31-01-2013, 09:08 AM
A Level Set Method for Image Segmentation in the Presence of Intensity Inhomogeneities With Application to MRI
1A Level Set Method.pdf (Size: 2.01 MB / Downloads: 48)
Abstract
Intensity inhomogeneity often occurs in real-world
images, which presents a considerable challenge in image segmentation.
The most widely used image segmentation algorithms are
region-based and typically rely on the homogeneity of the image
intensities in the regions of interest, which often fail to provide
accurate segmentation results due to the intensity inhomogeneity.
This paper proposes a novel region-based method for image
segmentation, which is able to deal with intensity inhomogeneities
in the segmentation. First, based on the model of images with
intensity inhomogeneities, we derive a local intensity clustering
property of the image intensities, and define a local clustering criterion
function for the image intensities in a neighborhood of each
point. This local clustering criterion function is then integrated
with respect to the neighborhood center to give a global criterion
of image segmentation. In a level set formulation, this criterion
defines an energy in terms of the level set functions that represent a
partition of the image domain and a bias field that accounts for the
intensity inhomogeneity of the image.
INTRODUCTION
I NTENSITY inhomogeneity often occurs in real-world images
due to various factors, such as spatial variations in illumination
and imperfections of imaging devices, which com- plicates many problems in image processing and computer vision.
In particular, image segmentation may be considerably difficult
for images with intensity inhomogeneities due to the overlaps
between the ranges of the intensities in the regions to segmented.
This makes it impossible to identify these regions based
on the pixel intensity. Those widely used image segmentation
algorithms [4], [17], [18], [23] usually rely on intensity homogeneity,
and therefore are not applicable to images with intensity
inhomogeneities. In general, intensity inhomogeneity has been
a challenging difficulty in image segmentation.
Local Intensity Clustering Property
Region-based image segmentation methods typically relies
on a specific region descriptor (e.g. intensity mean or a Gaussian
distribution) of the intensities in each region to be segmented.
However, it is difficult to give such a region descriptor for images
with intensity inhomogeneities. Moreover, intensity inhomogeneities
often lead to overlap between the distributions of
the intensities in the regions . Therefore, it is impossible
to segment these regions directly based on the pixel intensities.
Nevertheless, the property of local intensities is simple,
which can be effectively exploited in the formulation of our
method for image segmentation with simultaneous estimation
of the bias field.
Numerical Implementation
The implementation of our method is straightforward. The
level set evolution in (22) and (26) can be implemented by using
the same finite difference scheme as for the DRLSE provided
in [11]. While we use an easy full domain implementation to
implement the proposed level set method in this paper, it is
worth pointing out that the narrow band implementation of the
DRLSE, provided in [11], can be also used to implement the
proposed method, which would greatly reduce the computational
cost and make the algorithm significantly faster than the
full domain implementation.
CONCLUSION
We have presented a variational level set framework for
segmentation and bias correction of images with intensity
inhomogeneities. Based on a generally accepted model of
images with intensity inhomogeneities and a derived local
intensity clustering property, we define an energy of the level
set functions that represent a partition of the image domain
and a bias field that accounts for the intensity inhomogeneity.
Segmentation and bias field estimation are therefore jointly
performed by minimizing the proposed energy functional.
The slowly varying property of the bias field derived from
the proposed energy is naturally ensured by the data term
in our variational framework, without the need to impose an
explicit smoothing term on the bias field. Our method is much
more robust to initialization than the piecewise smooth model.
Experimental results have demonstrated superior performance
of our method in terms of accuracy, efficiency, and robustness.