Wavelet de-noising method with adaptive threshold selection for photoacoustic tomography

Zhou, Meng; Xia, Haibo; Lan, Hengrong; Duan, Tingyang; Zhong, Hongtao; Gao, Fei

doi:10.1109/embc.2018.8513159

Cited by 13 publications

(13 citation statements)

References 20 publications

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“…Hence, prior to image formulation, different signal processing techniques can be used to remove the noise, enhancing the signal-tonoise ratio. Filtering in addition to principal component analysis (PCA) are widely used techniques [15][16][17][18][19][20][21][22][23][24]. Also, multiresolution analysis, utilizing wavelet analysis and subband decomposition, of PA signals can be used to obtain images at different scales [17,[20][21][22][23][24].…”

Section: Pa Signal and Image Processingmentioning

confidence: 99%

Photoacoustic Imaging for Cancer Diagnosis: A Breast Tumor Example

Gharieb¹

2020

Photoacoustic Imaging - Principles, Advances and Applications

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Photoacoustic (PA) imaging utilizes laser pulses to deliver energy to an examined object for the generation of ultrasonic waves. Thus, it provides a noninvasive and nonionizing imaging modality. Therefore, it has found clinical use for cancer diagnosis in different organs, e.g., breast, prostate, and thyroid nodules. It offers morphological, functional, and molecular imaging. Moreover, the oxygen saturation in a body can be computed by calculating the wavelength-dependent light absorption coefficients at two different wavelengths. In this chapter, the principle of the PA imaging is introduced for the present book.

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Section: Pa Signal and Image Processingmentioning

confidence: 99%

Photoacoustic Imaging for Cancer Diagnosis: A Breast Tumor Example

Gharieb¹

2020

Photoacoustic Imaging - Principles, Advances and Applications

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Section: Introductionmentioning

confidence: 99%

“…Several PA de-noising methods have been demonstrated to enhance the SNR of the PA signals and/or images [7,8,9,10,11,12,13,14,15,16,17]. The de-noising has been studied for PACT [7,8,9,10,12,16,17] and PAM [11,14,15]. For most PACT de-noising, PA raw data (A-line signals) are usually de-noised first prior to im-age reconstruction [7,9,12].…”

Section: Introductionmentioning

confidence: 99%

De-Noising of Photoacoustic Microscopy Images by Deep Learning

He¹,

Zhou²,

Shang³

et al. 2022

Preprint

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As a hybrid imaging technology, photoacoustic microscopy (PAM) imaging suffers from noise due to the maximum permissible exposure of laser intensity, attenuation of ultrasound in the tissue, and the inherent noise of the transducer. De-noising is a post-processing method to reduce noise, and PAM image quality can be recovered. However, previous de-noising techniques usually heavily rely on mathematical priors as well as manually selected parameters, resulting in unsatisfactory and slow de-noising performance for different noisy images, which greatly hinders practical and clinical applications. In this work, we propose a deep learning-based method to remove complex noise from PAM images without mathematical priors and manual selection of settings for different input images. An attention enhanced generative adversarial network is used to extract image features and remove various noises. The proposed method is demonstrated on both synthetic and real datasets, including phantom (leaf veins) and in vivo (mouse ear blood vessels and zebrafish pigment) experiments. The results show that compared with previous PAM de-noising methods, our method exhibits good performance in recovering images qualitatively and quantitatively. In addition, the de-noising speed of 0.016 s is achieved for an image with 256 × 256 pixels. Our approach is effective and practical for the de-noising of PAM images.

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“… 38 A commonly used PA signal denoising technique is the wavelet denoising method. 39 – 43 Wavelet-based denoising methods face some challenges, such as choosing an appropriate basis function, the optimum number of wavelet decomposition levels, and especially choosing an optimum threshold value. 28 While there are solutions for these drawbacks, these solutions are often complicated and computationally expensive.…”

Section: Introductionmentioning

confidence: 99%

Photoacoustic image improvement based on a combination of sparse coding and filtering

et al. 2020

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Significance: Photoacoustic imaging (PAI) has been greatly developed in a broad range of diagnostic applications. The efficiency of light to sound conversion in PAI is limited by the ubiquitous noise arising from the tissue background, leading to a low signal-to-noise ratio (SNR), and thus a poor quality of images. Frame averaging has been widely used to reduce the noise; however, it compromises the temporal resolution of PAI. Aim: We propose an approach for photoacoustic (PA) signal denoising based on a combination of low-pass filtering and sparse coding (LPFSC). Approach: LPFSC method is based on the fact that PA signal can be modeled as the sum of low frequency and sparse components, which allows for the reduction of noise levels using a hybrid alternating direction method of multipliers in an optimization process. Results: LPFSC method was evaluated using in-silico and experimental phantoms. The results show a 26% improvement in the peak SNR of PA signal compared to the averaging method for in-silico data. On average, LPFSC method offers a 63% improvement in the image contrast-tonoise ratio and a 33% improvement in the structural similarity index compared to the averaging method for objects located at three different depths, ranging from 10 to 20 mm, in a porcine tissue phantom. Conclusions: The proposed method is an effective tool for PA signal denoising, whereas it ultimately improves the quality of reconstructed images, especially at higher depths, without limiting the image acquisition speed.

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Wavelet de-noising method with adaptive threshold selection for photoacoustic tomography

Cited by 13 publications

References 20 publications

Photoacoustic Imaging for Cancer Diagnosis: A Breast Tumor Example

Photoacoustic Imaging for Cancer Diagnosis: A Breast Tumor Example

De-Noising of Photoacoustic Microscopy Images by Deep Learning

Photoacoustic image improvement based on a combination of sparse coding and filtering

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