HoTPiG: A Novel Geometrical Feature for Vessel Morphometry and Its Application to Cerebral Aneurysm Detection
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 st
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Department of Radiology, The University of Tokyo Hospital, 7-3-1 Hongo, Bunkyo-ku, Tokyo, Japan 2 Tokyo University of Agriculture and Technology, 2-24-16 Nakamachi, Koganei, Tokyo, Japan 3 Department of Computational Diagnostic Radiology and Preventive Medicine, The University of Tokyo Hospital, 7-3-1 Hongo, Bunkyo-ku, Tokyo, Japan 4 Department of Intelligent Systems, Graduate School of Information Sciences, Hiroshima City University, 3-4-1 Otsuka-higashi, Asaminami-ku, Hiroshima, Japan [email protected]
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. Keywords: graph feature, computer-assisted detection, MR arteriography, cerebral aneurysm, support vector machine.
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Introduction
A branching treelike structure is one of the major types of structure in the human body. For example, a wide variety of vessels (blood vessels, bronchi, bile ducts, etc.) © Springer International Publishing Switzerland 2015 N. Navab et al. (Eds.): MICCAI 2015, Part II, LNCS 9350, pp. 103–110, 2015. DOI: 10.1007/978-3-319-24571-3_13
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have a treelike structure. Quite a large number of diseases affect these vascular structures and cause pathological shape changes including narrowing, occlusion, and dilation. Vascular diseases, including cerebral infarction and coronary occlusive disease, are one of the major causes of death in advanced nations. Since precise evaluation of the shape of vessels is essential in diagnosing these diseases, computerassisted detection/diagnosis (CAD) of these treelike structures is required. Among the vascular diseases, cerebral aneurysm has been one of the targets of CAD applications [1-3]. Although
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