HoTPiG: A novel geometrical feature for vessel morphometry and its application to cerebral aneurysm detection

Abstract

A novel feature set for medical image analysis, named HoTPiG (Histogram of Triangular Paths in Graph), is presented. The feature set is designed to detect morphologically abnormal lesions in branching tree-like structures such as vessels. Given a graph structure extracted from a binarized volume, the proposed feature extraction algorithm can effectively encode both the morphological characteristics and the local branching pattern of the structure around each graph node (e.g., each voxel in the vessel). The features are derived from a 3-D histogram whose bins represent a triplet of shortest path distances between the target node and all possible node pairs near the target node. The extracted feature set is a vector with a fixed length and is readily applicable to state-of-the-art machine learning methods. Furthermore, since our method can handle vessel-like structures without thinning or centerline extraction processes, it is free from the “short-hair” problem and local features of vessels such as caliper changes and bumps are also encoded as a whole. Using the proposed feature set, a cerebral aneurysm detection application for clinical magnetic resonance angiography (MRA) images was implemented. In an evaluation with 300 datasets, the sensitivities of aneurysm detection were 81.8% and 89.2% when the numbers of false positives were 3 and 10 per case, respectively, thus validating the effectiveness of the proposed feature set.