INTRODUCTION
Several of the commonly used medical screening techniques used for breast screening
such are; mammography, ultrasound and Magnetic Resonance Imaging (MRI). Mammograms
and ultrasound are difficult to interpret because of the sensitivity of screening
is affected by the image quality and the radiologist’s level of expertise
(Rangayyan et al., 2007). While MRI images are
clear and have better contrast between most of the breast regions. For that
reason, MRI is used for breast screening. Although, some regions in MRI breast
images have the same intensity values and that may affect the segmentation process.
Therefore, the MRI breast images are enhanced using computer techniques of image processing in Computer Aided Detection (CAD) systems that help radiologists in detecting the tumour regions.
The intensity level between the breast skinline and the tumor regions is similar
in the majority of MRI breast cases. Thus, segmenting and removing the breast
skinline region methods will be discussed in this study. Without this, segmentation
errors may occur if not managed correctly (Solves Llorens
et al., 2012). This process would be more importance in the next
stage of the automatic tumor segmentation in CAD systems.
MATERIALS AND METHODS
The proposed approach of MRI breast skin line segmentation and removal consists of three main stages. These are: image preprocessing using the median filter, followed by skin line segmentation using Level Set Active Contour which is applied on the resultant image in the first stage. The last stage involves skin line removal using Morphological Thinning. The following is a detailed description of the processes involved.
Image preprocessing: As mentioned, the preprocessing phase is the first process that is executed. The median filter is applied in order to enhance the images’ resolution and to reduce the presence of the salt and pepper noise.
Breast skinline segmentation: To segment the breast skin line, Level
Set Active Contour algorithm (Li et al., 2005)
is used. Level set formulation without reinitialization (which is specially
known as Chunming’s algorithm) is included as one of the active contour’s
detection algorithms. Active contours are dynamic curves that move toward the
mass border. An external energy moves the zero level curves toward the mass
border using the edge indicator function g that is defined in Eq.
1:
where, I is the image, G_{σ} is the Gaussian kernel with a standard deviation σ. By changing the σ parameter value and the number of the iterations N_{LS}, the resultant detection is changed. Figure 1 shows the different results after Level Set Active Contour algorithm with different values of σ and N_{LS} is applied.
Based on Fig. 1, it can be observed that the higher number of iterations N_{LS} gives better detection. Therefore, the best value for N_{LS} is 700 while the best value of σ is 1.5. These choices of the values are generic for all breast MRI images after resizing the image to be 288x288 pixels.
Breast skinline removal: In order to delete the detected breast skin
border, the Morphological Thinning Algorithm as described by Lam
et al. (1992) is used. The image is divided into two distinct subfields.
Then in the first subiteration, pixel p from the first subfield is deleted
if and only if the first three conditions as shown below are true.

Fig. 1(af): 
Different results after the application of level set active
contour algorithm with different values of σ and N_{LS}. (a)
σ = 3, N_{LS} = 100, (b) σ = 1.5, N_{LS} =100,
(c) σ = 3, N_{LS} = 300, (d) σ = 1.5, N_{LS} =
300, (e) σ = 3, N_{LS} = 700 and (f) σ = 1.5, N_{LS}
= 700 
In the second subiteration, pixel p from the second sub field is deleted
if and only if, the first two and fourth conditions are true.
First condition:
Where:
x_{1}, x_{2}, ..., x_{8} are the values of the eight neighbours of p, starting with the east neighbour and numbered in counterclockwise order.
Second condition:
Where:
Third condition:
Forth condition:
The two sub iterations formulate the main iteration of the thinning algorithm.
The thinning level depends on the number of iterations. Whenever the number
of iterations is increased, there would be more shrinking of the image’s
border. The Morphological Thinning Algorithm only accepts a binary version of
the image. Therefore, the resultant image after the Level Set Active Contour
algorithm would be converted to binary image. Furthermore, after the thinning
procedure, the binary image is reconverted into its original grey scale representation.
Figure 2 shows the results after the Morphological Thinning
algorithm is applied using three different iteration numbers (N_{Th}
= 1, N_{Th} = 3 and N_{Th} = 7).

