5
Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. CCSEIT-12, October 26-28, 2012, Coimbatore [Tamil nadu, India] Copyright © 2012 ACM 978-1-4503-1310-0/12/10…$10.00. Automatic Brain Portion Segmentation from T1 and T2- weighted Coronal MRI of Head Scans using Bond Number and Block Truncation Method K. Somasundaram Image Processing Lab Department of Computer Science and Applications Gandhigram Rural Institute (Deemed University) Gandhigram, Dindigul, Tamil Nadu, India [email protected] K. Ezhilarasan Image Processing Lab Department of Computer Science and Applications Gandhigram Rural Institute (Deemed University) Gandhigram, Dindigul, Tamil Nadu, India [email protected] ABSTRACT In this paper we propose a fully automatic method for segmenting the brain portion from the MRI of head scans. We make use of Bond Number (N o ) to detect the edges of brain and head. Block truncation is used as a filter in edge detected image and finally morphological operations are done to segment the brain portion. The segmented brain portions are compared with the gold standard images provided by the IBSR. Experimental results on a number of MRI volumes show that the proposed method gives satisfactory and comparable results to that of few existing brain extraction method. Keywords Bond number, Block Truncation Coding, MRI, Largest Connected Component (LCC). 1. INTRODUCTION Medical imaging is most important to diagnose the diseases, for surgical planning, research activities, treatment planning etc. Medical imaging techniques have grown enormously and there are many imaging modalities. They are X-rays, CT scans, Ultra sound scans, Magnetic Resonance Images (MRI) and so on. Each method has its own advantages as well as disadvantages. MRI is such a modality which is used to visualize the 3-dimensional internal structure of soft tissues. MRI is a non-invasive, non destructive and non-ionizing method MRI is taken in three different types, T1- Weighted, T2- Weighted and Proton Density (PD), each differ in their relaxation time. MRI is taken in orientations axial, sagital and coronal. An axial is taken from toe to head, sagital is taken from left to right side of the head and coronal is taken from back to front of the head. These three orientations give different views of the internal brain tissues. Brain portion of human head scan is surrounded by skull, cerebrospinal fluid (CSF), dura matter, spinal cord and so on. Hand stripping of brain portion from MRI head scan is more time consuming, and therefore there is a need for some automated method to segment the brain portion. Many research works have been reported in MRI segmentation since 1996. Kapur et al. proposed the first brain segmentation algorithm[1]. Anisotropic operation and thresholding have been used to generate rough brain mask, followed by an active contour and snake algorithm to get fine brain portion [2]. Some popular methods are Brain Extraction Tool (BET) [3], Brain Surface Extractor (BSE) [4] and Hybrid Watershed Algorithm (HWA) [5]. In BET, image histogram is used to generate a binary image, and then Center of Gravity (COG) of head portion is estimated in the binary image. A deformable sphere has been moved from the COG to brain surface and extracted the fine brain portion. BSE is based on edge detection and morphological operations. In BSE, anisotropic filters, edges detected by Marr- Hildreth followed by morphological operations are used. HWA is a combination of edge detection operation and surface reconstruction. The edge detection is done by watershed algorithm and the surface reconstruction is based on brain atlas. HWA is more sensitive than BET and BSE. BSE has higher specificity than BET and HWA [6]. BSE results are some times closer with BET [7]. Few other methods are used for brain extraction are Model based level sets (MLS)[8], 2D region growing [9], Brain Extraction Algorithm (BEA) for T1 [10], histogram and simplex mesh [11]. In this work, we report a new brain extraction algorithm (BEA) using Bond number and Block Truncation Coding (BTC). Bond number is one of the concepts on surface tension of fluids and it is used to study the character of forced non-linear waves [12]. The bond number represents the ratio of effective gravitational forces to surface tension forces [13]. Such a bond number is used here for edge detection. Plenty of research work are reported on BTC. The base article on BTC can be found in [14]. Some recent studies are: accelerated BTC [15],vector quantizer for the color image compression in LCD overdrive [16]. In our work here BTC is used as a noise filter. Morphological operations are used to refine the brain boundaries. The performance of this algorithm is evaluated using the hand 306

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Page 1: Automatic Brain Portion Segmentation from T1 and T2 ... · Bond number, Block Truncation Coding, MRI, Largest Connected Component (LCC). 1.NTRODUCTION I Medical imaging is most important

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. CCSEIT-12, October 26-28, 2012, Coimbatore [Tamil nadu, India]

Copyright © 2012 ACM 978-1-4503-1310-0/12/10…$10.00.

