A novel approach to speckle noise filtering based on Artificial Bee Colony algorithm: An ultrasound image application

Latifoğlu, Fatma

doi:10.1016/j.cmpb.2013.05.009

Cited by 56 publications

(14 citation statements)

References 38 publications

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“…The ultrasound imaging is non invasive, relatively inexpensive and is performed in real time. However, the images are degraded by speckle noise [2], which affects their quality reducing the contrast and concealing the details. Consequently, a proper interpretation of the results and a correct diagnosis can be difficult.…”

Section: Introductionmentioning

confidence: 99%

Escaping path approach for speckle noise reduction

Szczepański

Radlak

2015

SPIE Proceedings

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A novel fast filtering technique for multiplicative noise removal in ultrasound images was presented in this paper. The proposed algorithm utilizes concept of digital paths created on the image grid presented in [1]adapted to the needs of multiplicative noise reduction. The new approach uses special type of digital paths so called Escaping Path Model and modified path length calculation based on topological as well as gray-scale distances. The experiments confirmed that the proposed algorithm achieves a comparable results with the existing state-of-theart denoising schemes in suppressing multiplicative noise in ultrasound images.

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Section: Introductionmentioning

confidence: 99%

Escaping path approach for speckle noise reduction

Szczepański

Radlak

2015

SPIE Proceedings

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“…Torali and Kandarpa performed a de-noising technique using feed forward artificial neural network (FFANN) to remove the speckle noise (Saikia and Sarma 2014). Latifoglu introduced an ABC algorithm for speckle noise filtering (2D FIR filter) in an ultrasound image application (Latifoglu 2013). Moreover, the ANN is used for fast particle characterization in 2016 (Schenider et al 2016).…”

Section: Introductionmentioning

confidence: 99%

Usage of artificial neural networks method for image enhancement of reconstructed image in digital holography

Kaya

Saraç

2018

KSEJ

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The aim of paper is to use an artificial neural network approach for enhancement of three dimensional image reconstructed in digital holography. An artificial neural network method based on Gerchberg-Saxton algorithm is implemented to reduce the noise and increase the brightness of this image. The results of proposed method have been presented by a relative error. In addition, these relative error figures are supported with error histogram obtained from MATLAB neural network fitting toolbox.

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“…Physical and Numerical Phantoms are very important tools in many algorithm-development stages of image processing applications (Cardoso et al, 2012;Culjat et al, 2010;Hoskins, 2008;King et al, 2011). Due to the flexibility of controlling parameters and desired features, they are used to calibrate and to evaluate the performance of algorithms; hence, valuable for the development of image systems and techniques, such as filtering (Latifoğlu, 2013), segmentation (Kurnaz et al, 2007) and speckle tracking. In (Culjat et al, 2010) a review on tissue-mimicking physical phantoms for US imaging is described.…”

Section: Introductionmentioning

confidence: 99%

Realistic deformable 3D numeric phantom for transcutaneous ultrasound

2017

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Introduction: Numerical phantoms are important tools to design, calibrate and evaluate several methods in various image-processing applications, such as echocardiography and mammography. We present a framework for creating ultrasound numerical deformable phantoms based on Finite Element Method (FEM), Linear Isomorphism and Field II. The proposed method considers that the scatterers map is a property of the tissue; therefore, the scatterers should move according to the tissue strain. Methods: First, a volume representing the target tissue is loaded. Second, parameter values, such as Young's Modulus, scatterers density, attenuation and scattering amplitudes are inserted for each different regions of the phantom. Then, other parameters related to the ultrasound equipment, such as ultrasound frequency and number of transducer elements, are also defined in order to perform the ultrasound acquisition using Field II. Third, the size and position of the transducer and the pressures that are applied against the tissue are defined. Subsequently, FEM is executed and deformation is computed. Next, 3D linear isomorphism is performed to displace the scatterers according to the deformation. Finally, Field II is carried out to generate the non-deformed and deformed ultrasound data. Results: The framework is evaluated by comparing strain values obtained the numerical simulation and from the physical phantom from CIRS. The mean difference between both phantoms is lesser than 10%. Conclusion: The acoustic and deformation outcomes are similar to those obtained using a physical phantom. This framework led to a tool, which is available online and free of charges for educational and research purposes.

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A novel approach to speckle noise filtering based on Artificial Bee Colony algorithm: An ultrasound image application

Cited by 56 publications

References 38 publications

Escaping path approach for speckle noise reduction

Escaping path approach for speckle noise reduction

Usage of artificial neural networks method for image enhancement of reconstructed image in digital holography

Realistic deformable 3D numeric phantom for transcutaneous ultrasound

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