AUTOMATIC DETECTION AND SEGMENTATION OF · PDF fileJ. S. Gonal and V. V. Kohir AUTOMATIC DETECTION AND SEGMENTATION OF BRAIN ... LEVEL SETS INTEGRATED WITH BOUNDING BOX Jayalaxmi S

International Journal of Computer Engineering and Applications, Volume IX, Issue VII,

June 2015

www.ijcea.com ISSN 2321-3469

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J. S. Gonal and V. V. Kohir

AUTOMATIC DETECTION AND SEGMENTATION OF BRAIN

TUMORS AND NECROSIS USING BINARY MORPHOLOGICAL

LEVEL SETS INTEGRATED WITH BOUNDING BOX Jayalaxmi S. Gonal

1, Vinayadatt V. Kohir

2

1 Department of Electronics & Communication, BLDEA’s Engineering College, Bijapur, India

2 Department of Electronics & Communication, PDA Engineering College, Gulbarga, India

ABSTRACT: In this paper, a method for automatic detection and efficient segmentation of brain tumor from magnetic resonance imagery has been introduced. We have proposed a variant bounding box technique for detecting brain tumor that utilizes approximate symmetry associated with the brain in an axial MR image. It detects the brain tumor by exactly circumscribing an axis parallel rectangular box over the entire tumor. Further for segmentation of boundary of brain tumor, we have implemented a segmentation technique using Binary Morphological Level Sets initialized at the centre of rectangular bounding box. The level set function is evolved using simple binary morphological operations. This level set method can evolve level set function in bidirectional, i.e., the interface of a level set function can either grow or shrink toward the object boundary. It is shown from experimental results that the proposed algorithm can segment the boundary of necrosis also, along with that of the tumor; whereas, the recent popular methods like, Chan-Vese, Graph-cut within bounding box can segment only the boundary of the tumor.

Keywords: MR Image, Brain Tumor, Detection, Segmentation, Bounding box algorithm, Bhattacharya Coefficient, Binary Level sets, Morphological operation

[1] INTRODUCTION

Tumors are identified as the second cause of deaths due to cancer, in children under the

age of 20, in adults of age 20 to 39 [1-5, 6]. This accelerated the research on tumors to extract

the clinically useful data viz., location, area, volume, growth rate etc. The anatomy of the brain

can be captured by scans of Magnetic Resonance Imaging (MRI) or computed tomography

(CT). MRI is preferred over CT scans, as MRI uses magnetic field and radio waves and does

not use any radiation; hence it is not harmful to human bodies [7].

In hospitals, MR images are stored in a huge database. It is difficult to retrieve the

relevant images without these images are segmented. Currently radiologists segment the tumors

by hand which is a laborious and expensive process. Also it requires an expertise in domain

knowledge. As there is a shortage of expert radiologists, hence the automation of segmentation

process has become the need of the time.

Automatic segmentation of tumors in brain MRI is considered as challenging task

because medical images are affected by different types of noise, poor contrasts, and no defined

or diffusive boundaries [8]. There are many challenges corresponding to the incorporating

Automatic Detection And Segmentation Of Brain Tumors And Necrosis Using Binary

Morphological Level Sets Integrated With Bounding Box


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domain knowledge. In the proposed work, we have developed an algorithm for automatic

detection and efficient segmentation of brain tumors from MR images.

The paper is organized as follows: the survey of the related work is carried in section 2,

the proposed technique is discussed in section 3. Experiment results are seen in section 4,

Validation in section 5 and conclusion of the paper is dealt in section 6.

[2] STATE OF THE ART

Nilanjan Ray et al. [9] developed a bounding box technique, by comparing the left-right

symmetry of the brain. Here vertical sweep and a horizontal sweep of the axial MR slice

produce score plots. The maxima and the minima from the plot using vertical sweep are

detected as top and bottom edges and from the plot using horizontal sweep are detected as left

and right edges of the bounding box. And this bounding box is overlaid on the input MR image

that circumscribes the tumor. But the bounding box with this technique fails to circumscribe the

tumor entirely and exactly.

Fuping Zhu and Jie Tian [10] have proposed an algorithm to segment an object from a

medical image. The algorithm is based on the fast marching and level set technique. They use

fast marching method to extract the rough boundaries of interested object; which are further

considered as an initialization of level set method. Then, fine tuning of the contour acquired by

fast marching method is done by the level set method.

Chunming Li. et al. [11] proposed novel image segmentation based on region based

method. The algorithm define an energy functional based on integration of local clustering

criterion and the neighborhood center, which is used in evolution of a level set formulation.

