Image Processing Reference
der the GUP Research Grant No. 04H40, titled “Dimension reduction and Data Clustering for
High Dimensional and Large Datasets.”
graphy is considered as one of the primary studies on tumor diagnosis and breast disease. It is
not recommended to have frequent MRIs for high-risk women because of radiation. Scientists
are looking for the best way for quantifying breast densities of MR images in healthy women
to assessment of healthy breast composition. They are trying to find the link between breast
cancer and breast composition. In addition, these assessments can help future researches to
estimate potential risk of breast cancer. Our goal is normalizing MR images for quantifying
density of the breast accurately, because this factor is an important marker in diagnosis of
breast cancer. However, MR images has some limitations, such as, they sometimes are in low
contrast, the dependence of MR image quality upon the condition the image is acquired, ideal
image situation is never realized practically, bias field, etc.
problem in MR image segmentation. It is a smooth and low-frequency signal that corrupts
MRI images specially those produced via old MRI machines. Bias ield is atributed to eddy
ate high-frequency contents of MRI image like contours and edges, blur the images, change
the intensity of image pixels, as a result, same tissues have different gray level distribution in
the image. In order to decrease the aforementioned restriction, research teams throughout the
According to their studies there are two main methods for bias field correction: prospective
and retrospective methods. The prospective methods try to solve this problem in the process
of acquisition by using special hardware. These approaches can only delete inhomogeneities
due to hardware imperfections.
Retrospective approaches have been more developed; they are classified into two groups:
irst group uses the segmentation-based methods for computing the bias field and the second
one works directly on data.
Although the segmentation-based approaches, such as expectation maximization (EM) al-
work solely on intensity of image and they are able only to estimate and correct low amplitude
a problem of entropy minimization. Another method in this category is N3 (nonparametric
After digitalizing the images, N3 method was used for bias field estimation and correction.
The input of proposed method includes different percentages of intensity inhomogeneities,
while the output consists of bias field corrected images with very high quality, which indicate
breast regions more precisely.