3D vascular decomposition and classification for Computer-Aided detection

Ashirwad Chowriappa, Sarthak Salunke, Maxim Mokin, Peter Kan, Peter D. Scott

Research output: Contribution to journalArticlepeer-review


In this study, we propose a weighted approximate convex decomposition (WACD) and classification methodology for computer-aided detection (CADe) and analysis. We start by addressing the problem of vascular decomposition as a cluster optimization problem and introduce a methodology for compact geometric decomposition. The classification of decomposed vessel sections is performed using the most relevant eigenvalues obtained through feature selection. The method was validated using presegmented sections of vasculature archived for 98 aneurysms in 112 patients. We test first for vascular decomposition and next for classification. The proposed method produced promising results with an estimated 81.5% of the vessel sections correctly decomposed. Recursive feature elimination was performed to find the most compact and informative set of features. We showed that the selected subset of eigenvalues produces minimum error and improved classifier precision. The method was also validated on a longitudinal study of four cases having internal cerebral aneurysms. Volumetric and surface area comparisons were made between expert-segmented sections and WACD classified sections containing aneurysms. Results suggest that the approach is able to classify and detect changes in aneurysm volumes and surface areas close to that segmented by an expert.

Original languageEnglish (US)
Article number6557462
Pages (from-to)3514-3523
Number of pages10
JournalIEEE Transactions on Biomedical Engineering
Issue number12
StatePublished - Dec 2013
Externally publishedYes


  • Aneurysm
  • classification
  • segmentation
  • spectral
  • vascular decomposition

ASJC Scopus subject areas

  • Biomedical Engineering


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