International Journal of Application or Innovation in Engineering & Management (IJAIEM)
Web Site: www.ijaiem.org Email: editor@ijaiem.org, editorijaiem@gmail.com
Volume 2, Issue 8, August 2013
ISSN 2319 - 4847
Detection of Abnormal Tissue Growth in MR
Imaging using Biogeography Based
Optimization
Gaganpreet Kaur1 ,Dr. Dheerendra Singh2 ,Harpreet Kaur3
1
Assistant Professor, Sri Guru Granth Sahib World University, Fatehgarh Sahib
2
Professor & Head, SUSCET, Tangori
3
Student Masters Of Technology, Sri Guru Granth Sahib World University, Fatehgarh Sahib
Abstract
The Medical image segmentation is an essential tool in visual surveying and analyzing magnetic resonance images and solving
the problems in medical imaging. Many algorithm have been elaborated for medical image segmentation. In this paper we have
used a biogeography based optimization. Biogeography is the study of the distribution of animal and plants over time. We have
applied a BBO on the MRI to detect the abnormal tissue growth. We have detect cancer in this work, for cancer detection images
that are used in our work is Aorta, Coronal, Transverse. This technique has main characteristic it does not involve reproduction
of the generation. The total time taken to detect abnormal tissue growth in our work is from 3.89 to 4.08 in seconds.
Keywords: Magnetic resonance imaging (MRI), image segmentation, Biogeography Based Optimization.
1. INTRODUCTION
The Segmentation involves portioning an image into set of homogenous and meaningful regions, such that the pixel in
each partitioned region possesses an identical set of properties. Segmentation can be used for object recognition, image
compression, image editing [1]. The quality of the segmentation depends upon the digital image. Segmentation is the
process of dividing an image into distinct regions with property that each region is characterized by unique feature such
as intensity, color etc .Further it refers to the process of dividing a digital image into multiple segments such as a sets of
pixels, also known as super pixels [2]. The main objective of segmentation is to transform the representation of an image
into meaningful image that is more understandable and easier to analyze [2]. Segmentation is basically a collection of
methods that allowing Image segmentation is a technique which uniquely identifies pixels or close parts of image which
share certain visual characteristics.
Medical image is the technique and process used to create images of the human body. In medical image processing
segmentation has been used for various purposes like: surgical planning, heart image extraction from cardiac cine
angiograms, detection of tumors, measuring tumor volume, detection of micro classification on mammograms, detection
of the coronary border in angiograms, its response to therapy, automated classification of blood cells, etc.
Image segmentation may be defined as a process of assigning pixels to homogenous and disjoint regions which form a
partition of the image that share certain visual characteristics[2]. Image segmentation is used to locate and find objects
and boundaries (lines, curves, etc.) in images[2]. It basically aims at dividing an image into subparts based on certain
feature. Features could be based on certain boundaries, contour, color, intensity or texture pattern, geometric shape or any
other pattern [2]. It provides an easier way to analyze and represent an image. Practical application of image
segmentation range from filtering of noisy images, medical applications (Locate tumors and other pathologies, Measure
tissue volumes, Computer guided surgery, Diagnosis, Treatment planning, study of anatomical structure), Locate objects
in satellite images (roads, forests, etc.), Face Recognition, Finger print Recognition, etc.
1.1 Requirements that good image segmentation meets
1. Every pixel in the image belongs to a region
2. A region is connected: any two pixels in a particular region can be connected by a line that doesn’t leave the region
3. Each region is homogeneous with respect to a chosen characteristic. The characteristic could be syntactic (for example,
colour, intensity or texture) or based on semantic interpretation
4. Adjacent regions can’t be merged into a single homogeneous region
5. No regions overlap
2. MAGNETIC RESONANCE IMAGING
Medical image is the technique and process used to create images of the human body. In medical image processing
segmentation has been used for various purposes like: surgical planning, heart image extraction from cardiac cine
angiograms, detection of tumors, measuring tumor volume, detection of micro classification on mammograms, detection
of the coronary border in angiograms, its response to therapy, automated classification of blood cells, etc. The advances in
image technology, diagnostic imaging has become an indispensable tool in medicine today. X-ray angiography (XRA),
magnetic resonance angiography(MRA), magnetic resonance imaging(MRI), computed tomography(CT), and other
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International Journal of Application or Innovation in Engineering & Management (IJAIEM)
Web Site: www.ijaiem.org Email: editor@ijaiem.org, editorijaiem@gmail.com
Volume 2, Issue 8, August 2013
ISSN 2319 - 4847
imaging modalities are heavily used in clinical practice[3]. Such images provide complementary information about the
patient[3]. We have applied a Biogeography Based Optimization technique on MR images.