Minimization of this energy is achieved by an interleaved process of level set evolution and

estimation of the bias field.

C. Li et al. [12] proposed a region-based active contour model for the segmentation of

brain tumor. It defined a contour from data fitting energy and the image intensities on the two

sides of the contour are locally approximated. A regularization term is included in the level set

formulation, which is used to derive a curve evolution equation for energy minimization.

Regularization term intrinsically preserved the regularity of the level set function to get

accurate computation; this avoided the need of reinitialization of the evolving level set function,

which is computational expensive process.

Some researchers [13] derived a local intensity clustering property from brain tumor

and other images with intensity inhomogeneities and defined in a neighborhood of each point, a

local clustering criterion function for the intensities.

A. Hamamci et al. [14] proposed a tumor-cut algorithm which combines a level set

evolving on the tumor probability map with the tumor segmentation using cellular automata to

impose spatial smoothness. Its accuracy depends on the probability map.

Eman Abdel-Maksoud et. al. [15] presents a novel image segmentation approach

integrating K-means clustering technique with Fuzzy C-means algorithm. To provide accurate

brain tumor detection, it uses thresholding and level set segmentation stages. It combined

together the benefits of k-Means clustering which has minimal computation time and the Fuzzy

C-means which has greater accuracy. But the algorithm performs the segmentation of outer

boundary of tumor and has not considered the boundaries of constituents of tumor.


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[3] PROPOSED TECHNIQUE

In this paper, a method for automatic detection and efficient segmentation of brain

tumor from magnetic resonance imagery has been developed. We have proposed a variant

bounding box technique for detecting brain tumor that utilizes approximate symmetry

associated with the brain in an axial MR image. It detects the brain tumor by exactly

circumscribing an axis parallel rectangular box over the entire tumor. Further for segmentation

of boundary of brain tumor, we have implemented a segmentation technique using Binary

Level Sets initialized at the centre of rectangular bounding box. The level set function is

evolved using simple binary morphological operations. This level set method is can evolve

level set function in bidirectional, i.e., the interface of a level set function can either expand or

shrink toward the object boundary. It can be verified from the experimental results that the

proposed algorithm can segment the boundary of necrosis also, along with that of the tumor;

whereas, the recent popular methods like, Chan-Vese algorithm, Graph-cut methods within

bounding box can segment only the boundary of the tumor.

[3.1] BRAIN TUMOR DETECTION

We present an automatic precise detection technique that locates a “bounding box” – i.

e., an axis-parallel rectangle, exactly around the entire tumor in an MRI slice. This bounding

box can then be used to derive the useful data about the tumor, viz., position, size, growth rate

etc.

The method exploits the facts that a normal brain structure is approximately symmetric.

The left part and the right part of the brain can be symmetrically divided by an axis of

symmetry. And abnormalities viz., tumors, edema typically disturb this symmetry.

[3.1.1] CONCEPT OF DISSIMILARITY DETECTION

The algorithm first locates in the MRI slice, the axis of symmetry of the brain [9],

which divides the brain into 2 parts. The left (or the right) part is considered as the test

image I, and the right (or the left) part is considered as the reference image R. The algorithm

searches for an axis-parallel rectangular box on the left part that is very dissimilar from its

reflection about the axis of symmetry on the right part – i.e., the intensity histograms of two

rectangular boxes are most dissimilar, but the intensity histograms of the outside of the

rectangular boxes are relatively similar. We assume that one of the two rectangular boxes

will circumscribe the tumor appearing in one part of the brain. The degree of dissimilarity

between two normalized intensity histograms is quantified using Bhattacharya Coefficient.

Bhattacharya Coefficient (BC) is a measure of the correlation between two histograms.

It can be found by taking the inner product between square roots of two normalized

histograms [16]. Let A(s) and B(s) be the portions of the image domain, s, respectively. Let

BC(s) denote Bhattacharya Coefficient between them, given by equation (1):

(1)

where, denote normalized intensity histograms i.e., the probability

mass functions of image intensities, the of test image within A(s) or of reference image




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within B(s). Bhattacharya coefficient is a real number between 0 and 1. Its value is 1 for two

identical normalized histograms; whereas is 0 for the completely different histograms [16].

[3.1.2] PROCEDURE FOR BRAIN TUMOR DETECTION

We have used T1-C (T1 after injecting a contrast agent) MR imaging modalities from

the dataset as in paper [9], as they are good at identifying for tumor regions. The input MR

slice (axial view), is subdivided into 6 regions on both the sides of the axis of symmetry.

The graphical representation of procedure for Brain Tumor Detection is shown below.

