Proceedings Article | 19 March 2008
Proc. SPIE. 6914, Medical Imaging 2008: Image Processing
KEYWORDS: Image processing algorithms and systems, Tumors, Magnetic resonance imaging, Image segmentation, Adaptive optics, 3D modeling, Head, Neuroimaging, 3D image processing, Brain
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 - <i>object</i> and
<i>background</i> - 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.
