Photoacoustic (PA) signal experiences excessive background noise when generated using cost-effective, low-energy laser diodes. A denoising technique is essential in this case. Averaging is a common approach to increase the Signal-to-Noise Ratio (SNR) of PA signals. This technique requires numbers of data acquisition in hundreds and thousands and hence, demands more hardware and time consuming at the same time. Here, an adaptive method based on Adaptive Line Enhancers (ALE) algorithm to improve the SNR of PA signals has been presented. Our results validate the feasibility of the usage of an adaptive method and also indicate excellent improvement in terms of increasing the SNR of the PA signals. Additionally, this proposed algorithm requires way less number of acquisitions as compared to the conventional averaging techniques that leads to faster PA image processing.
In practice, photoacoustic (PA) waves generated with cost-effective, low-energy laser diodes, are weak and almost buried in noise. Reconstruction of an artifact-free PA image from noisy measurements requires an effective denoising technique. Averaging techniques are widely used to increase the signal-to-noise ratio (SNR) of the weak PA signals but the process is time-consuming and in case of very low SNR measurements, hundreds/thousands of data acquisition epochs needed to provide the required data In this study, we propose to use adaptive denoising methodology in which adaptive line enhancers (ALE) has been embedded for increasing the SNR of PA signals in very low-cost PA systems. Our results show that the proposed method increases the SNR of the PA signals with fewer acquisitions more efficiently, compared to common averaging techniques. Consequently, PA imaging with this method can be conducted considerably faster.
Optical Coherence Tomography (OCT) offers real-time high-resolution three-dimensional images of tissue
microstructures. In this study, we used OCT skin images acquired from ten volunteers, neither of whom had any skin
conditions addressing the features of their anatomic location. OCT segmented images are analyzed based on their
optical properties (attenuation coefficient) and textural image features e.g., contrast, correlation, homogeneity, energy,
entropy, etc. Utilizing the information and referring to their clinical insight, we aim to make a comprehensive
computational model for the healthy skin. The derived parameters represent the OCT microstructural morphology and
might provide biological information for generating an atlas of normal skin from different anatomic sites of human
skin and may allow for identification of cell microstructural changes in cancer patients. We then compared the
parameters of healthy samples with those of abnormal skin and classified them using a linear Support Vector Machines
(SVM) with 82% accuracy.