Dr. Peter Zhilkin Profile

Dr. Peter Zhilkin

at National Research Council Canada

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Proceedings Article | 19 March 2008 Paper

Multi-phase image segmentation using level sets

Peter Zhilkin, Murray Alexander

Proceedings Volume 6914, 69143V (2008) https://doi.org/10.1117/12.770700

KEYWORDS: Image segmentation, Head, 3D modeling, Magnetic resonance imaging, Tumors, 3D image processing, Neuroimaging, Brain, Image processing algorithms and systems, Adaptive optics

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A hierarchical multi-phase image segmentation using the original and a modified Chan-Vese 2-phase method is considered. A method of capturing features inside a pre-selected region of interest (ROI) is proposed that effectively restricts the segmentation operation to the ROI. At the first step, a modified image is created by setting the portion of the image outside the ROI to a uniform intensity equal to the mean image intensity inside the ROI. Effectively, this procedure partitions the initial image into two phases, in such a way that the ROI effectively becomes a 'segmented' feature. At the second step, the segmentation procedure is applied to the modified image, partitioning the image in two phases - object and background - inside the ROI. By confining segmentation to the ROI, it is shown, using an artificial image, that objects can be discriminated that could not have been found if segmentation had been performed on the entire image. If necessary, this second step can be repeated to further segment features of interest within the ROI, thereby providing a multi-phase segmentation procedure. ROI placement around features of interest requires prior knowledge, and may be derived from an atlas or manually prescribed by the operator. In this way, segmentation is possible on low-contrast features of interest, while ignoring features irrelevant for a particular application. Examples are provided for segmentation of several 2D/3D images performed both on entire images and inside a ROI.

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