Polarimetric imaging and image analysis have gained increased interest in soft tissue analysis at the cellular level. However, polarimetric imaging has widely been tested on thin tissue sections to provide reliable information correlated with histopathological findings. Polarimetric bulk tissue analysis always offered an overall assessment of various tissue optical properties for diagnosis. In this study, the histopathological correlation of bulk tissue polarimetry images for soft tissues is discussed. The first-hand information on the use of bulk tissue Mueller polarimetry and image analysis as an alternative to tissue histopathology is presented for surgically extracted colon and breast tissues.
Laser speckle imaging is one of the powerful non-invasive imaging techniques to monitor and assess microcirculation parameters. Qualitative analysis of perfusion parameters has been carried out in the recent past. But the quantitative estimation of tissue perfusion parameters like flow velocity and scatterer concentration simultaneously from laser speckle images remains challenging. The introduction of machine learning methods into laser speckle image analysis can help meet these challenges to a great extent. This paper presents an approach for the simultaneous extraction of perfusion parameters, using multi-target regression techniques applied to the extracted features from acquired laser speckle images after Eigendecomposition filtering. The multi-target regression trees are identified as an effective tool for the simultaneous extraction of flow velocity and scatterer concentration with adequate mean absolute percentage error. Besides the achieved speed and computational efficiency, our work demonstrates the viability of this approach in quantifying perfusion parameters simultaneously. Due to its simple, non-invasive, and cost-effective nature, the proposed technique could be used in the realtime assessment of tissue health.
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