Brain Tumor Segmentation Using A Novel Unified
Legendra Polynomial Algorithm (ULPA) in MRI
Images
C. Jaspin Jeba Sheela
Reg.No. 17221282162010,Research Scholar, St.Xavier’s Autonomous College,
Palayamkottai affiliated to Manonmaniam Sundaranar University, Abishekapatti,
Tirunelveli 627012, Tamil Nadu, India
E-mail: [email protected]
G. Suganthi
Associate Professor, Department of Computer Science, Women’s Christian College,
Nagercoil affiliated to Manonmaniam Sundaranar University, Abishekapatti, Tirunelveli
627012, Tamil Nadu, India
E-mail: [email protected]
Abstract
The segmentation, detection, and extraction of infected tumor area from
Magnetic Resonance MR Images are a prime concern as they are tedious and time
taking task performed by radiologists or clinical experts. This study analyzes the ways to
improve performance and reduce the complexity involved in the medical image
segmentation process. This paper describes a novel Unified Legendre Polynomial
Algorithm which is an important segmentation performance for automatic tumor
segmentation. In this paper, Spatial Fuzzy C-Means clustering is used to evaluate the
Region Of Interest (ROI) in MRI images. A two step approach is designed to upgrade the
tumor border with region merging and improved distance regularization level. BRATs
2015 training database, evaluates the accuracy and robustness of this method with
respect performance scores, Dice, Positive Predictive Value (PPV), Sensitivity,
Hausdorff Distance (HD) and Euclidean Distance (ED). In general, the proposed
method is effective in segmenting tumor in MRI images, and it has the potential to
identify the tumors in daily clinical, routine examination.
Keywords: MR Images, region growing, Legendre polynomial, Segmentation, Tumor
detection
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1. Introduction
The brain is a highly specialized organ. It serves as the chief control mechanism
of the body. The brain is a soft, spongy mass of tissues. The brain is in charge of our
sensory organs. The brain has a very complex structure. The skull keeps the brain safe.
Tumors can directly destroy all healthy brain cells. The tumor detects the sudden growth
of a particular period.[12]
A Brain tumor is a formation of abnormal cells within the brain that can disrupt
the function of the brain. A Brain tumor is an uncontrolled growth of cells. When our
body functions in a normal manner, the cells die and get replaced by new cells. But this
normal cycle gets disrupted in tumors. Brain Tumor has various sizes, shapes, locations,
and appear in different image intensities. Manual detection of the tumor is a time
consuming task and is also inaccurate. There are many automated methods which can be
used for surgical and treatment planning. But they have specific drawbacks and
limitations.
Magnetic Resonance Imaging (MRI) is a type of scan that uses magnetic fields
and radio waves. MRI is the most common type of tests used to diagnose brain tumors.
It uses computers to create detailed images of the brain. The MRI is the best type of
brain tumor diagnosis than the others. It detects the brain tumors with high resolution and
ability to show clear brain structures [11]. MRI provides Brain and nerve tissues in
multiple planes without overlying bones. Medical images pose very important and useful
information about the anatomical structure of the human body.MRI is used for
visualizing the internal structure of the body.MRI provides rich information for brain
tumor diagnosis and treatment planning. MRI images also increase the difficulty in the
segmentation of tumor [1]. MRI is a challenging and critical task in Medical Image
Analysis. The advantage of MRI is that it has no radiation. MRI is a non invasive
medical image technique that provides high resolution images for the Structure [6]. MRI
images have been used frequently by radiologists. This study addresses the problems of
segmentation of abnormal brain tissues and normal tissues such as Gray Matter (GM),
White Matter (WM), seed point and Normalization from Magnetic Resonance (MR)
Images using feature extraction.
2. Literature Survey
The author Maddalena Strumia et al. [1] proposes the spatial lesion distribution
which plays a major role in diagnosing tumor segmentation based on an adaptive
geometric brain model. This is to motivate and formulate a new distance to evaluate the
quality of the brain tumor segmentation which shows the region of abnormalities. The
topological properties of the lesions and brain tissues are segmented as the white matter.
Paper [2] presents an automatic segmentation method based on Convolutional
Neural Networks (CNN) which explores the small 3X3 kernels. The small 3X3 Kernels
are used to design a deeper architecture and identify the use of intensity normalization
as a preprocessing step. It can consume much time to calculate the manual
segmentation.
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Paper [3] suggests the accuracy and feature extraction of MRI brain tumor
segmentation. The advantages of this paper is a correlation between intracranial
structure deformation and compression from MRI brain Tumor growth. The techniques
used in this paper are 3-Dimensional non rigid registration and deformation modeling
techniques.
