Glioma Multiforme Brain Tumor Segmentation using ... - IRNet Explore

GLIOMA MULTIFORME BRAIN TUMOR SEGMENTATION USING 

SOFT COMPUTING TECHNIQUES WITH INTEGRATED 

RADIOLOGY STUDY MAKER 

SHREESHAYANA.R 1 , UDAYASHANAKARA.V 2 

1 M.Tech BMI Student, Dept of IT, SJC Engineering College, Mysore, India 

2 Head & Prof, Dept of IT, SJC Engineering College, Mysore, India 

Abstract— In this work, we investigate the effectiveness of Automatic segmentation along with classification in Magnetic 

Resonance (MR) Images for Glioma brain tumors of different grades.The project cites the current scenario in JSS Hospital 

Mysore where radiologists perform manual brain tumor segmentation and also find it difficult in diagnosis as there is no 

provision of patient case history which is essential for cancer diagnosis. The inter and intra observer segmentation variability 

increases significantly when segmenting higher grade tumors.The paper describes Automatic segmentation tool consisting of 

three phases. In the first phase, the MRI brain image is acquired from patient’s database, In that images, artifact and noise 

are removed. This is followed by Kmeans algorithm which is applied for soft tissue differentiation by colour classification of 

different tissues such as gray matter, white matter etc. Finally based on intensity feature extraction, Hierarchical self 

organizing map is applied for malignant region segmentation. The Hsom is the extension of the conventional self organizing 

map used to classify the image row by row. In this lowest level of weight vector, a faster computation speed is achieved by 

the HSom. Also framework for performing colour fusion of T1 (gadolinium-enhanced) and T2 weighted MRI images using 

Rbg2hsv algorithm is provided. The soft tissues appear darker in T1 weighted and Pathological regions appear brighter in T2 

weighted MRI images and fusion helps in enhanced contrast visualization and thus better diagnosis. 

A Semi Automatic tool localizing all above strategies is also provided. Here a Study maker tool ‘Sulabh’ is also 

proposed to solve the patient case history issues with provision of user interface to enter patient case history & clinical 

statistics and local storage of radiology cases with images. The experimental results are compared with those of ground truth 

methods. Comparative efficacy of true positive and false positive ratios is tested on 40datasets with 806 slices to determine 

manual and automatic performances. 

Keywords- Fusion, Glioma, HSOM, K Means, MRI, Neural Network, Radiology Case Maker, Segmentation. 

I. INTRODUCTION 

Glioma tumor, which is one of the most 

common brain diseases, has affected and devastated 

many lives. According to International Agency for 

Research on Cancer (IARC) it is 

estimated more than 126,000 people diagnosed for 

brain tumor per Year around the world with more 

than 97,000 mortality [1]. In recent years, researchers 

from different disciplines ranging from medical to 

mathematical and computer sciences have combined 

their knowledge and efforts to better understand the 

disease and to find more effective treatments. Due to 

the complex structure of different tissues such as 

white matter (WM), gray matter (GM) and 

cerebrospinal fluid (CSF) in the brain images, 

extraction of useful feature is a fundamental task. 

Brain tissue and tumor segmentation in MR images 

have been an active area of research today [1-3]. 

Neural Networks are mathematical 

analogues of biological neural systems, in the sense 

that they are made up of a parallel interconnected 

system of nodes, called neurons. Artificial Neural 

Networks. (ANNs), Genetic Algorithms (GAs) and 

Fuzzy Logic are CI non-symbolic learning 

approaches for solving problems (Mantzaris et al., 

2008). The huge mass of applications, which ANNs 

have been used with satisfactory results, has 

supported their rapid growth. Fields that ANNs were 

used are image processing (Gendy et al., 2001).In this 

paper, a new unsupervised learning Optimization 

algorithm such as HSOM is implemented to extract 

the suspicious region in the Segmentation of MRI 

Brain tumor. 

A. Radiology Study Maker 

In the first leg of the project, Study maker tool 

is designed. The logistic used is C#.net. The Study 

maker was very essential citing the current situation 

in JSS hospital where there is no digital storage of 

patient cases details in centralized database. Hence 

considering the above situation the project is 

developed under the feedbacks from the radiology 

department JSS in association with Philips innovation 

lab for overall deliverability. 

