A novel multiphoton microscopy images segmentation method based on superpixel and watershed

Multiphoton microscopy (MPM) imaging technique based on two-photon excited fluorescence (TPEF) and second harmonic generation (SHG) shows fantastic performance for biological imaging. The automatic segmentation of cellular architectural properties for biomedical diagnosis based on MPM images is still a challenging issue. A novel multiphoton microscopy images segmentation method based on superpixels and watershed (MSW) is presented here to provide good segmentation results for MPM images. The proposed method uses SLIC superpixels instead of pixels to analyze MPM images for the first time. The superpixels segmentation based on a new distance metric combined with spatial, CIE Lab color space and phase congruency features, divides the images into patches which keep the details of the cell boundaries. Then the superpixels are used to reconstruct new images by defining an average value of superpixels as image pixels intensity level. Finally, the marker-controlled watershed is utilized to segment the cell boundaries from the reconstructed images. Experimental results show that cellular boundaries can be extracted from MPM images by MSW with higher accuracy and robustness.

[1]  Haishan Zeng,et al.  Image segmentation for integrated multiphoton microscopy and reflectance confocal microscopy imaging of human skin in vivo. , 2015, Quantitative imaging in medicine and surgery.

[2]  Sven J. Dickinson,et al.  TurboPixels: Fast Superpixels Using Geometric Flows , 2009, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[3]  Yong Kim,et al.  A novel surface-based geometric approach for 3D dendritic spine detection from multi-photon excitation microscopy images , 2009, 2009 IEEE International Symposium on Biomedical Imaging: From Nano to Macro.

[4]  D. Tank,et al.  Imaging Large-Scale Neural Activity with Cellular Resolution in Awake, Mobile Mice , 2007, Neuron.

[5]  B. Bouma,et al.  Improved signal-to-noise ratio in spectral-domain compared with time-domain optical coherence tomography. , 2003, Optics letters.

[6]  Erik H. W. Meijering,et al.  Cell Segmentation: 50 Years Down the Road [Life Sciences] , 2012, IEEE Signal Processing Magazine.

[7]  Pascal Fua,et al.  SLIC Superpixels Compared to State-of-the-Art Superpixel Methods , 2012, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[8]  R. Murphy,et al.  Automated subcellular location determination and high-throughput microscopy. , 2007, Developmental cell.

[9]  W. Denk,et al.  Two-photon laser scanning fluorescence microscopy. , 1990, Science.

[10]  Agostinho C. Rosa,et al.  A Statistical Pixel Intensity Model for Segmentation of Confocal Laser Scanning Microscopy Images , 2010, IEEE Transactions on Image Processing.

[11]  Petr Dokládal,et al.  Automatic 3D segmentation of multiphoton images: a key step for the quantification of human skin , 2013, Skin research and technology : official journal of International Society for Bioengineering and the Skin (ISBS) [and] International Society for Digital Imaging of Skin (ISDIS) [and] International Society for Skin Imaging.

[12]  Min Liu,et al.  Automated Segmentation and Tracking of SAM Cells , 2014, CCPR.

[13]  G. Muehllehner,et al.  Positron emission tomography , 1988, Seminars in neurology.

[14]  Jürgen Popp,et al.  Automated seeding-based nuclei segmentation in nonlinear optical microscopy. , 2013, Applied optics.

[15]  Anirban Chakraborty,et al.  Adaptive cell segmentation and tracking for volumetric confocal microscopy images of a developing plant meristem. , 2011, Molecular plant.

[16]  Pierre Soille,et al.  Morphological image analysis applied to crop field mapping , 2000, Image Vis. Comput..

[17]  D. LeBihan,et al.  Molecular diffusion nuclear magnetic resonance imaging. , 1991 .

[18]  Jie Wang,et al.  VCells: Simple and Efficient Superpixels Using Edge-Weighted Centroidal Voronoi Tessellations , 2012, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[19]  Wiro J. Niessen,et al.  Advanced Level-Set-Based Cell Tracking in Time-Lapse Fluorescence Microscopy , 2010, IEEE Transactions on Medical Imaging.

[20]  Jürgen Popp,et al.  Towards automated segmentation of cells and cell nuclei in nonlinear optical microscopy. , 2012, Journal of biophotonics.

[21]  Xiaobo Zhou,et al.  Nuclei Segmentation Using Marker-Controlled Watershed, Tracking Using Mean-Shift, and Kalman Filter in Time-Lapse Microscopy , 2006, IEEE Transactions on Circuits and Systems I: Regular Papers.