Vascular decomposition using weighted approximate convex decomposition

Chowriappa, Ashirwad; Kesavadas, Thenkurussi; Mokin, Maxim; Kán, Peter; Salunke, Sarthak; Natarajan, Sabareesh K.; Scott, Peter

doi:10.1007/s11548-012-0766-6

Cited by 4 publications

(1 citation statement)

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“…The literature shows several 2-D/3-D vascular decomposition and segmentation methods [6]- [11]. For extended reviews on vessel segmentation algorithms, we refer the reader to the following surveys [12]- [15].…”

Section: A Previous Work On Decomposition and Segmentationmentioning

confidence: 99%

3D Vascular Decomposition and Classification for Computer-Aided Detection

Chowriappa

Salunke

Mokin³

et al. 2013

IEEE Trans. Biomed. Eng.

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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.

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Section: A Previous Work On Decomposition and Segmentationmentioning

confidence: 99%

3D Vascular Decomposition and Classification for Computer-Aided Detection

Chowriappa

Salunke

Mokin³

et al. 2013

IEEE Trans. Biomed. Eng.

Self Cite

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3-D Vascular Skeleton Extraction and Decomposition

Chowriappa

Seo

Salunke

et al. 2014

IEEE J. Biomed. Health Inform.

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We introduce a novel vascular skeleton extraction and decomposition technique for computer-assisted diagnosis and analysis. We start by addressing the problem of vascular decomposition as a cluster optimization problem and present a methodology for weighted convex approximations. Decomposed vessel structures are then grouped using the vessel skeleton, extracted using a Laplace-based operator. The method is validated using presegmented sections of vasculature archived for 98 aneurysms in 112 patients. We test first for vascular decomposition and next for vessel skeleton extraction. The proposed method produced promising results with an estimated 80.5% of the vessel sections correctly decomposed and 92.9% of the vessel sections having the correct number of skeletal branches, identified by a clinical radiological expert. Next, the method was validated on longitudinal study data from n = 4 subjects, where vascular skeleton extraction and decomposition was performed. Volumetric and surface area comparisons were made between expert segmented sections and the proposed approach on sections containing aneurysms. Results suggest that the method is able to detect changes in aneurysm volumes and surface areas close to that segmented by an expert.

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