Precise detection of tumor region using Bounding box is done in two phases: Crude

detection phase and Precise detection phase.

[3.1.3] GRAPHICAL REPRESENTATION OF THE PROPOSED

ALGORITHM


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Crude detection phase: deals with detection of sub-region consisting of the major

portion of the tumor. For this, Bhattacharya Coefficients (BC) of all sub-regions with

respect to their reflections in the other hemisphere of the brain is found. The sub-region

with the minimum BC is detected as a sub-region with major tumor portion. However,

the crudely detected sub-region may not cover the tumor entirely and precisely.

Precise detection phase: performs precise fixing of the positions of top, bottom, right

and left edges of sub-region detected by Crude detection phase. To precisely fix the

position of four edges of the bounding box, the position of each edge is searched from its

opposite edge in the direction of the edge to be positioned, and at each search position

the Bhattacharya Coefficient (BC) of the rectangular region are found. The values of BC

are plotted v/s search positions. The minima of the steeply rising curve in BC plot dictate

the precise position of the search edge.

[3.1.3.1] CRUDE DETECTION PHASE

The input brain MR slice is divided into 6 equal regions (vertically 3 and horizontally

2) on either side of the axis of symmetry as in Fig. 1(a) for the three cases of tumors

considered in this paper. In each input image, Bhattacharya Coefficient (BC) is found

between pair of regions: Region -1 and 1s (ITLL-ITRR), Region - 2 and 2s (ITLR-ITRL),

Region - 3 and 3s (IMLL-IMRR), Region - 4 and 4s (IMLR-IMRL), Region - 5 and 5s

(IBLL-IBRR), Region - 6 and 6s (IBLR-IBRL).

The values of BC v/s regions are plotted as shown in the last column of Table 1. The

region pair with BC minima is the pair containing tumor region. Further, this pair is chosen

for precisely fitting the edges of rectangular regions, as explained in the precise detection

phase.

To determine whether the left or the right hemisphere of the image contains the tumor,

the average intensity within the bounding boxes placed on both sides is compared. The

side which has greater mean image intensity within the bounding box is assumed to

contain the tumor [9]. The crudely segmented tumor regions are shown in Fig. 1(b) for the

three tumor cases.

Case-1




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Case-2

Case-3

MRI slice divided into sub-regions (b) Segmented tumor region

Fig. 1: Crude Detection of tumor of 3 different Cases: Region pair 4-4s (refering BC minima)

The Bhattacharya Coefficient of a region in one part of brain with respect to their

reflection in other part is tabulated in Table 1. It can be observed that the region pair

containing tumor portion has minimum Bhattacharya Coefficient.


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Table 1: Bhattacharya Coefficient between a sub-region and its reflection about the axis of symmetry

[3.1.3.2] PRECISE DETECTION PHASE

The output of Crude Detection phase, i.e. detected pair of sub-regions, containing major

tumor portion, is considered as the input in this phase. Here the top, bottom, right and left

edges of the bounding box are precisely fitted to cover entire tumor accurately. The

locations of the four edges of the bounding box are fitted by the following the procedure

below.

Fitting of Top edge: The Precise location of the top edge of crudely detected sub-region is

searched from the bottom edge of the sub-region in vertical upward direction.

Bhattacharya Coefficient at each search location is noted down. The minimal point (x = 41

pixels) of the steepest rising curve of the plot of Bhattacharya Coefficients v/s search

location in pixels is the location of top edge of the rectangular sub- region, as shown in

Fig. 2(c) – (i).The segmented region with the top edge precisely fitted is shown in the Fig.

2(b) –(i).




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Fitting of Bottom edge: The Precise location of bottom edge of the rectangular sub-

region is searched from fitted top edge of the sub-region in vertical downward direction.

Bhattacharya Coefficient at each search location is noted down. The minimal point(x = 67


location in pixels is the location of bottom edge of the rectangular sub- region, as shown in

Fig. 2(c) – (ii).The segmented region with the bottom edge precisely fitted is shown in the

Fig. 2(b) – (ii).

Fitting of Right edge: The Precise location of the right edge of rectangular sub-region is

searched from the left edge of the sub-region in horizontal right direction. Bhattacharya

Coefficient at each search location is noted down. The minimal point (x = 41 pixels) of the

steepest rising curve of the plot of Bhattacharya Coefficients v/s search location in pixels is

the location of right edge of the rectangular sub-region, as shown in Fig. 2(c) – (iii).The

segmented region with the right edge precisely fitted is shown in the Fig. 2(b) – (iii).

