Massachusetts Institute of Technology

The Brigham and Women's Hospital, Inc.

Axys Pharmaceuticals, Inc.

Tasc, Inc.

Tomas Lozano-Perez

W Eric Grimson

William M Wells, Iii

Gil J Ettinger

Steven J White

Ronald Kikinis

David Chapman

Richard H Lathrop

Ajay N Jain

Roger Critchlow

Thomas Glen Dietterich

Machine-learning approach to modeling biological activity for molecular

An image data registration system and method of storing a first data set of three-dimensional image data associated with a predetermined portion of an object with reference to a first coordinate frame; obtaining and storing a second data set of three-dimensional image data associated with a surface of the predetermined portion of the object with reference to a second coordinate frame; obtaining and storing a third data set of three-dimensional image data associated with a probe in the vicinity of the predetermined portion of the object with reference to a third coordinate frame; and registering the first, second and third data sets to generate a matched image data set in which the first, second and third coordinate frames are relatively aligned with one another.

System and method of registration of three-dimensional data sets

The present invention provides an image data registration system and method of storing a first data set of three-dimensional image data associated with a predetermined portion of an object with reference to a first coordinate frame; obtaining and storing a second data set of three-dimensional image data associated with a surface of the predetermined portion of the object with reference to a second coordinate frame; and registering the first data set and the second data set to generate a matched image data set in which the first coordinate frame is aligned with the second coordinate frame. In an alternative embodiment, the present invention provides a method of registering three-dimensional data sets, comprising generating a first data set of image data with reference to a first coordinate frame; generating a second data set of image data with reference to a second coordinate frame; selecting a predetermined subset of data points from the first data set; matching the subset of data points to all possible corresponding subsets of data points associated with the second data set and producing a set of hypothesized transformation data sets; and sequentially mapping said second data set to each of the hypothesized transformation data sets to form a solution data set, the solution data set representative of a highly accurate transformation of the second data into the first coordinate frame associated with the first data set.

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West Newton, MA, United States of America

Tomas Lozano-Perez