Biomedical Engineering Reference
In-Depth Information
produced by a uniform phantom to correct for MRI images of any orienta-
tion. Similarly, Tincher
et al.
[33] modeled the inhomogeneity function by a
second-order polynomial and fitted it to a uniform phantom-scanned MR image.
These phantom approaches, however, have the drawback that the geometry
relationship of the coils and the image data is typically not available with the
image data. They also require the same acquisition parameters for the phan-
tom scan and the patient. In addition, these approaches assume the intensity
corruption effects are the same for different patients, which is not valid in
general [31].
The homomorphic filtering approach to remove the multiplicative effect of
the inhomogeneity has been commonly used due to its easy and efficient im-
plementation [29, 34]. This method, however, is effective only on images with
relatively low contrast. Some researchers [33, 35] reported undesirable artifacts
with this approach.
Dawant
et al.
[35] used operator-selected reference points in the image to
guide the construction of a thin-plate spline correction surface. The performance
of this method depends substantially on the labeling of the reference points.
Considerable user interactions are usually required to obtain good correction
results. More recently, Gilles
et al.
[36] proposed an automatic and iterative B-
spline fitting algorithm for the intensity inhomogeneity correction of breast MR
images. The application of this algorithm is restricted to MR images with a single
dominant tissue class, such as breast MR images. Another polynomial surface
fitting method [37] was proposed based on the assumption that the number of
tissue classes, the true means, and standard deviations of all the tissue classes in
the image are given. Unfortunately, the required statistical information is usually
not available.
A different approach used to segment images with intensity inhomogeneities
is to simultaneously compensate for the shading effect while segmenting the
image. This approach has the advantage of being able to use intermediate infor-
mation from the segmentation while performing the correction. Recently, Wells
et al.
[28] developed a new statistical approach based on the EM algorithm to
solve the bias field correction problem and the tissue classification problem.
Guillemaud
et al.
[38] further refined this technique by introducing the extra
class “other.” There are two main disadvantages of this EM approach. First,
the EM algorithm is extremely computationally intensive, especially for large