Fitting of Left edge: The Precise location of the left edge of rectangular sub-region is

searched from the fitted right edge of the sub-region in horizontal left direction.

Bhattacharya Coefficient at each search location is noted down. The minimal point (x = 39


location in pixels is the location of left edge of the rectangular sub- region, as shown in

Fig. 2(c) – (iv).The segmented region with the left edge precisely fitted is shown in the

Fig. 2(b) – (iv).

Crudely Segmented Fixing of top Fixing of bottom Fixing of right Fixing of left

Fig. 2: Locating of top, bottom, right and left edges of the crudely segmented sub-region: Case-3 Tumor.

(a) Crudely Segmented Tumor Region, (b) Output images after the four edges are fixed, (c) Plots of

Bhattacharya coefficient v/s search locations in pixels.

[3.1.4] OUTPUTS OF CRUDE AND PRECISE DETECTION FOR THE

THREE CASES

Fig. 3 (a) and Fig. 3 (b) show the outputs of Crude and Precise detection, respectively,

for the three cases of tumors. It is observed that the output of Crude detection is a sub-region


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that does not contain tumor entirely and exactly. Whereas, the output sub-region of Precise

detection contains tumor entirely and exactly.

Fig. 3: Detection of Tumor Regions. (a) Crude detection, (b) Precise detection.

[3.2] SEGMENTATION OF BRAIN TUMOR

As mentioned before, only a rough estimate of the abnormal region is provided by the

proposed bounding box algorithm. Further, to have precise segmentation of boundary of

brain tumors we have implemented Binary Morphological Level Sets algorithm [17].

In this algorithm, level set functions are evolved using simple binary morphological

operations. This morphological level set method can evolve level set functions in

bidirectional way, i.e., the interface of a level set function can either grow or shrink towards

the object boundary. We initialize the level set function at the center of the bounding box that

circumscribe the tumor, and allows the level set function to evolve till it sits on the boundary

of the tumor.

[3.2.1] CONCEPT OF LEVEL SETS

The idea behind active contours for image segmentation, introduced by Kass et al. [18],

is quite simple. The user draws a contour as an initial guess. This contour is evolved by

image driven forces to the boundaries of the desired objects. Here two types of forces are

considered - the internal forces, defined within the curve, performs smoothing of the model

during the deformation process, while the external forces, which are computed from the

underlying image data, used drags the model toward an object boundary within the image.




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The curve can be described by using an explicit parametric form. But when the curves

have to undergo splitting or merging, during their evolution to the desired shape the

continuity in the curve is broken. Hence, the implicit active contour approach is described.

Instead of explicitly following the moving interface itself, takes the original interface and

embeds it in higher dimensional scalar function, defined over the entire image. The use of

level set method has provided more flexibility and convenience in the implementation of

active contours.

Level set is a numerical technique for tracking moving interfaces that partition a

domain into several sub-domains. Due to their property of topological adaptively, level set

methods used for image segmentation [19-22]. The main idea behind the level set

formulation is to represent an interface Γ consisting of multiply connected region Rn in by a

Lipschitz continuous function , changing sign at the interface, i.e.,

(1)

In numerical implementations, to prevent the level set function to be too steep or flat

near the interface, the signed distance function is defined as,

(2)

where, dist(Γ, x) denotes Euclidian distance between x and Γ . Equation (2) is a

technicality to prevent instabilities in numerical implementations. And there is a one to one

correspondence between the curve and the function.

During evolution of the level set function, it will be no longer a signed distance

function. A re-distance procedure is to be adopted to keep the level set function to be a

signed distance function during its evolution. But the re-distance procedure is increases

computation complexity.

Binary Level Sets: Recently, in order to prevent the re-distance procedure, the signed

distance function is replaced by the binary level set function [17].

(3)

The binary level set function formulated by equation (3) can use its interface Γ to

partition the image domain Ω into two sub-domains, which are inside and outside the

interface, respectively. Given a gray-value image I: Ω→R+, we assume that image I can be

approximated by a binary function

(4)

where, and are two constants. The problem of two-phase image segmentation can

be modeled to minimize the energy functional [6, 7],


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(5)

subject to

= 1 (6)

where, non-negative parameter. The first term in equation (4) measures how well

the function approximates and the second term measures the length of the interface Γ. It

is difficult to directly solve the constrained minimization problem given by equation (4) and

(5).

