Biomedical Engineering Reference
In-Depth Information
4
IMAGE SEGMENTATION USING
THE LEVEL SET METHOD
Yingge Qu, Pheng Ann Heng, and Tien-Tsin Wong
Department of Computer Science and Engineering
The Chinese University of Hong Kong
Construction of a speed function is crucial in applying the level set method to medical
image segmentation. In this chapter we focus on the construction of the speed function.
First of all, we have to investigate the curvature term in the speed function, and then show
how to transform the image segmentation problem into an interface propagating problem.
When segmenting medical images with classical level set methods, the propagating front
may not be able to capture the real boundaries, although they are obvious to the human
eye. We propose two formulations to enhance the speed function in level set methods, in
order to tackle the segmentation problem of tagged MR images. First, a relaxation factor
is introduced, aimed at relaxing the boundary condition when the boundary is unclear or
blurry. Second, in order to incorporate human visual sensitive information from the image, a
simple and general model is introduced to incorporate shape, texture, and color features. By
further extending this model, we present a unified approach for segmenting and tracking of
the high-resolution color anatomical Chinese Visible Human (CVH) data. The underlying
relationship of these two applications relies on the proposed variational framework for the
speed function. Our proposed method can be used to segment the first slice of the volume
data. Then, based on the extracted boundary on the first slice, our method can also be
adapted to track the boundary of the homogeneous organs among the subsequent serial
images. In addition to the promising segmentation results, the tracking procedure requires
only a small amount of user intervention.
