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Spatial and spectral regularization for multispectral photoacoustic image clustering

机译：多光谱光声图像聚类的空间和光谱正则化

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摘要

Photoacoustic imaging is a hybrid modality used to image biological tissues. Multispectral optical excitation permits to obtain functional images thanks to the tissue specific optical absorption that depends on the light wavelength. The aim of this study is to propose a clustering method for photoacoustic multispectral images based on both spatial neighbourhood and spectral behaviour. The proposed methodology is adapted from spatio-temporal mean-shift approach: it clusters distant or neighbouring patterns having similar spectral profiles. The clustering performance of our modified mean-shift algorithm is experimentally tested on multispectral photoacoustic tomography data. Results obtained from phantoms including two blood dilutions and colored absorbers are presented. It is thus shown that our strategy allows the experimental discrimination of media, achieving a clustering performance of more than 99%. Moreover, depending on the applied pre-processing the discrimination of different concentrations of a same medium is possible.
机译：光声成像是用于图像生物组织的混合模态。由于依赖于光波长的组织特异性光学吸收，多光谱光学激发允许获得功能图像。本研究的目的是提出基于空间邻域和光谱行为的光声多光谱图像的聚类方法。所提出的方法是从时空平均换档方法的调整：它包括具有相似光谱分布的远处或相邻模式。我们修改平均移位算法的聚类性能在多光谱光声断层扫描数据上进行了实验测试。介绍了包括两个血液稀释度和着色吸收剂的幽灵获得的结果。因此表明，我们的战略允许媒体的实验鉴别，实现超过99％的聚类性能。此外，取决于所施加的预处理，可以实现不同浓度的相同培养基。

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