Bjoern H. Menze et al [4] represents a generative probabilistic model for
segmentation of brain lesions in multidimensional MR Images. Gaussian Mixture and
probabilistic tissue atlas methods estimate the label map for a new image. These
methods extract a latent atlas prior distribution and the lesion posterior distribution
jointly from the image data sets.
Annemie Ribbens et al [5] evaluates a large amount of MRI segmentation and
comparison followed by the normal and abnormal MRIs. The main advantage of this
paper is to identify homogeneous subgroups automatically in the unsupervised method
and detect the relevant morphological features based on the segmentation. The atlas
method is optimally adapted for guiding the segmentation of each subgroup.
Colm Elliott [6] represents two stages of classification process one is Bayesian
classifier which provides a probabilistic brain tissue and another one is a random forest
based lesion level classification and it compares the truth segmentation and the manual
identification of MRI.
Zexuan Ji [7] represents the voxel’s neighborhood which satisfies the Gaussian
Mixture Model (GMM) and fuzzy local GMM (FLGMM) algorithm for automated
brain MR Image segmentation. It compares to the algorithm to state of the art
segmentation approaches in both synthetic and clinical data. It overcomes the
difficulties raised by noise, low contrast, and bias field and improves the accuracy of
brain MR Images segmentations.
Y Chen [8] proposes a new energy minimization framework for simultaneous
estimation of the intensity inhomogeneities and segmentation. It is formulated to
modify the objective function of the standard fuzzy C Means algorithm and the
functions which depends on coefficient of the basis function, membership ratio,
centroid and non-local information of MR Images.
A.Ortiz [9] represents the diagnosis of brain disorders. The main disadvantage of
this paper is that it has discovered different regions on the image without using prior
information. It consists of hybridizing multi objective optimization for feature selection
with a Growing Hierarchical Self Organizing Map (GHSOM) classifier and a
probability clustering method.
The above literature survey has revealed that some of the techniques
obtain only segmentation while some of the other techniques are invented to obtain
identification, detection and Feature Extraction.
3. Proposed MRI Segmented Method
Brain tumor segmentation is an important and challenging factor in the medical
image segmentation. The Tumor is mainly categorized into two groups. They are
probability based methods and non-probability based method [4]. The probability based
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methods directly learn the relationship between specific image features and
segmentation. This method is also used to find out the pixel labels and intensities. Non-
probability based segmentation method works efficiently. For example, Fuzzy C-
Means (FCM) algorithm is presented for automatic Tumor segmentation, to develop a
Legendre Polynomial Algorithm and to detect the Tumor borders in MRI brain Images.
It comprises of three components,
1) Estimate Region Of Interest (ROI) with fewer pseudo lesions.[1]
2) Detect the entire tumor regions [18]
3) Refine the final tumor border [14]
Figure 1. Flow diagram for segmentation of brain tumor Architecture
Input Image
Segmented
Output
Preprocessing
Identification
Tumor Detection
Tumor Segmentation
ROI
Seed Point
Initial Mask
Feature Extraction
Gray and White
matter
Seed Point
Normalization
Segmentation
SFCM
Morphological
Legendre Process
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4. Methodology
4.1. Region Of Interest (ROI) estimation
ROI should cover the malignant tumor region without any benign tumor regions.
This method of detecting the Region Of Interest (ROI) using Spatial Fuzzy C-Means
(SFCM) algorithm is limited to a certain extend [11]. A new region growing method is
used to detect the entire tumor region even if there are more than one tumor settlements,.
The Distance Regularized Level Set Evolution (DRLSE) method is used to refine the
final and accurate tumor border [7]. Some tumors have irregular shapes and improper
boundaries. Therefore it is very difficult to identify the exact boundary of effective
algorithm that improves the accuracy of clustering. Fuzzy is a soft computing technique
C Means clustering. Fuzzy C- Means is a combined form of clustering that identifies the
tumor [8][9][11] as well as reshapes the image. The input is in one-dimensional data [3].
Fuzzy C-Means clustering performs on two-dimensional data [14]. The following
concepts are important for ROI estimation.
a) Gray Matter:
It is easy to understand the tumor as the gray color represents tumor. The Gray
Matter (GM) and White Matter (WM) are an important clinical diagnosis [10]. Gray
matter is composed of neural and glial cell, It controls the brain activity. Gray matter
consists of mostly unmyelinated neurons, most of GM are inter-neuron [15].
b) White Matter:
White Matter is made up of mostly myelinated neurons and they connect gray
matter. White Matter consists of many eliminated axons that are connected to the
cerebral cortex with other brain regions [10]. White matter is similar to gray matter [15].