The magnetic resonance imaging of the body to performed to get structural detail of the brain, liver, chest, abdomen and
pelvis which help in diagnosis or monitoring the treatment. The different types of Magnetic resonance imaging are:
(1) Brain Magnetic resonance imaging: MR is generally more sensitive in detecting brain abnormalities during the
early stages of disease, and is excellent in early detection of cases of cerebral infarction, brain tumors, or infections. MR
is particularly useful in detecting white matter disease.
(2) MR Liver imaging: MR provides outstanding intrinsic soft contrast that can enhance subtle differences between
normal and pathologic tissues and tissues of different histologic subtypes.
(3) Chest Magnetic resonance imaging: The chest MRI is used to detect following disorders: thymus tumor, lung
masses, esophageal mass, other masses (aggregations of cells) or tumors of the chest, abnormal lymph nodes, swollen
glands and enlarged lymph nodes in any location of the chest, staging of tumors including invasion of blood vessels,
alveolar bullae (COPD), bronchial abnormalities, bronchiectasis, cystic lung lesions, pleural abnormalities, including
thickening or pleural effusion, abnormal pulmonary vessels, aortic stenosis, etc.
(4) Abdominal Magnetic resonance imaging: The Abdominal MRI may reveal many medical conditions, including:
abscess, acute tubular necrosis, adrenal masses, cancer, enlarged spleen or liver, gallbladder or bile duct problems,
gallstones, bile duct stones, hemangiomas, kidney infection, kidney damage, lymphadenopathy, obstructed venacava,
pancreatic cancer, tumor of the gallbladder, abdominal aortic aneurysm, ovarian cancer, etc.
2.1 Advantages and Disadvantages of MRI:
Advantages are:
1. It has an excellent capability for soft tissue imaging
2. It has very high resolution of the order of 1mm cubic voxels
3. It has high signal to noise ratio
4. Multi channel images with variable contrast can be achieved by using different pulse sequences; this can be further
utilized for segmenting and classifying different structures.
Disadvantages are:
1. MR acquisition takes considerably longer time as compared to CT and
2. In case of MR it is more difficult to obtain uniform image quality.
3. OPTIMIZATION M ETHODS
(a) Ant colony optimization: Ant colony optimization based on the pheromone deposition of ants. ACO is a Meta
heuristic that can be used to refine method applicable to a wide set of problems with few modifications. The ant based
clustering algorithms are based upon the brood sorting behavior of ants.
(b) Particle Swarm Optimization: Particle swarm optimization is based on the swarming behavior of birds, fish, etc. In
this each particle learns from its neighbors and adjusts itself accordingly. PSO has no crossover and mutation operator. In
PSO the solutions are known as particle which fly through the problem space.The particles are also called swarm. In each
iteration, the particles are updated using two values one is pbest and other is gbest.
(c) Genetic algorithm: The genetic algorithm based on the natural selection in biological evolution. The GA reproduce
children by crossover, namely their solution disappear at the end of each generation.
(d) Evolutionary strategy: The evolutionary strategy based on multiple parents contributing to offspring. The most fit
offspring become the next generation of parents. The ES reproduce children by crossover, their solution disappear at the
end of each generation.
(e) Differential Evolution: The DE is based on the simple difference-based modification of solutions. The DE its solution
directly, but changes in a particular DE solution are based on difference between other DE solutions.
(f) Population Based Incremental Learning: PBIL is an optimization algorithm and an estimation of distribution
algorithm, This is a type of genetic algorithm where the genotype of an entire population is evolved rather than individual
member.
(g) Biogeography Based Optimization: BBO is population based optimization algorithm it does not involve
reproduction or the generation.
4.BIOGEOGRAPHY BASED OPTIMIZATION
Biogeography Based Optimization is a population based evolutionary algorithm(EA) motivated by migration mechanism
ecosystem. It is based on the mathematics of biogeography. In BBO, problem solutions are represented as islands, and the
sharing of features between solution is represented as emigration and immigration.(BBO satellite). Biogeography Based
optimization was first presented in December 2008 by Dan Simon[4].
The mathematical model of biogeography describes how species migrate from one island to another, and how species
arise. The geographical areas that are well suited for the residence of biological species are said to have high suitability
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Volume 2, Issue 8, August 2013
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index(HIS) and the variables that characterize habitability are called suitability index variable(SIV).(medical image
quantization). SIVs can be considered the independent variables of the habitat, and HSI can be considered the dependent
variable[4].
