Clutter noise is an important challenge in photocoustic (PA) and ultrasound (US) imaging as they degrade the image
quality. In this paper, the short-lag spatial coherence (SLSC) imaging technique is used to reduce clutter and side lobes
in PA images. In this technique, images are obtained through the spatial coherence of PA signals at small spatial
distances across the transducer aperture. The performance of this technique in improving image quality and detecting
point targets is compared with a conventional delay-and-sum (DAS) beamforming technique. A superior contrast,
contrast-to-noise ratio (CNR) and signal-to-noise ratio (SNR) are observed when SLSC imaging is employed. Point
spread function of point targets shows an improved spatial resolution and reduced side lobes when compared with DAS
beamforming. Also shown is the impact of increasing the number of frames on which SLSC is applied. The results show
that contrast, CNR, and SNR are improved with increasing number of frames.
In this paper a new method is proposed to classify vascular tissues in the range from normal to different degrees of
abnormality based on the Photo-Acoustic (PA) signals generated by different categories of vasculatures. The
classification of the vasculatures is achieved based on the statistical features of the photoacoustic radiofrequency (RF)
signals such as energy, variance, and entropy in the wavelet domain. A feature vector for each category of vasculature is
provided and the distance between feature vectors are computed as the measure of similarity between vasculatures. The
distances are mapped in two-dimensional space depicting the proximities of the different categories of the vasculatures.
The method proposed in this paper can help both detecting abnormal tissues and monitoring the treatment progress by
measuring the similarity between vascular tissues in different stages of treatment. The method is applied to simulated
data as well as in vivo data from tumor bearing mice to detect cancer treatment effects.