It helps to identify the tumor easily.
4.2. Segmentation Of Seed Points
The Brain Tumor segmentation is a process of identifying affected tumor tissues
and protects healthy tissues from damage and identifies the tumor tissue in the brain
accurately [1][12]. Seed point detects the starting point of the tumor. Morphological
Concept is used to find out the extracted seed point[5]. Due to the irregular shape and
size of the tumor. Thus the center point of the tumor is read and clearly identified [6].
Moreover, each of initial seeds, rather than their combination should start to grow
iteratively with region growing criteria to ensure the entire tumor detection [17].
Segmentation techniques that are based on finding the regions directly.
5. Region Growing
The region growing method is an interactive image segmentation
method. The initial region begins as the extract location of the seeds. This method selects
the starting and current seed points and it is based on the location information. It is easy
to select the centroids. These techniques are generally better in noisy images. The edges
are difficult to detect. Identifying disorders and treatment planning in the field of
medicine. Edges are important features in an image to separate region.
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a) Gradient
Gradient magnitude is often used to preprocess for Segmentation. Gradient helps
to identify the initial mask, flow of direction and the shape of the tumor. The level set is
dynamic. It will be moved or spread highly. It is one of the tools to identify the extract
seed point [16]. We modify the gradient image by imposing both internal and external.
The gradient shows the x and y directions.
b) Signed Distance Found (SDF)
SDF stands for Signed Distance Found. It is based on the mask to identify
distance. The Vector image may identify the length. It will be in 2- Dimensional [3].
Then it is converted into one dimensional. Legendre is used in this process.
c) Curvature
The initial shape of a tumor may be a circle, square or spherical. The curvature
will be used to identify the directions [18]. When using a gradient Two-Dimensional
image will be displayed.
d) Normalization
When the row and column are combined the Orthonormal function 2D Legendre
will be displayed. The row is read first and it is followed the column. The
Normalization shows the X and Y direction. It specifies X and Y normalization.
(Gradient rules apply).The gradient value may be divergence. Normalization shows the
intensity [2]. A clear image has been obtained by removing the blurred area of the
image.
e) Compute Divergence (Identify the Internal and External Tumor)
The level set is moved from one direction to another. It helps to identify
the starting point, X direction, Y direction and current state of the tumor [18]. Because it
is a high-grade tumor as it spread all over the brain tissues.
Algorithm
Input: MRI Images
Output: Segmented Output
1. Read the MRI Image.
2. Estimation of the Region Of Interest (ROI) using the Spatial Fuzzy
Clustering Method (SFCM) clustering results of input images.
3. Estimating the Region growth based on affinity.
4. Extract seed points using location and information of every region.
5. The similarity Method is used to determine whether the unlabeled pixels are
added to the detection region.
6. Merging the Region based on the Minimum Description Length (MDL)
criteria are used to extract non-tumor regions from the detection regions in
RGBA.
7. Apply Region Growth based Affinity (RGBA).
8. To reduce the noise and smoothing the tumor image.
9. To detect the initial counter of the tumor based on the DRLSE Method.
10. Prepare signed distance map of initial counter.
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11. Requires specification of a few parameters, like the number of color band
and maximal polynomial degree for two-dimensional images.
12. Estimate the gradient descent flow for energy minimization.
13. To define X and Y Gradient and to identify X and Y direction
14. To apply the normalized gradient used to normalize the tumor image.
15. Determines convergence criteria.
16. Finally, the region is selected and segmented.
6. Result and Discussion
a) Database:
The novel Legendre Polynomial method takes medical MR image as input and
effective segmenting tumor image to make an enhanced version. The medical database
namely BRATS 2015 is a large growing database. The software testing for the proposed
Legendre polynomial method is performed by 220 cases detected as true positives are
available to make use in the following work, including 182 HGT cases and 38 LGT
cases. Exactly it covers 158 cases detected 128 in HGT and 30 in LGT. Besides 58 cases
of the remaining can be applied to evaluate our ULPA method.
b) Evaluation:
The Evaluation of the segmentations considered five metrics
Sensitivity
Dice
Specificity
positive predictive value (PPV)
Euclidean distance (ED).
