Intensity-based hierarchical clustering in CT-scans: application to interactive segmentation in cardiology

Jonathan Hadida; Christian Desrosiers; Luc Duong

doi:10.1117/12.878285

1 March 2011 Intensity-based hierarchical clustering in CT-scans: application to interactive segmentation in cardiology

Jonathan Hadida, Christian Desrosiers, Luc Duong

Proceedings Volume 7964, Medical Imaging 2011: Visualization, Image-Guided Procedures, and Modeling; 79641R (2011) https://doi.org/10.1117/12.878285
Event: SPIE Medical Imaging, 2011, Lake Buena Vista (Orlando), Florida, United States

Abstract

The segmentation of anatomical structures in Computed Tomography Angiography (CTA) is a pre-operative task useful in image guided surgery. Even though very robust and precise methods have been developed to help achieving a reliable segmentation (level sets, active contours, etc), it remains very time consuming both in terms of manual interactions and in terms of computation time. The goal of this study is to present a fast method to find coarse anatomical structures in CTA with few parameters, based on hierarchical clustering. The algorithm is organized as follows: first, a fast non-parametric histogram clustering method is proposed to compute a piecewise constant mask. A second step then indexes all the space-connected regions in the piecewise constant mask. Finally, a hierarchical clustering is achieved to build a graph representing the connections between the various regions in the piecewise constant mask. This step builds up a structural knowledge about the image. Several interactive features for segmentation are presented, for instance association or disassociation of anatomical structures. A comparison with the Mean-Shift algorithm is presented.

Citation Download Citation

Jonathan Hadida, Christian Desrosiers, and Luc Duong "Intensity-based hierarchical clustering in CT-scans: application to interactive segmentation in cardiology", Proc. SPIE 7964, Medical Imaging 2011: Visualization, Image-Guided Procedures, and Modeling, 79641R (1 March 2011); https://doi.org/10.1117/12.878285

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KEYWORDS

Image segmentation

Image processing algorithms and systems

Arteries

Cardiology

Image processing

Visualization

Angiography

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