Automatic Brain Portion Segmentation from T1 and T2-weighted Coronal MRI of Head Scans using Bond Number

and Block Truncation Method K. Somasundaram Image Processing Lab

Department of Computer Science and Applications Gandhigram Rural Institute (Deemed University)

Gandhigram, Dindigul, Tamil Nadu, India

[email protected]

K. Ezhilarasan Image Processing Lab

Department of Computer Science and Applications Gandhigram Rural Institute (Deemed University)

Gandhigram, Dindigul, Tamil Nadu, India

[email protected]

ABSTRACT

In this paper we propose a fully automatic method for segmenting the brain portion from the MRI of head scans. We make use of Bond Number (No) to detect the edges of brain and head. Block truncation is used as a filter in edge detected image and finally morphological operations are done to segment the brain portion. The segmented brain portions are compared with the gold standard images provided by the IBSR. Experimental results on a number of MRI volumes show that the proposed method gives satisfactory and comparable results to that of few existing brain extraction method.

Keywords

Bond number, Block Truncation Coding, MRI, Largest Connected Component (LCC).

1. INTRODUCTION Medical imaging is most important to diagnose the diseases, for surgical planning, research activities, treatment planning etc. Medical imaging techniques have grown enormously and there are many imaging modalities. They are X-rays, CT scans, Ultra sound scans, Magnetic Resonance Images (MRI) and so on. Each method has its own advantages as well as disadvantages. MRI is such a modality which is used to visualize the 3-dimensional internal structure of soft tissues. MRI is a non-invasive, non destructive and non-ionizing method MRI is taken in three different types, T1- Weighted, T2- Weighted and Proton Density (PD), each differ in their relaxation time. MRI is taken in orientations axial, sagital and coronal. An axial is taken from toe to head, sagital is taken from left to right side of the head and coronal is taken from back to front of the head. These three orientations give different views of the internal brain tissues. Brain portion of human head scan is surrounded by skull, cerebrospinal fluid (CSF), dura matter, spinal cord and so on. Hand stripping of brain portion from MRI head scan is more time consuming, and therefore there is a need for some automated method to segment the brain portion.

Many research works have been reported in MRI segmentation since 1996. Kapur et al. proposed the first brain segmentation algorithm[1]. Anisotropic operation and thresholding have been used to generate rough brain mask, followed by an active contour and snake algorithm to get fine brain portion [2]. Some popular methods are Brain Extraction Tool (BET) [3], Brain Surface Extractor (BSE) [4] and Hybrid Watershed Algorithm (HWA) [5].

In BET, image histogram is used to generate a binary image, and then Center of Gravity (COG) of head portion is estimated in the binary image. A deformable sphere has been moved from the COG to brain surface and extracted the fine brain portion. BSE is based on edge detection and morphological operations. In BSE, anisotropic filters, edges detected by Marr- Hildreth followed by morphological operations are used. HWA is a combination of edge detection operation and surface reconstruction. The edge detection is done by watershed algorithm and the surface reconstruction is based on brain atlas. HWA is more sensitive than BET and BSE. BSE has higher specificity than BET and HWA [6]. BSE results are some times closer with BET [7].

Few other methods are used for brain extraction are Model based level sets (MLS)[8], 2D region growing [9], Brain Extraction Algorithm (BEA) for T1 [10], histogram and simplex mesh [11].

In this work, we report a new brain extraction algorithm (BEA) using Bond number and Block Truncation Coding (BTC). Bond number is one of the concepts on surface tension of fluids and it is used to study the character of forced non-linear waves [12]. The bond number represents the ratio of effective gravitational forces to surface tension forces [13]. Such a bond number is used here for edge detection. Plenty of research work are reported on BTC. The base article on BTC can be found in [14]. Some recent studies are: accelerated BTC [15],vector quantizer for the color image compression in LCD overdrive [16]. In our work here BTC is used as a noise filter.

Morphological operations are used to refine the brain boundaries. The performance of this algorithm is evaluated using the hand

306

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segmented gold standard images in IBSR[17], and by calculating the similarity measures Jaccard[18] and Dice[19].

The remaining part of the paper is organized as follows. In section 2, we present our method. In section 3 the results and discussion are given. In section 4 the conclusion is given.

2. THE PROPOSED METHOD

We make use of the concept of bond number which is used in characterizing the shape of bubbles moving in its surrounding. The shape of a bubble or liquid drop is defined by the energy of the bubble and its surface area. The bond number (No) for a bubble is given by [12]:

where, ∆ρ is the difference in density of the two phases in kg/m3, g is the gravitational force in m/sec2, L is the characteristic length in m and σ is the surface tension in N/m and it is represented as :

where, energy is the total energy of the moving bubble and area is the surface area of the bubble.

We consider a sub-image block of size n×n pixel as equivalent to a bubble. For the image block, we make the following analogy.

∆ is given by:

where, ‘i’ and ‘j’ are the row and column value of the pixel in

the image(I). ‘g’ is a constant, L is equaled to n, and the area is n2, xij is the value in the image pixel,

where, T is the intensity threshold value. The threshold value T has been computed from input image (I) by using Riddler’s method, which gives an optimal threshold value. Riddler’s method is an iterative one. To start with the Riddler’s method we first need to compute an initial threshold T1 as:

where, n is the total number of pixels in the image (I). Using this initial threshold T1, we divide the pixels into two groups G1 and G2.