The Radiology Study maker tool will help in 

storage of both patient history and diagnosis details 

with images in a local storage system for further 

retrieval of patient visiting again .It also facilitates for 

storage of scanned MRI image, classified, segmented 

tumor images. This will facilitate the radiologist in 

International Conference on Electronics and Communication Engineering, 28 th April-2013, Bengaluru, ISBN: 978-93-83060-04-7 

121

Glioma Multiforme Brain Tumor Segmentation using Soft Computing Techniques with Integrated Radiology Study Maker 

diagnosis as all three image slices can be viewed in 

the image viewer provided. 

Fig 1: Use Case Diagram Sulabh-Study Maker 

II. MEDICAL BACKGROUND 

The brain is a soft, spongy mass of nerve cells 

and supportive tissue connected to the spinal cord. A 

brain tumor takes up space within the skull and 

interferes with normal brain activity. A tumor can 

cause damage by increasing pressure in the brain, 

shifting the brain or pushing against the skull, and 

invading and damaging nerves and healthy brain 

Tissue [4]. 

Primary brain tumors originate in the brain 

itself & usually do not spread from the brain to other 

parts of the body. A brain tumor that develops from 

glial cells is called a Glioma. About one third of all 

primary brain and other nervous system tumors form 

from glial cells. Gliomas tend to grow in the cerebral 

hemispheres, but may also occur in the brain stem, 

optic nerves, spinal cord, and Cerebellum. Glioma is 

further divided into glial and non glial tumors. Glial 

includes Astrocytoma which is classified into four 

grades Grade I- Pilocytic Astrocytoma, Grade II- 

Low-Grade Astrocytoma, Grade III-Anaplastic 

Astrocytoma, Grade IV- Glioblastoma (or GBM) is 

the most invasive type of glial tumor which Grows 

rapidly and commonly spreads to nearby tissue. Non 

glial Tumors include CNS Lymphoma, Meningioma, 

Schwannoma etc. 

Fig 2: Radiology Study Maker Sulabh Main Window 

Fig 4. a) Axial T2-weighted image of low-grade glioma in the 

right hemisphere, showing the lesion as a bright area of highsignal 

intensity. 

b) The same glioma on a T1- weighted image with contrast 

enhancement 

III. RELATED WORKS 

Fig 3: Radiology Study Maker Sulabh Multi Image viewer 

option with scanned and classified slices. Provision to enter 

patient clinical statistics, diagnosis, history, Discussion 

interface with teaching window provision. 

B. Glioma Brain tumor Segmentation Tool 

In the second leg of the project, a case study 

tool is provided where radiologist can classify and 

delineate the brain tumor region. The image can be 

just dragged and dropped into the study maker tool 

where doctor can visualize both normal and 

pathological images 

Automatic detection and segmentation of brain 

tumor from brain MR images offer a mechanism for 

overcoming the tedium involved in the manual 

segmentation of large datasets. But automated 

systems have significant problems to achieve these 

objectives. The major problems are pixel intensities 

violate the independent and identically distributed 

assumption within and between images due to the 

nature of brain MR images, and presence of a 

significant amount of artifacts and intensity in 

homogeneity in MR images [5, 6]. 

Many techniques have been proposed to automate 

the brain tumor detection and segmentation in recent 

Years. The proposed methods can be broadly 


122


classified into two, intelligent based and nonintelligent 

based. The notable intelligent based 

systems are artificial neural network, fuzzy c-means, 

support vector machine and hybrid methods. On the 

other hand, most notable non-intelligent methods 

include thresholding and region growing. 

A survey on available thresholding techniques is 

provided on [7].Most recently, Anam Mustaqeem, Ali 

Javed and Tehseen Fathima in [2012] proposed 

thresholding based tumor detection and segmentation 

methods which integrated with watershed and 

histogram analysis . But, these papers did not specify 

the data used for testing and validating their methods. 

Region growing is not often used alone because it is 

not sufficient to segment brain structures accurately 

and robustly. In Kai Xie, Jie Yang, Z.G. Zhang et.al 

[2005] region growing is integrated with boundary 

information by a level set technique. 

A through treatment of neural networks can be 

found in [8]. Neural networks due to their ability of 

learning and generalization have attracted many 

researchers in medical image segmentation and for 

other image processing techniques by M. Egmont- 

Petersen, D. de Ridder and H. Handel’s et.al [2002]. 

