A 2-D/3-D model-based method to quantify the complexity of microvasculature imaged by in vivo multiphoton microscopy.
This paper presents model-based information-theoretic methods to quantify the complexity of tumor microvasculature, taking into account shape, textural, and structural irregularities. The proposed techniques are completely automated, and are applicable to optical slices (3-D) or projection images (2-D). Improvements upon the prior literature include: (i) measuring local (vessel segment) as well as global (entire image) vascular complexity without requiring explicit segmentation or tracing; (ii) focusing on the vessel boundaries in the complexity estimate; and (iii) added robustness to image artifacts common to tumor microvasculature images. Vessels are modeled using a family of super-Gaussian functions that are based on the superquadric modeling primitive common in computer vision. The superquadric generalizes a simple ellipsoid by including shape parameters that allow it to approximate a cylinder with elliptical cross-sections (generalized cylinder). The super-Gaussian is obtained by composing a superquadric with an exponential function giving a form that is similar to a standard Gaussian function but with the ability to produce level sets that approximate generalized cylinders. Importantly, the super-Gaussian is continuous and differentiable so it can be fit to image data using robust non-linear regression. This fitting enables quantification of the intrinsic complexity of vessel data vis-a-vis the super-Gaussian model within a minimum message length (MML) framework. The resulting measures are expressed in units of information (bits). Synthetic and real-data examples are provided to illustrate the proposed measures.
Tyrrell
James A
JA
Department of Electrical, Computer and Systems Engineering, Rensselaer Polytechnic Institute, Troy, NY 12180-3590, USA.
Mahadevan
Vijay
V
Tong
Ricky T
RT
Brown
Edward B
EB
Jain
Rakesh K
RK
Roysam
Badrinath
B
D000465:Algorithms; D000818:Animals; D001808:Blood Vessels; D045744:Cell Line, Tumor; D003198:Computer Simulation; D007089:Image Enhancement; D007091:Image Processing, Computer-Assisted; D021621:Imaging, Three-Dimensional; D016014:Linear Models; D051379:Mice; D016513:Mice, SCID; D008833:Microcirculation; D018613:Microscopy, Confocal; D008954:Models, Biological; D015233:Models, Statistical; D009368:Neoplasm Transplantation; D009369:Neoplasms / Q000098:blood supply*; D009389:Neovascularization, Pathologic; D016011:Normal Distribution; D010363:Pattern Recognition, Automated; D017785:Photons; D012044:Regression Analysis; D015203:Reproducibility of Results; D013997:Time Factors