[3.2.2] SEGMENTATION USING BINARY MORPHOLOGICAL LEVEL

SETS (BMLS) WITH BOUNDING BOX

At the center of rectangular bounding box the Morphological Level Set function is

initialized. Binary Morphological Level Sets technique is implemented by building a

narrowband region along the interface of the binary level set function. This can be done by

using morphological operations: dilation and erosion [17]. Then the sign of the level set

function value changes only in the narrowband region to form a new interface for the level

set function. The evolution of the interface is continued iteratively until it has converged.

The procedure of segmentation using Binary Morphological Level Sets is shown in Fig. 4.

Fig. 4: Segmentation procedure using Binary Morphological Level Sets

[4] EXPERIMENTAL RESULTS

After detecting the brain tumors using bounding box which is considered as rough

segmentation, we can fine tune the segmentation boundary as shown in Fig. 5.




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[4.1] COMPARISON OF TUMOR SEGMENTATION RESULTS OF BMLS-

BB WITH BMLS

Fig. 5(a) shows the bounding box locating the tumor. Fig. 5(b) shows the result of

BMLS segmentation algorithm applied on the entire image. On the other hand, Fig. 5(c)

shows the result of the Binary Level Sets algorithm coupled with the Bounding box technique.

In Fig. 5(b) spurious segmentation boundaries are created, while in Fig. 5(b) the segmentation

boundary is confined to the correct region of abnormality.

Fig. 5: Segmentation of brain tumor. (a) Bounding box technique, (b) Binary Level Sets on the entire image

(b) Binary Level Sets coupled with Bounding box technique.

[4.2] TUMOR SEGMENTATION FOR THREE CASES OF BRAIN MR

IMAGES

Fig.6 shows the segmentation of brain tumor for three cases of brain tumors. Fig. 6(a)

shows original MR Images having tumors. Fig. 6(b) shows the brain tumor segmented from

MR Images using Binary Morphological Level Sets coupled with Bounding Box (BMLS-BB)

technique. In this technique Level Set function is initialized at the center of rectangular

bounding box.

Fig.6: Segmentation of brain tumor for three cases of brain tumors. (a) Original Images, (b) Brain Tumor

Segmented Images using (BMLS-BB) algorithm.


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[4.3] COMPARISON OF TUMOR SEGMENTATION RESULTS OF BMLS-

BB ALGORITHM WITH CHAN–VESE AND GRAPH– CUT METHODS

WITHIN BOUNDING BOX

Fig.7(b) shows the output of our proposed work; a 2D contour fitted accurately on the

boundary of tumor and also necrosis within the tumor of MR Image in Fig. 7(a). The proposed

algorithm performs segmentation of tumor along with the necrosis which is an added feature

compared to the recent popular methods like, Chan-Vese algorithm, Graph-cut methods

within bounding box, as seen shown in Fig. 7(c) and 7(d).

Fig.7: Segmentation of brain tumor using different algorithms. (a) Original Images, (b) Using (BMLS-BB)

algorithm, (c) Chan-Vese algorithm and (d) Graph – cut algorithm

[5] VALIDATION

Table 2 gives the comparison of performance of segmentation algorithms. To quantify

the performance of segmentation algorithm we use Dice Coefficient [23],

Where, R is the set of the pixels in segmented tumor according to an expert radiologist

and S is set of pixels by our algorithm. The modulus sign appearing in the Dice coefficient

expression denotes cardinality (number of pixels in this case) of a set. The Dice coefficient has

a value between 0 and 1. For the ideal segmentation, its value will be 1, indicating the

segmentation by our algorithm is exactly similar to that of the radiologist (S = R). The

segmentation is considered as better, if its value is closer to unity.

Table 2: Comparison of performance of segmentation algorithms

[6] CONCLUSION

This work provides automatic detection and accurate segmentation of brain tumor from

magnetic resonance imagery (MRI) with no user intervention. The proposed algorithm

performs segmentation of tumor along with the necrosis which is an added feature when

compared to the existing popular segmentation algorithms. The algorithm is simple and straight

forward. It needs no training data and image registration process. It uses a single MR image.

We plan to implement multiphase segmentation to delineate tumor, necrosis and edema. Also

we plan to extend this to 3D segmentation.

Tumor-1 Tumor-2 Tumor -3 Tumor-4

Our Algorithm 0.988 0.989 0.985 0.989

Chan-Vese 0.843 0.898 0.865 0.897

Normalized Graph Cut 0.822 0.865 0.834 0.853




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AUTOMATIC DETECTION AND SEGMENTATION OF · PDF fileJ. S. Gonal and V. V. Kohir AUTOMATIC DETECTION AND SEGMENTATION OF BRAIN ... LEVEL SETS INTEGRATED WITH BOUNDING BOX Jayalaxmi S