Automated noise removal system for photoacoustic imaging using independent component analysis

This study proposes an automated system to remove noise from photoacoustic (PA) signal using Independent Component Analysis (ICA). PPA signals suffer from optical and acoustic noise that degrades image quality due to the low intensity of laser light permissible in tissues. Our approach Catch Photoacoustic Peak - Independent Component Analysis (CPP-ICA), addresses this issue by applying smoothing and ICA to reduce noise without distorting PA signal characteristics. This ultimately enhances image quality while preserving important details. All independent components (ICs) of smoothed PA signal extracted using the FastICA method are processed based on their maximum peak regions, eliminating the need for manual selection of ICs for each dataset. This enables the noise removal system to operate automatically without requiring adjustments for different PA sources. Experimental results and comparative simulations with the Wavelet Denoising method show significant improvements in noise reduction performance. Our proposed technique improved the Contrast-to-Noise Ratio (CNR) and Signal-to-Noise Ratio (SNR) by 6 dB to 20 dB in experimental studies compared to the Wavelet Denoising approach, while preserving image details with minimal blurring.