Habitats with a high HSI tend to have a large number of species, while those with a low HSI have a small number of
species. Habitats with a high HSI have many species that emigrate to nearby habitats, simply by virtue of the large
number of species that they host. Habitats with a high HSI have a low species immigration rate because they are already
nearly saturated with species. Therefore, high HSI habitats are more static in their species distribution than low HSI
habitats. By the same token, high HSI habitats have a high emigration rate; the large number of species on high HSI
islands have many opportunities to emigrate to neighboring habitats. Habitats with a low HSI have a high species
immigration rate because of their sparse populations. This immigration of new species to low HSI habitats may raise the
HSI of the habitat, because the suitability of a habitat is proportional to its biological diversity. However if a habitat’s HSI
remains low, then the species that reside there will tend to go extinct, which will further open the way for additional
immigration. Due to this, low HSI habitats are more dynamic in their species distribution than high HSI habitats. A good
solution is analogous to an island with a high HSI, and a poor solution represents an island with a low HSI. High HSI
solutions resist change more than low HSI solutions. By the same token, high HSI solutions tend to share their features
with low HSI solutions. Poor solutions accept a lot of new features from good solutions. This addition of new features to
low HIS solutions may raise the quality of those solutions. We call this new approach to problem solving biogeographybased optimization (BBO)[4].
4.1 BBO basically depends upon the following terms:
Migration: The BBO migration strategy in which we decide whether to migrate from one region to other or not. The
migration rates of each solution are used to probabilistically share features between solution. BBO migration is used to
change existing habitat. Migration in BBO is adaptive process. It is used to modify existing islands. Migration stages
arises when LSI occurs. When species are less compatible with their habitat they migrate.
Mutation: Mutation is a probabilistic operator the randomly modifies a solution feature. The purpose of mutation is to
increase habitat among the population. For low value solution, mutation gives them a chance of enchancing the quality of
the solution, and for high fitness value solution, mutation try to improve the
Figure 1. Species Model
value as compared to the previous value. The implemented mutation mechanism is problem dependent. In which a new
regions are created by hybrid the other the region.
Certain Features of Biogeography Based optimization:1. BBO has a way of sharing information between solutions.
2. BBO solutions survive forever (although their characteristics change as the optimization process progresses)
3. BBO solutions do not necessarily have any built-in tendency to cluster.
4. It does not require a priori knowledge of the number of partitions in the image.
5. PROPOSED BBO BASED SCHEME FOR IMAGE SEGMENTATION
BBO can be better to detect abnormal growth of tissues as compare to other optimization method. BBO is a population
based optimization algorithm it does not involve reproduction or the generations. BBO is uniquely a biology technique
that we used for image segmentation. BBO is very fast algorithm. BBO is an application of biogeography to EAs. It is
modelled after the immigration and emigration of species between habitats to achieve information sharing. For each
generation, BBO uses the fitness of each solution to determine its immigration and emigration rate. BBO has a way of
sharing information between solutions. Its solution survives forever. It does not require a priori knowledge of the number
of partitions in the image. It yields regions, more homogeneous than the existing method even in presence of noise. So,
BBO based segmentation is used in this work with medical images to help in object detection.
Proposed algorithm of Biogeography Based Optimization
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Step 1: Take MRI image
Step 2: Convert it into grayscale image
Step 3: Initialize the BBO parameters
Step 4: Initialize a random set of habitat, each habitat corresponding to a potential solution
Step 5: Define scale, Smax, immigration rate(λ) and emigration rate(µ), HSI
Step 6: Pi(n+1)= Pi(n)=r1(PHSIi-Xi)+(r2(Hhsi)-Xi) (use for habitat(image in our case)outward, if habitat HIS is high then
the pixels are migrate and if low HIS then pixels are immigrate)
Step 7: Xi(n+1)=Xi(n)+Pi(n+1)
Step 8: After 3, According to random migration & immigration value new matrix is obtained.
Step 9: To detect ROI, segment the image on basis of immigration and migrate value
Step 10: After 4, segment image and must compare the ROI part of the image
Step 11: With different scale and different value new circle or Hough is obtained
Step 12: end
Based on step (6) (7) the pixels can migrate to habitat having high HIS, otherwise the pixel values immigrate.
The Flowchart of the proposed algorithm is shown in Figure 2. By following this algorithm we reached the target
efficiently.
Figure 2: Flow chart of proposed scheme
The following criterion is follow for image segmentation using Biogeography Based Optimization:
The proposed algorithm is implemented with Matlab 7.5.0. In this work first take MRI image. Edge detected using canny
edge method. The canny method finds edges by looking for local maxima of the gradient of image. The method uses two
thresholds, to detect strong and weak edges, and includes the weak edges in the output only if they are connected to strong
edges. This method is therefore less likely than the others to be fooled by noise, and more likely to detect true weak edges.
