Comparison of noise reduction methods in photoacoustic microscopy

Abstract

Photoacoustic microscopy (PAM) is classified as a hybrid imaging technique based on the photoacoustic effect and has been frequently studied in recent years. Photoacoustic (PA) signals are inherently recorded in a noisy environment and are also exposed to noise by system components. Therefore, it is essential to reduce the noise in PA signals to reconstruct images with less error. In this study, an image reconstruction algorithm for PAM system was implemented and different filtering approaches for denoising were compared. Studies were carried out in three steps: simulation, experimental phantom and blood cell studies. FIR low-pass and band-pass filters and Discrete Wavelet Transform (DWT) based filters (mother wavelets: "bior3.5", "bior3.7", "sym7") with four different thresholding techniques were examined. For the evaluation purpose, Root Mean Square Error (RMSE), Signal to Noise Ratio (SNR) and Contrast to Noise Ratio *Revised manuscript (clean) Click here to download Revised manuscript (clean): CBM Revised Clean-2.docx (CNR) metrics were calculated. In the simulation studies, the most effective methods were obtained as; sym7/heursure/hard thresh. combination (low and medium level noise) and bior3.7/sqtwolog/soft thresh. combination (high-level noise). In experimental phantom studies, noise was classified into five levels. Different filtering approaches perform better depending on the SNR of PA images. For the blood cell study, based on the standard deviation in the background, sym7/sqtwolog/soft thresh. combination provided the best improvement and this result supported the experimental phantom results.

Multistage adaptive noise reduction technique for optical resolution photoacoustic microscopy

“…Although only band pass filtering was used as a preprocessing technique in this study, Wavelet based transformation can also be combined with the band pass filter to further condition the signal for image processing [41]. However, in order for this method to be combined with our proposed technique, its effect on impulsive noise (ie, smoothing) needs to be addressed.…”

“…As a result, a window size of was selected. The filtering strength was selected to be equal to [41]: …”

Signal and Image Processing in Biomedical Photoacoustic Imaging: A Review

“…Rigrsure, Sqtwolog, Heursure, Minimaxi) [83]. Guney et al [84] evaluated the performance of wavelet transform based signal processing methods (bior3.5, bior3.7 and sym7) in MATLAB by using the PA signals as input signals, acquired from blood vessels using photoacoustic microscopy (PAM). The results were compared with conventional FIR low and bandpass filters.…”

An Investigation of Signal Preprocessing for Photoacoustic Tomography

“…Improved results could be obtained by changing the basis function to waveforms matched to the signal, as in wavelet denoising [ 7 , 8 ]. The signal is decomposed using the discrete wavelet transform method into wavelet coefficients where, ideally, the signal and noise are separated.…”