There are two main general types of learning in 

machine learning algorithms: supervised learning, 

unsupervised learning. In supervised Learning 

algorithms, such as Multi-Layer Perceptrons (MLP or 

NN), K-Nearest Neighbors (KNN), Support Vector 

Machine (SVM), Decision Tree (DT) to mention a 

few, the learning algorithm is given a labelled set of 

training data or examples. These labelled examples 

are the training set that the program tries to learn 

about or learn how to map the input data to the 

desired output. In unsupervised learning, a set of 

examples are given, but no labels are provided. 

Instead it is up to the learner to find the pattern or 

discover the groups. Unsupervised learning 

algorithms includes: Self-Organizing Maps (SOM), 

hierarchical clustering, K-means and fuzzy c-means 

clustering algorithms. 

Logeswari and Karnan in [9] used hierarchical 

self organizing map (HSOM), which is the extension 

of conventional SOM, to detect and visualize a brain 

tumor. Algorri and Flores-Mangas have used fuzzy 

parameters to segment normal brain tissue. [10].Dang 

Pelleg and Andrew Moore have proposed a new 

Accelerated K-Means algorithm. [11] 

IV AUTOMATIC SEGMENTATION METHOD 

Here we propose accelerated Kmeans algorithm 

which is adopted for Classification of soft tissues and 

faster Hierarchical self organizing map algorithm is 

developed for tumor segmentation. 

A. Image Acquisition 

Is through 1.5 Tesla Magnetom Siemens Magnetic 

Resonance scanner which offers the possibility to 

acquire256*256*58(0.86 mm, 0.86 mm, 2.5 mm) T1 

weighted images with the fast spin echo protocol (TR 

= 400, TE =16 ms, FOV = 220*220 mm) in 3 min and 

40 s. 

B. Image pre-process, Classification & segmentation 

• Noise removal is done through median filtering 

to preserve edges and suppress surgical 

instruments, hand movement, radio frequency 

noise etc 

• Intensity feature is extracted using mean, 

Std.deviation. K means algorithm classifies 

and provides tumor clusters and tissue 

differentiation. 

• Hsom segments region of interest based on 

winning neuron index of the weighted vector. 

C. Overview of proposed work 

1. Hsom Method Overview 

A Hierarchical self-organizing map consists of 

components called nodes or neurons. Associated with 

each node is a weight vector of the same dimension as 

the input data vectors and a position in the map space. 

The Hsom describes a mapping from a higher 

dimensional input space to a lower dimensional map 

space. The procedure for placing a vector from data 

space onto the map is to find the node with the closest 

weight vector to the vector taken from data space and 

to assign the map coordinates of this node to our 

vector. Euclidean distance to all weight vectors is 

computed. The neuron with weight vector most 

similar to the input is called the best matching unit. 

The update formula for a neuron with weight vector: 

Wv(i) is Wv(i + 1) = Wi(i+1) = wi(i) + hci(I)*[(x(i) - 

w(i))] (1) 

Here hci is neighbourhood function to calculate it 

h (i) = h (rc-r1)*a (i)*alpha (2) 

Here rc-r1=current neuron-next current neuron 

a (i) = sigma 0 * exp (-i/nsm) (3) 

2. K means Method overview 

The algorithm which follows for the k-means 

classification is given below: 

The Tumor cluster centres are obtained by 

minimising the objective function 

Where there are k clusters Si, i= 1, 2,…..k and μi 

is the centroid or mean point of all the points xi Si 

1. Initialise the centroids with k random values. 


123


2. Repeat the following steps until the Tumor 

cluster labels of the image does not change anymore. 

3. For each data point, we calculate the Euclidean 

distance from the data point to the mean of each 

cluster. 

C (i) = arg min ||x (i)-μj|| ² 

If the data point is not closest to its own tumor 

cluster, it will have to be shifted into the closest 

cluster. If the data point is already closest to its own 

cluster, we will not shift it. 

4. Compute the new centroid for each of the 

Tumor clusters. 

μi = Σ {c(i) = j}x(i) / Σ {c(i) = j} 

Where k is a parameter of the algorithm (the 

number of clusters to be found), i iterates over the all 

the intensities, j iterates over all the centroids and μi 

are the centroid intensities. 

The block diagram involving the strategies employed 

is shown as follows. 

Fig 6: Main Graphical user Interface of classification and 

segmentation tool 

Fig 7: Auto segmentation tool Using Hsom and K means 

Algorithm. 

