Abstract
Photoacoustics is a hybrid modality used to image biological tissues. As optical absorption of tissues depends on the wavelength of the transmitted light, multispectral photoacoustic datasets can be obtained by changing this wavelength. This study presents a regularization method to segment multispectral photoacoustic images based on both the spatial and spectral features of the dataset pixels. The proposed processing is adapted from the spatiotemporal mean-shift approach and cluster patterns with similar spectral profiles, i.e., the variation of the received amplitude among the wavelengths, independent of their initial position. The segmentation performance of this method has been experimentally tested on multispectral photoacoustic tomographic data. We initially used a phantom that contained fresh and stale liver samples, and then a second phantom that contained two blood dilutions or a colored absorber. Experimentally, a clustering performance greater than 98% is achieved. This method makes it possible to discriminate between different media, between the same medium as fresh or stale, and between the same medium with different dilutions.
Highlights
1 Introduction Multispectral photoacoustic imaging is a functional method that combines the advantages of acoustic and optical imaging [1], the principle of which is to illuminate a region of interest (ROI) with a pulsed laser at different wavelengths
Acoustic waves that originate from a temperature increase due to optical absorption can backpropagate from the ROI to a receiving ultrasound probe
Illumination of a ROI with a range of optical wavelengths allows the study of the spectral profile of the contained media, as their respective optical absorptions will differ from each other depending on the wavelength of the light [2, 3]
Summary
Multispectral photoacoustic imaging is a functional method that combines the advantages of acoustic and optical imaging [1], the principle of which is to illuminate a region of interest (ROI) with a pulsed laser at different wavelengths. In past years, unsupervised and supervised segmentation or classification methods have been proposed to automatically discriminate between biological media using multispectral photoacoustic datasets Whether they were based on principal and independent component analysis [7], on spectral fitting [7], or on leastsquares criteria [8], these methods essentially relied on the spectral shape evolution rather than on the absolute photoacoustic signal amplitude at each wavelength. They did not take into account light attenuation or ultrasonic dispersion. Media discrimination in multispectral photoacoustic imaging is a challenging task that
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