Fig. 2(af): 
Results after applying morphological thinning algorithm with
three different iteration numbers on the resultant image of level set active
contour algorithm (a) After applying thinning (N_{Th} = 1) on binary
image, (b) After reconverting thinning image (N_{Th} = 1) to its
original grayscale, (c) After applying thinning (N_{Th} = 3) on
binary image, (d) After reconverting thinning image (N_{Th} = 3)
to its original grayscale, (e) After applying thinning (N_{Th} =
7) on binary image and (f) After converting thinning image (N_{Th}
= 7) to its original grayscale 
From Fig. 2, N_{Th} =7 is the best iteration number which enables the thinning algorithm whereby the deleted amount of pixels equals the thickness of the breast skin.
Evaluation approach: To evaluate the results of the segmentation and
the removal processes, pixel based evaluation approach (Rosin
and Ioannidis, 2003) is used. This approach compares between pixels of processed
image (R_{t}) and ground truth (R_{g}). The following measures
are used in this work to build the comparison; Dice index, Jaccard index, True
Positive Fraction (TPF), False Negative Fraction (FNF), False Positive Fraction
(FPF) and True Negative Fraction (TNF). The calculations are made using the
equations 1015. (McNeil
and Hanley, 1984; Metz, 1986; Chalana
and Kim, 1997; Rosin and Ioannidis, 2003; Fenster
and Chiu, 2005; Prasad et al., 2011):
As the basis of measurements: for the Dice, Jaccard, TPF and TNF approaches, whenever the results are higher, the performance is better. Meanwhile for the rest of the basis of measurements which are MCR, FNF and FPF, the lower results indicate better performance.
RESULTS
The methodology explained earlier is applied and tested on the RIDER Breast
MRI dataset which is downloaded from the National Biomedical Imaging Archive
(NBIA)(US National Cancer Institute, 2007). This website
belongs to the U.S. National Cancer Institute. The dataset includes breast MRI
images for five patients. All images are Axial 288x288 pixels. Three sequences
are selected for each patient to be used in the experiments as test images.
Two Ground Truth (GT) images sets are prepared manually for the purpose of the
evaluation; the first GT is to evaluate the segmentation process while the second
GT set is to evaluate the removal process.
In the breast skinline segmentation stage, the chosen parameters for Level Set algorithm are σ = 1.5 and N_{LS} =700 while N_{Th} =7 is the chosen parameter for the Thinning algorithm for the removal stage. The parameters have been selected using the Trial and Error method. Figure 3 shows five RIDER patients images and the processes of breast skinline segmentation and deletion processes.
The proposed approach is applied on all the RIDER dataset images. Then the
calculations are made by comparing these with the ground truth using the evaluation
measures eq.1015. The results of the
segmentation stage are tabled as in Table 1 while the results
of the skinline removal are tabled as in Table 2.

Fig. 3(ao): 
(ae) Breast skin segmentation and removal processes on the
five RIDER images; original images (fj) Five images after applying level
set algorithm with σ = 1.5 and N_{LS} = 700 and (ko) Then
applying the Thinning Algorithm with N_{Th} = 7 
Table 1: 
Results of skinline segmentation for RIDER MRI breast images
using evaluation measures (Dice, Jaccard, MCR, TPF, FNF, FPF and TNF) 

Table 2: 
Results of skinline removal for RIDER MRI breast images
using evaluation measures (Dice, Jaccard, MCR, TPF, FNF, FPF and TNF) 

DISCUSSION
From Table 1 and 2, it can be concluded that the proposed approached scored good results using the various measures. For the skinline segmentation stage, the mean of results was high with (Dice = 0.9607), (Jaccard = 0.9275), (MCR = 0.0354), (TPF = 0.9646), (FNF = 0.0354), (FPF = 0.0401) and (TNF = 0.9599). For the skinline removal stage the mean of results was high with (Dice = 0.9099), (Jaccard = 0.8394), (MCR = 0.1453), (TPF = 0.8547), (FNF = 0.1453), (FPF = 0.0186) and (TNF = 0.9814).
Then, Receiver Operating Characteristic (ROC) is applied to illustrate by drawing curve the True Positive Fraction compared with the False Positive Fraction as shown in Fig. 4 and 5.
From the curves, it can be observed that the Area under the Curve (AUC) for the segmentation stage is 0.9902 and AUC for the removal stage is 0.9507. The high AUC indicates improved segmentation performance.

Fig. 4: 
ROC curve for MRI breast skinline segmentation 

Fig. 5: 
ROC curve for MRI breast skinline removal 
CONCLUSION
An integration method of Level Set Active Contour algorithm and Morphological Thinning algorithm is presented in this work. The procedure for segmenting and removing the skinline region from MRI breast images is a necessary process before the next level of the breast segmentation processes. The proposed approach is divided into three stages; preprocessing using median filter, skinline segmentation using Level Set Active Contour which is then followed by skinline removal using Morphological Thinning Algorithm.
The study is then tested by applying the methodology on the RIDER breast MRI dataset. The ground truth is manually prepared from the dataset as a benchmark. The evaluation statistic using six measures shows that the performance is significantly high compared with ground truth. The results of the segmentation stage are Dice = 0.9607, Jaccard = 0.9275, MCR = 0.0354, TPF = 0.9646, FNF = 0.0354, FPF = 0.0401 and TNF = 0.9599. While the results of the removal stage are Dice = 0.9099, Jaccard = 0.8394, MCR = 0.1453, TPF = 0.8547, FNF = 0.1453, FPF = 0.0186 and TNF = 0.9814. The proposed approach could also be implemented on other types of breast images such as the XRay mammogram images. However, it is necessary to take into account of changing the parameter values of the algorithms to fit specifications of the type of breast images.