V .SEMI AUTOMATIC METHOD 

Implementation of semi automated novel 

approach contains various processing steps like 

resizing, image pre-processing using Gaussian low 

pass filtering, Sliding threshold window with 

malignant localization facility, and two unsupervised 

tissue classification methods using K means and Fcm 

algorithm. Kmeans algorithm steps have already been 

discussed. Fuzzy c means algorithm steps include 

• Let X=(x1, x2…xn) denotes an image with 

N pixels to be partitioned into c clusters, 

where xi represents the data. The algorithm 

is an iterative optimization that minimizes 

the cost function defined as follows: 

Fig 5.Flow Diagram of Auto segmentation strategy 

• Where µij represents the membership of 

pixel xj in the ith cluster, vi is the i th cluster 

center, and m is a constant. The parameter m 

controls the fuzziness of the resulting 

partition, and m=2 is used in this study. 


124


• Starting with an initial guess for each cluster 

centers, the FCM converges to a solution for 

vi representing the local minimum of the 

cost function. Convergence can be detected 

by comparing the changes in the 

membership function 

or the cluster center at two successive 

iteration 

Steps. 

Fig 10:.Flow Diagram of Fusion using Rgb2hsv algorithm 

VII. RESULT AND ANALYSIS 

Fig 8: Semi automated tool with various tumor localizing 

facilities 

VI. FUSION METHOD 

Fusion here adopts RGB2Hsv algorithm [12].The 

tool is provided as an additional aid to radiologist in 

diagnosis. Fusion helps in analysis of soft and 

pathological characteristics better. Soft tissues appear 

darker in T1 weighted and Pathological regions 

appear brighter in T2 weighted MRI images and 

fusion helps in enhanced contrast visualization and 

thus better diagnosis. 

The block diagram involving the strategies employed 

is shown as follows. 

The results are shown with tables. Only viewing 

at the segmented images by algorithms is not 

sufficient enough to evaluate the results. Four 

procedures- true positive ratio(TPR), false positive 

ratio(FPR), true negative ratio (TNR) and false 

negative ratio (FNR) are observed in the 

segmentation results for quality measurement 

[14].The misclassification error determines the error 

rate of classification while determining the tumor 

region. The Sensitivity and specificity determines the 

efficacy of positive prediction of malignancy on the 

subject who has been initially screened for diagnosis 

through MRI. 

TABLE 1: Technical Result estimation 

Hemingioblastoma 

Datasets 

240 slices of axial, 

sagittal and 

coronal T1 

weighted MRI 

Images 

Hemingioblastoma 

Adult Male(42 to 

59 years ) 

12 data sets with 

136 slices and 

Adult Female(49 to 

62 years) 

10 datasets with 

104 slices 

RESULT COMPARISON 

Technical Efficacy % 

parameters 

comparison with 

ground truth 

images from 

radiologist 

0.0793 

Misclassification 

error 

True positive ratio 84.590 

False positive ratio 6.1011 

True negative ratio 93.898 

False negative ratio 6.1399 

Accuracy 94.323 

Sensitivity 92.394 

Specificity 93.898 

TABLE 2: Technical Result estimation Astrocytoma 

Grade 2 and Meningioma 

Fig 9:.Flow Diagram of Fusion strategy 

Datasets 

566 slices of axial, 

sagittal and 

coronal T1 

weighted MRI 

Images 

RESULT COMPARISON 

Technical 

parameters 

comparison with 

ground truth 

images from 

Efficacy % 


125


Meningioma & 

Astrocytoma 

Adult Male(47 to 

56 years ) 

17 data sets with 

430 slices and 

Adult Female(52 

to66 years) 

14 datasets with 

136 slices 

ratio 

96 

95 

94 

93 

92 

91 

90 

radiologist 

Misclassification 

0.0672 

error 

True positive ratio 86.690 

False positive ratio 5.2019 

True negative ratio 92.867 

False negative ratio 5.1376 

Accuracy 95.423 

Sensitivity 93.394 

Specificity 94.898 

Graph Analysis 

Accuracy 

Sensitivity 

Specificity 

effectiveness of this tool from the radiologist, whom 

the project is cooperated with, is positive and this tool 

helps the radiologist in diagnosis, and treatment 

planning .The Study maker has also come out with 

positive feedbacks. 