Hough circle used to detect circle, The Hough Circle used to detect features of the image. Function of Hough Circle used
in this work like this [y0detect, x0detect, Accumulator]= Hough circle(Imbinary, r, thresh, region)
Y0detect= row coordinate of detected circle
X0detect= column coordinate of detected circle
Accumulator= the accumulator array in Hough space
Imbinary= binary image, r= radius, thresh= threshold value, region= region to search for circle centers with in (x, y, w,
h).
In this work we have take values of these parameters is r=45, thresh= 4.
The Region of Interest used to filter out the portion of the image. Apply Biogeography Based Optimization algorithm and
initialize all parameters of BBO. Segment the object that we want to detect in the MR Images.
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6. IMPLEMENTATION
In this work we have implemented Biogeography Based Optimization to segment the MR imaging. We have detect a
cancer in medical images. Different parameters are implement of BBO, the population size= 50, Generation limit=100,
Mutation probability=0( for small number of population). We have checked the performance of different function (
Rastrigin, Schwefel 2.26, Sphere, Step, Griewank ) at different population size, for mean min cost and mean generation
count at different samples by BBO algorithm mean improvement segment generation and mean improvement amounts
checked it gives better results. So we have applied the proposed BBO algorithm at MR imaging for segmentation. The
implementation of Biogeography Based Optimization done in MATLAB 7.5.0.
Figure 3: Implementation of Biogeography Based Optimization
7. RESULTS
The proposed algorithm is used for segmentation of MR images using Biogeography Based Optimization .Different
images are used in the following results to detect cancer in the MR images, the images are Transverse, Aorta, Coronal.
The Transverse images are muscles images. The aorta images are that supplies all of the systemic circulation, which
means that the body except for the respiratory zone of the lung gets it blood from the aorta. It supplies the blood to neck,
heart, arm. The coronal images are those that are divided the body of into vertical and dorsal(belly and back) section. The
Table 1 shows the time for segmentation using proposed BBO algorithm, the time taken by this algorithm very less to
detect cancer at proper position in MR imaging.
Table 1: TIME TAKEN BY BBO BASED MR IMAGING SEGMENTATION
IMAGES Name
TIME IN SEC
Transverse .jpg
4.04
Coronal .jpg
4.08
Aorta .jpg
3.89
Figure 4. (a)Transverse original image b) Edge detection using canny method c) Hough circle d) Segmented image e) 3D
view of segmented image
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International Journal of Application or Innovation in Engineering & Management (IJAIEM)
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Volume 2, Issue 8, August 2013
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Figure 5. a)Coronal original image b) Edge detection using canny method c) Hough circle d) Segmented image e) 3D
view of segmented image
Figure 6. a)Aorta original image b) Edge detection by canny method c) Hough circle d) Segmented image e) 3D view of
segmented image
In earlier papers, work has been done on color images but in this work we used MRI images instead of color images and
checked the abnormal tissue in correct position and detect in very less time by Biogeography Based Optimization. By this
technique easily detect the proper area of cancer. This proposed algorithm gives better results as compare to other
optimization methods. The output of the above algorithm can be seen in above figures.
7. CONCLUSION
Segmentation is a collection of methods allowing interpreting close parts of the image as objects. BBO is a biogeography
technique used for image segmentation which provide more accurate segmented images as compared to other
optimization method. In this paper, We have used new approach using BBO based segmentation technique on medical
images to improve the quality of detection of object which help to detect abnormal tissue growth. In this work the MRI
database is used for testing, the major objective of this algorithm is to detect cancer from different parts of the human
body that images shows in results. Similarly, this proposed approach can achieve better results than previous approach
that uses image segmentation techniques like, ACO, GA, DE, PSO, ES.
ACKNOWLEDGMENT
This thesis work is based on Biogeography Based optimization technique to segment the MR images by using MATLAB.
The Main objective of this thesis is to study and implement the Biogeography Based Optimization for MR images
segmentation, the images[4] [5] [6]. The basic software required are Window 7, MATLAB version 7.5.0, Image
processing toolbox. I would like to thank my guide Mrs. Gaganpreet Kaur, SRI GURU GRANTH SAHIB WORLD
UNIVERSITY, FATEHGARH SAHIB for her valuable advice and healthy criticism throughout my thesis which helped
me immensely to complete my work successfully. I would like to thanks for the authors of all those books and papers
which I have consulted during my thesis work as well as for preparing the report.
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Web Site: www.ijaiem.org Email: editor@ijaiem.org, editorijaiem@gmail.com
Volume 2, Issue 8, August 2013
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