A linear transport-based embedding method for analyzing biomedical images

A fundamental difficulty facing those who wish to extract quantitative information from biomedical images is finding a manner with which to compare two morphological examplars (e.g. cells or sub cellular structures). We present a new approach for finding a linear embedding for a set of images that is isometric to a transportation-based metric. The approach is capable of quantifying both shapes as well as textures, is generative, and facilitates the use of geometric data processing techniques such as LDA and PCA.

[1]  Leonidas J. Guibas,et al.  The Earth Mover's Distance as a Metric for Image Retrieval , 2000, International Journal of Computer Vision.

[2]  Gustavo K. Rohde,et al.  A Linear Optimal Transportation Framework for Quantifying and Visualizing Variations in Sets of Images , 2012, International Journal of Computer Vision.

[3]  Robert F. Murphy,et al.  A neural network classifier capable of recognizing the patterns of all major subcellular structures in fluorescence microscope images of HeLa cells , 2001, Bioinform..

[4]  Yilin Mo,et al.  Penalized Fisher discriminant analysis and its application to image-based morphometry , 2011, Pattern Recognit. Lett..

[5]  Alejandro F Frangi,et al.  Automatic construction of 3-D statistical deformation models of the brain using nonrigid registration , 2003, IEEE Transactions on Medical Imaging.

[6]  Mikhail Belkin,et al.  Laplacian Eigenmaps for Dimensionality Reduction and Data Representation , 2003, Neural Computation.

[7]  Karl J. Friston,et al.  Identifying global anatomical differences: Deformation‐based morphometry , 1998 .

[8]  J. Thompson,et al.  MOVPE growth for the fabrication of OEICs , 1992 .

[9]  Gustavo K. Rohde,et al.  An Optimal Transportation Approach for Nuclear Structure-Based Pathology , 2011, IEEE Transactions on Medical Imaging.

[10]  J. Tenenbaum,et al.  A global geometric framework for nonlinear dimensionality reduction. , 2000, Science.

[11]  Gustavo K. Rohde,et al.  A template matching approach for segmenting microscopy images , 2012, 2012 9th IEEE International Symposium on Biomedical Imaging (ISBI).

[12]  Robert F Murphy,et al.  Deformation‐based nuclear morphometry: Capturing nuclear shape variation in HeLa cells , 2008, Cytometry. Part A : the journal of the International Society for Analytical Cytology.

[13]  M L Mendelsohn,et al.  THE ANALYSIS OF CELL IMAGES * , 1966, Annals of the New York Academy of Sciences.

[14]  Lei Zhu,et al.  Optimal Mass Transport for Registration and Warping , 2004, International Journal of Computer Vision.

[15]  D'arcy W. Thompson.  On growth and form i , 1943 .

[16]  S T Roweis,et al.  Nonlinear dimensionality reduction by locally linear embedding. , 2000, Science.

[17]  Wei Wang,et al.  Detection and classification of thyroid follicular lesions based on nuclear structure from histopathology images , 2010, Cytometry. Part A : the journal of the International Society for Analytical Cytology.

[18]  M. Miller.  Computational anatomy: shape, growth, and atrophy comparison via diffeomorphisms , 2004, NeuroImage.