REFERENCES 

[1]. Ferlay J, Shin HR, Bray F, Forman D, Mathers C and Parkin 

DM, GLOBOCAN 2008 v2.0, Cancer Incidence and 

Mortality Worldwide, International Agency for Research 

on Cancer, Lyon, France, 2010. 

[2]. K. M. Iftekharuddin, “On techniques in fractal analysis and 

their applications in brain,” Medical imaging systems: 

technology and applications, Analysis and Computational 

Methods, vol. 1, World Scientific Publications, ISBN 981- 

256-993-6, 2005. 

[3] H. S. Zadech and H. S. Windham, “Optimal linear 

transformation for MRI feature extraction”, IEEE Trans. 

Med. Imaging, vol. 15, pp. 749–767. 

[4] The essential guide to brain tumors-National brain tumor 

society.org 

[5]. Schmidt M. , Levner I. , Greiner R. , Murtha A. and Bistritz A, 

Segmenting Brain Tumors using Alignment-Based 

Features, IEEE 4th International Conference on Machine 

Learning and Applications, ICMLA, Dec. 2005, pp. 215-220. 

[6]. L. J. Erasmus, D. Hurter, M. Naude, H.G. Kritzinger and S 

Acho, ―A short overview of MRI artifacts, SA Journal of 

Radiology, Vol. 8 ,No. 2 , August 2004, pp. 13-17. 

VIII .FUTURE WORK 

Fig 11:.Graph of Result Estimates 

Clinical effectiveness of the proposed algorithm 

for segmentation and fusion strategy needs to be 

evaluated with physician satisfaction, and other 

clinical related variables. In future, the system should 

be improved by adapting more segmentation 

algorithm to suit the different medical image 

segmentation.Further improvements after practical 

usage of study maker will be undertaken. 

IX.CONCLUSION 

In this paper, we show an efficient algorithm for 

fully automated brain tumor segmentation in Glioma 

brain tumor images using unsupervised Hierarchical 

self organizing map technique. Method performs very 

well in extracting tumors when compared with that of 

the ground truth images from radiologist. However, 

the spreading of grade 4 tumor to different soft 

tissues and brain edges is a challenging task in 

segmentation as it may lead to misclassified brain 

tumor regions. Hence supervised method should also 

be evaluated to improve the robustness of automatic 

segmentation. The automatic segmentation methods 

and Fusion tool has been provided to aid the 

radiologist in better diagnosis of tumor. The 

[7]. Sezgin M. and Sankur B.―Survey over image thresholding 

techniques and quantitative performance evaluation, 

Journal of Electronic Imaging ,Vol. 13,No. 1 , Jan. 2004, pp. 

146–165. 

[8]. Nikola K. Kasabov, Foundations of Neural Networks, Fuzzy 

Systems, and Knowledge Engineering, Massachusetts 

Institute of Technology, 1998, pp. 167-473. 

[9]. T. Logeswari and M. Karnan, ―An improved implementation 

of brain tumor detection using segmentation based on soft 

computing, Journal of Cancer Research and Experimental 

Oncology, Vol. 2, No. 1, March 2010, pp. 006-014. 

[10]. M.E. Algorri, F. Flores-Mangas, Classification of anatomical 

structures in MR brain images using fuzzy parameters, IEEE 

Trans.Biomed. Eng. 51 (9) (2004) 1599–1608. 

[11]. Dan Pelleg, Andrew Moore-Accelerating K means 

Algorithms with geometric reasoning, Carnegie Mellon 

university special issue. 

[12]. “A Comparative Analysis of Fuzzy C-Means Clustering and 

K Means Clustering Algorithms” Mrs. Bharati R.Jipkate, Dr. 

Mrs.V.V.Gohokar /International Journal Of Computational 

Engineering Research / ISSN: 2250–3005. 

[13].Stokes, Michael, Matthew Anderson, Srinivasan 

Chandrasekar, and Ricardo Motta, "A Standard Default Color 

Space for the Internet – RGB." November 5, 1996. 

[14] B. Peng, L. Zheng and J. Yang, “Iterated Graph Cuts for 

Image Segmentation”, Asian Conference on Computer Vision 

(ACCV’09), Xi'an, China, September 23-27, 2009. 

[15]Microsoft Development network .Net tutorial website, 

www.msdn.com 


126

Glioma Multiforme Brain Tumor Segmentation using ... - IRNet Explore

Create successful ePaper yourself

Delete template?

Save as template?