y. Zheng, S. Tang, and Y. Wang (2016) “Local statistics and non-local mean filter for speckle noise reduction in medical ultrasound image.” Neurocomputing 195: 88-95. [18] P. V. Sudeep, P. Palanisamy, J. Rajan, H. Baradaran, L. Saba, A. Gupta, and J. S. Suri (2016) “Speckle reduction in medical u ltrasound images using an unbiased non-local means method” Biomedical Signal Processing and Control, 28: 1-8. [19] Nagashettappa Biradar, Mohan Lal Dewal and Manoj Kumar Rohit (2015) “Speckle noise reduction in B-made echocardiographic images: A comparision.” IET Technical Review, Taylor & Francis:1-19. [20] S.Chang, B.Yu and M.Vetterli (2000)“Adaptive wavelet thresholding for image denoising and compression.” IEEE Trans. Image Processing9: 1532-1546. [21] Nusrat Ahmed Surobhi and Md. Ruhul Amin (2006) “employment of modified homomorphic filters in medical imaging. ”Daftdil International University Journal of science and Technology1(1). [22] Y.Liu, K.M Passino and M.A Simaan (2002)“Biomimiray of Social Foraging Bacteria for distributed optimization: Models. Principles and Emergent Behaviors.” Journal of optimization theory and applications115(3):603-28. [23] K.M Passino (2002)“Biomimiray of bacterial foraging for distributed optimization and control.”IEEE control systems magazine: 53-67. [24] Tanpia Tasnim, Md. Maruf Hossain Shuvo, and Sabbir Hasan (2017) “Study of Speckle Noise Reduction from Ultrasound B-mode Images using Different Filtering Techniques.” International Conference on Advances in Electrical Engineering, Dhaka, Bangladesh: 229-234. [25] K.M Backwad, S.S Pattnaik, B.S Sohi, S.Devi, B.K Panigrahi, Sastri and V.R.S Gollapudi(2009)“Bacterial foraging optimization technique cascaded with adaptive filter to enhance peak signal to noise ratio from single image.”IETE Journal of Research55(4) :173-179. [26] Cris Solomon and Toby Breekon (2011) “Fundamental of digital image processing.” Willey Blackwell, U.K. [27] Rajeshwar Dass, Swapna Devi, Priyanka and Sunil Kumar(2012)“Image Quality Assessment Techniques.” National Conference on Advances in Video, Cyber Learning and Electronics, NITTTR Chandigarh:234-240. [28] Rajeshwar Dass, Swapna Devi and Priyanka (2012)“Effect of Wiener-Helstrom Filtering Cascaded with Bacterial Foraging Optimization to Despeckle the Ultrasound Images.”IJCSI International Journal of Computer Science 4(2):372-380. [29] Dina Arifatul Khusna, Hanung Adi Nugroho, Indah Soesanti(2015) “Performance analysis of edge and detailed preserved speckle noise
Rajeshwar Dass / Procedia Computer Science 132 (2018) 1543–1551 Rajeshwar Dass/ Procedia Computer Science 00 (2018) 000–000
1551 9
reduction filters for breast ultrasound images.” Int. Conference on Information Technology, Computer and Electrical Engineering, Indonesia:76-80. [30] A.Bovik(2000) “Handbookof image and video processing” New York: Academic.