Patent Document ID: 20100082692
Application ID: 12236789
Patent Flag: 0

Claim One:
1. A method of automatically classifying anatomical features shown in a medical image volume data set, the method comprising: obtaining a polyline tree comprising a plurality of connected points in the data set corresponding to the centrelines of vessels in an arterial tree imaged in the data set, each vessel in the arterial tree being represented by a segment in the polyline tree; forming a topological representation of the polyline tree which indicates the relative generational positions of the segments within the polyline tree; comparing the topological representation with a set of topological rules specifying anatomically permissible relative generational positions of vessels in an arterial tree to identify feasible anatomical classifications for the vessels represented by each segment in the polyline tree; generating a set of candidate labeled polyline trees by associating labels representing the identified anatomical classifications with the corresponding segments in the polyline tree, each candidate labeled polyline tree being one combination of the identified feasible anatomical classifications; comparing each candidate labeled polyline tree with a set of geometric rules specifying anatomically permissible spatial attributes of vessels in an arterial tree which can be determined from a polyline representation and rejecting any candidate having one or more labels representing vessels which do not comply with the geometric rules; calculating a figure of merit for each remaining candidate labeled polyline tree by comparing features of the vessels represented in the polyline tree with known features of vessels in the anatomical classes indicated by the labels associated with the segments representing the vessels to determine a probability of the correctness of the labels in each candidate, the figure of merit reflecting the probability; and identifying the candidate labeled polyline tree having the best figure of merit.