GEOMETRIC ANALYSIS OF PLANAR SHAPES WITH APPLICATIONS TO CELL DEFORMATIONS

Shape analysis is of great importance in many fields such as computer vision, medical imaging, and computational biology. In this paper we focus on a shape space in which shapes are represented by means of planar closed curves. In this shape space a new metric was recently introduced with the result that this shape space has the property of being isometric to an infinite-dimensional Grassmann manifold of 2-dimensional subspaces. Using this isometry it is possible, from Younes et al. (2008), to explicitly describe geodesics, a task that previously was not at all easy. Our aim is twofold, namely: to use this general theory in order to show some applications to the study of erythrocytes, using digital images of peripheral blood smears, in the treatment of sickle cell disease; and, since normal erythrocytes are almost circular and many Sickle cells have elliptical shape, to particularize the computation of geodesics and distances between shapes using this metric to planar objects considered as deformations of a template (circle or ellipse). The applications considered include: shape interpolation, shape classification, and shape clustering.

[1]  Calyampudi R. Rao,et al.  Linear statistical inference and its applications , 1965 .

[2]  Calyampudi Radhakrishna Rao,et al.  Linear Statistical Inference and its Applications , 1967 .

[3]  Ali S. Hadi,et al.  Finding Groups in Data: An Introduction to Chster Analysis , 1991 .

[4]  Xavier Pennec,et al.  Intrinsic Statistics on Riemannian Manifolds: Basic Tools for Geometric Measurements , 2006, Journal of Mathematical Imaging and Vision.

[5]  Jan Pedersen,et al.  A Deformable Template Model, with Special Reference to Elliptical Templates , 2002, Journal of Mathematical Imaging and Vision.

[6]  Roger P. Woods,et al.  Characterizing volume and surface deformations in an atlas framework: theory, applications, and implementation , 2003, NeuroImage.

[7]  Dariusz Frejlichowski Pre-processing, Extraction and Recognition of Binary Erythrocyte Shapes for Computer-Assisted Diagnosis Based on MGG Images , 2010, ICCVG.

[8]  T Asakura,et al.  Morphologic studies of sickle erythrocytes by image analysis. , 1990, The Journal of laboratory and clinical medicine.

[9]  Yurii A. Neretin On Jordan Angles and the Triangle Inequality in Grassmann Manifolds , 2001 .

[10]  L L Wheeless,et al.  Classification of red blood cells as normal, sickle, or other abnormal, using a single image analysis feature. , 1994, Cytometry.

[11]  J. Miller Numerical Analysis , 1966, Nature.

[12]  Pete E. Lestrel,et al.  Fourier Descriptors and their Applications in Biology , 2008 .

[13]  Anuj Srivastava,et al.  Shape Analysis of Elastic Curves in Euclidean Spaces , 2011, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[14]  Stephan Huckemann,et al.  Intrinsic inference on the mean geodesic of planar shapes and tree discrimination by leaf growth , 2010, 1009.3203.

[15]  William A. Yasnoff,et al.  Computer Techniques for Cell Analysis in Hematology , 1977 .

[16]  Anuj Srivastava,et al.  Analysis of planar shapes using geodesic paths on shape spaces , 2004, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[17]  U. Grenander,et al.  A Stochastic Shape and Color Model for Defect Detection in Potatoes , 1993 .

[18]  Volodymyr V. Kindratenko,et al.  On Using Functions to Describe the Shape , 2003, Journal of Mathematical Imaging and Vision.

[19]  Sven Loncaric,et al.  A survey of shape analysis techniques , 1998, Pattern Recognit..

[20]  Ralph Roskies,et al.  Fourier Descriptors for Plane Closed Curves , 1972, IEEE Transactions on Computers.

[21]  D. Mumford,et al.  A Metric on Shape Space with Explicit Geodesics , 2007, 0706.4299.

[22]  Dong Hwan Lee,et al.  Multi-shape erythrocyte deformability analysis by imaging technique , 2010 .

[23]  J W Bacus,et al.  Image processing for automated erythrocyte classification. , 1976, The journal of histochemistry and cytochemistry : official journal of the Histochemistry Society.

[24]  Swaminathan Ramakrishnan,et al.  Analysis on the erythrocyte shape changes using wavelet transforms. , 2005, Clinical hemorheology and microcirculation.

[25]  R Core Team,et al.  R: A language and environment for statistical computing. , 2014 .

[26]  Asger Hobolth,et al.  A continuous parametric shape model , 2003 .

[27]  Asger Hobolth,et al.  Modelling stochastic changes in curve shape, with an application to cancer diagnostics , 2000, Advances in Applied Probability.

[28]  T Asakura,et al.  Percentage of reversibly and irreversibly sickled cells are altered by the method of blood drawing and storage conditions. , 1996, Blood cells, molecules & diseases.

[29]  J. Sethian,et al.  Fronts propagating with curvature-dependent speed: algorithms based on Hamilton-Jacobi formulations , 1988 .

[30]  Peter E. Hart,et al.  Nearest neighbor pattern classification , 1967, IEEE Trans. Inf. Theory.

[31]  L. Santaló Integral geometry and geometric probability , 1976 .