Table 1. The Evaluation of five metrics
Metrics Formulas
Sensitivity
Dice
Specificity
positive predictive value
(PPV) Euclidean distance ED = (q1-p1)+(q2-p2)
The Novel Legendre algorithm performance can be evaluated in terms of
sensitivity, Dice, Specificity, PPV, and Euclidean distance ED. The Segmentation of brain
tumor defining the terms Tpos, Tneg, Fpos and Fneg from the outcome parameters and
result for the calculation of the five metrics are shown in table,
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Table 2. Outcome Parameters
Outcome Parameters Ground truth
Positive Negative
Positive Detection Tpos Fpos
Negative Detection Fneg Tneg
Here Tpos is the number of Truth positives which is used to indicate the total
number of abnormal cases correctly classified. Tneg is the number of true negatives,
which is used to indicate normal cases correctly classified. Fpos is the number of false
positive and it is used to indicate wrongly detected or classified abnormal cases, when
they are actually normal cases and Fneg is the number of false negatives, it is used to
indicate wrongly classified or detected normal cases, when they are actually abnormal
cases all of the outcome parameters are calculated using the total number of samples
examined for the detection of the tumor.
Table 3. Input Case values for Truth Detection
Cases HGT LGT
220 182 32
158 128 30
Table 3 shows the tumor cases which is split it into high grade and low grade
tumor. In the first case 220 cases are split into 180 HGT and 32 LGT then 158 case set is
divided into 128 HGT and 30 LGT.
Table 4. Analysis of HGT and LGT using Truth positive
TRUTH POSITIVE (Tpos)
Grades Positive Detection Negative Detection Total
HGT 182 13 195
LGT 38 8 46
Table 4 shows the analysis on HGT and LGT using Truth Positive (Tpos)
analysis. A total of 241 cases are examined and detected as HGT and LGT. Of which
195 cases are HGT and 46 cases are LGT. HGT is split into positive and negative
detection which is accounted for 182 and 13 respectively. LGT is also split into the
positive detection with 38 cases and negative detection with 8 cases.
Figure 2. Comparison of tumor in HGT and LGT with Truth Positive (Tpos)
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Figure 2 shows the comparison of tumor in HGT and LGT with Truth Positive
(Tpos). It highlights the variety in positive and negative detection.
Table 5 . Analysis of HGT and LGT using Truth Negative
TRUTH NEGATIVE (Tneg)
Grades Positive Detection Negative Detection Total
HGT 16 18 34
LGT 6 6 12
Table 5 shows the analysis on HGT and LGT using Truth Negative (Tneg). A
total of 46 cases are examined and detected as HGT and LGT. Of which 34 cases are
HGT and 12cases are LGT. HGT is split into positive and negative detection which is
accounted for 16 and 18 respectively. LGT is also split into the positive detection with 6
cases and negative detection with 6 cases.
Figure 3. Comparison of tumor in HGT and LGT with Truth Negative (Tneg)
Fig 3 shows the comparison of tumor in HGT and LGT with Truth Negative
(Tneg). It focuses on the variety of positive and negative detection.
Table 6 . Total Analysis of Positive and Negative detection in truth Positive
TOTAL DETECTION
Grades Positive Detection Negative Detection Total
HGT 198 31 229
LGT 44 14 58
Table 6 displays the total analysis of positive and negative detection. Of the 287
total detection cases , 229 cases are HGT and 58 cases are LGT. High concentration is
found in HGT with 229 cases where 198 cases are detected positive and 31 cases are
detected negative. Of the remaining 58 LGT cases, 44 cases are detected positive and 14
cases are detected negative.
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Figure. 4 Comparision of total detection in HGT and LGT with Tpos and Tneg
Figure 4 compares the variation of total detection cases between positive and negative
detection
Table . 7 Input Case values for false detection
Cases HGT LGT
22 16 6
21 13 8
Table 7 shows the tumor cases which is split it into HGT and LGT. In the first 22 cases
are split into 16 HGT and 6 LGT, then 21 cases are split into 13 HGT and 8 LGT.
Table. 8 Analysis of HGT and LGT using False Positive
FALSE POSITIVE (Fpos)
Grades Positive Detection Negative Detection Total
HGT 12 4 16
LGT 5 1 6
Table 8 shows the analysis on HGT and LGT using False Positive (Fpos)
analysis. A total of 22 cases are examined and detected as HGT and LGT. Of which 16
cases are HGT and 6 cases are LGT. HGT is split into positive and negative detection
which is accounted for 12 and 4 respectively. LGT is also split into the positive detection
with 5 cases and negative detection with 1 case.
Figure. 5 Comparison of tumor in HGT and LGT with False Positive (Fpos)
Figure 5 shows the comparison of tumor in HGT and LGT with False Positive (Fpos). It
highlights the variation of positive and negative detection.
