Image segmentation for integrated multiphoton microscopy and reflectance confocal microscopy imaging of human skin in vivo

Background: Non-invasive cellular imaging of the skin in vivo can be achieved in reflectance confocal microscopy (RCM) and multiphoton microscopy (MPM) modalities to yield complementary images of the skin based on different optical properties. One of the challenges of in vivo microscopy is the delineation (i.e., segmentation) of cellular and subcellular architectural features. Methods: In this work we present a method for combining watershed and level-set models for segmentation of multimodality images obtained by an integrated MPM and RCM imaging system from human skin in vivo. Results: Firstly, a segmentation model based on watershed is introduced for obtaining the accurate structure of cell borders from the RCM image. Secondly,, a global region based energy level-set model is constructed for extracting the nucleus of each cell from the MPM image. Thirdly, a local region-based Lagrange Continuous level-set approach is used for segmenting cytoplasm from the MPM image. Conclusions: Experimental results demonstrated that cell borders from RCM image and boundaries of cytoplasm and nucleus from MPM image can be obtained by our segmentation method with better accuracy and effectiveness. We are planning to use this method to perform quantitative analysis of MPM and RCM images of in vivo human skin to study the variations of cellular parameters such as cell size, nucleus size and other mophormetric features with skin pathologies.