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Table. 9 Analysis of HGT and LGT using False Negative
FALSE NEGATIVE (Fneg)
Grades Positive Detection Negative Detection Total
HGT 9 4 13
LGT 6 2 8
Table 9 shows the analysis on HGT and LGT using False Negative (Fneg)
analysis. A total of 21 cases are examined and detected as HGT and LGT. Of which 13
cases are HGT and 8 cases are LGT. HGT are split into positive and negative detection
which is accounted for 9 and 4 respectively. LGT are also split into the positive detection
with 6 cases and negative detection with 2 cases.
Figure. 6 Comparison of tumor in HGT and LGT with False Negative (Fneg)
Figure 6 shows the comparison of tumor in HGT and LGT with False Negative It
highlights the variety in positive and negative detection.
Table. 10 Total Analysis of Positive and Negative detection in False Negative
TOTAL DETECTION
Grades Positive Detection Negative Detection Total
HGT 21 8 29
LGT 11 3 14
Table 10 displays the total analysis of positive and negative detection. Of the 43 total
detection cases , 29 cases are HGT and 14 cases are LGT. High concentration is found in
HGT with 29 cases where 21 cases are detected positive and 8 cases are detected
negative. Of the remaining 14 LGT cases, 11 cases are detected positive and 3 cases are
detected negative.
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Figure. 7 Comparison of total detection in HGT and LGT with Tpos and Tneg
Figure 7 compares the variation of total detection cases between positive and negative
detection
Table. 11 Existing system for Dice, PPV, Sensitivity and ED
EXISTING SYSTEM
Grades and
combination
Dice
PPV
Sensitivity
ED
HGT 0.907 0.913 0.895 2.642
LGT 0.873 0.880 0.583 2.80
Combined 1.78 0.583 1.478 5.442
Table 11 shows the Existing system for dice, PPV, Sensitivity, and ED. From an
HGT, the value for Dice (0.907), PPV (0.913), Sensitivity (0.895) and Euclidean
Distance (2.642). Similarly, from an LGT, the value for Dice, PPV, Sensitivity and ED
are 0.873, 0.880, 0.583 and 2.80 respectively. The combined value of HGT and LGT for
Dice, PPV, Sensitivity, and ED are 1.78, 0.583, 1.478 and 5.442 respectively.
Figure. 8 Comparison of Existing system for Dice, PPV, Sensitivity and ED
Figure 8 shows the comparison of Existing system for Dice, PPV, Sensitivity and ED.
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Table 12. Proposed system for Dice, PPV, Sensitivity and ED
PROPOSED SYSTEM
Grades and
combination
Dice PPV Sensitivity ED
HGT 0.910 0.909 0.9128 2.980
LGT 0.880 0.877 0.8826 1.113
Combined 1.79 1.786 1.794 4.093
Table 12 shows the proposed system for dice, PPV, Sensitivity and ED
are 0.910, 0,909, 0.9128 and 2.980. Similarly from an LGT the value for Dice (0.880),
PPV (0.877), Sensitivity (0.8826) and Euclidean Distance (1.113) respectively. The
combined value of HGT and LGT for DICE, PPV, Sensitivity and ED are 1.791, 1.786,
1.794 and 4.093 respectively. Comparative study shows the proposed system is better
than the Existing system .
Figure. 9 Comparison of Proposed system for Dice, PPV, Sensitivity and ED
Experimental results
(a)
(b)
(c)
Figure. 9 (a) Input Image (b) Gray Matter (c) White Matter
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Region growing
(a)
(b)
(c)
(d)
(e)
(f)
(g)
(h)
Figure. 10 Experimental result for the spatial fuzzy C means clustering method to evaluate Region
Of Interest (ROI) (a) Input MRI image (b)-(g) is the levels of ROI (h) Segmented tumor region.
The Spatial Fuzzy C Means clustering can be evaluated region growing which is overcome the
method Unified Legendre Polynomial. The result of the Unified algorithm is
(a)
(b)
(c)
(d)
(e)
(f)
(g)
(h)
Figure. 11 Experimental result for the Legendre Polynomial method to evaluate Segmentation (a)
Input MRI image (b)-(g) is the levels of Legendre (h) segmented tumor region.
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7. Conclusion
In this paper we propose a Unified Legendre Polynomial algorithm for tumor
segmentation named ULPA. We use SFCM clustering to justify the medical image
segmentation and to find out ROI depending on its location. The seed point is expanded
to grow independently based on the affinity method. The spatial distance is between the
neighboring pixels and the region growing. The method is a way forward to refine the
result of region growing, gradient and normalization. The Experimental results
demonstrate the effectiveness of our ULPA in MRI images.
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