We then compute a new threshold which is the mean value of pixels in each group as

where, N1 and N2 are number of pixels in G1 and G2 respectively. The above process is repeated until T and T1 converges. The final threshold value T is used to identify the pixel where we need to calculate the energy.

The energy is calculated for object pixels which have intensity value higher than the threshold value (T). The energy of each object pixel is used to calculate equation (2) and from (1) we can obtain No and forming an image IB with No as its pixels.

Then the threshold value TB is computed from the image IB as per equations (5), (6) and (7). An image IEG has been obtained as:

The flow chart of the proposed method is shown in Figure. 1.

I

IB

IEG

IBT

IHE

IHM

IROB

IEB

ILCC

ISB

IBrain

Figure. 1 Flow chart of the proposed method

The image IEG contains edges and some non boundary regions between the brain and the skull. Some unwanted edges are detected due to air gaps in the brain or artifacts. These edges create some problem while segmenting the brain portion. The edges covering the brain regions are continuous and others are

Input Image (I)

Compute the Bond number (NO)

Obtain IEG from IB

Obtain image IBT from IEG using BT.

IROB = IHM - IBT

Performing Erosion

Operation

LCC analysis

Performing Dilation Operation

Obtain Brain portion from ISB

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not. To remove these spurious edges, we perform block truncation. The image IEG is divided into n number of non-overlapping blocks. The number of bright pixels (NBP) in each block in the image (IEG) is counted. If the value of NBP is less than one fourth of the total number of pixels in that block, then it is considered as non brain edges and it is removed, otherwise it is treated as edges of brain or scalp regions. The pixel in the block that is removed all set to 0, and the pixels is the black retained with 1. This operation is performed on all the non overlapping blocks and the image IBT is obtained.

To obtain region of object (IROB), the image IBT is subtracted from the mask (IHM). The image (IHM) is generated in two stages. In the first stage, the binary image (IBin) of the input slice is obtained by using eqn. (7). The binary image (IBin) is given by:

During second stage, run length scheme is used to get head contour (IHE) from the binary image (IBin). This scheme is used to detect first active point from right to left and left to right of the binary image (IBin). The head contour is obtained (IHE) after these runs on the binary image (IBin). By collecting active points from each row, we get head mask (IHM) which is used to prune non brain regions.

The image region of object (IROB) obtained as:

The rough brain (IROB) contains many weakly connected structures and we perform morphological erosion operations to remove them. The eroded image IEB is obtained as:

where SE is the structuring element of 5 x 5 matrix and elements are 1.

However the image IEB contains many disconnected regions of brain and non-brain portions, brain is the largest connected region in the image IEB. Largest connected component (LCC) analysis has been used to segment the brain portion from the image IEB. To find the maximum connected regions, the labeling process is used. This process starts by scanning an image, pixel-by-pixel (from top to bottom and from left to right) in order to identify connected pixel regions, i.e. the regions of the adjacent pixels which share the same set of intensity values. The distinct labels are set to each group of pixel regions. A connected region with maximum number of same label is considered as the largest connected component ILCC from the image IEB.

Morphological dilation operation has been performed to recover the pixels which were lost during the erosion operation. The image ILCC has been dilated using the structuring element SE. The dilation of ILCC gives the segmented brain portion ISB as:

The image ISB is the fine brain mask for segmenting the brain portion Ibrain.

3. RESULTS AND DISCUSSION

3.1 Materials used

We used 11 volumes of MRI to evaluate the performance of our method. 6 volumes of normal T1 coronal images are taken from the Internet Brain Segmentation Repository (IBSR). This website also maintaining the hand segmented or gold standard for normal volumes. 2 volumes of T2 coronal MRI and 3 volumes of T1 coronal MRI were collected from Devaki Scans & Diagnostics Pvt. Ltd, Madurai, Tamil Nadu, India.

Figure. 2

Figure. 2 Images taken from IBSR(202_3).

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Figure. 3

Figure.3 Brain portion extraction from images of Figure. 2

We carried out experiment by applying our method on the material pool collected. For quantitative performance evaluation we computed the average values of Jaccard (J) and Dice (D) similarity indices for each volume and are given in Table .1 for IBSR data sets. The J is given by:

and D is given by:

Table 1 Computed values of Jaccard and Dice Similarity indices

using the proposed method and BET

Dataset

No

Proposed Method BET

Avg.

value of

Jaccard

Similarity

Avg.

value of

Dice

Similarity

Avg.

value of

Jaccard

Similarity

Avg.

value of

Dice

Similarity

202_3 0.9384 0.9762 0.8583 0.9237

205_3 0.9505 0.9744 0.7107 0.8309

100_23 0.9333 0.9655 0.8271 0.9053

191_3 0.9536 0.9760 0.8455 0.9163

13_3 0.9182 0.9565 0.8566 0.9227

8_4 0.96283 0.9810 0.7778 0.8750

As there is no gold standard for the remaining volumes we have not computed D and J for them. From Table 1 we observe that the average value of D is in the range of 0.9565 to 0.9762 and J is the range of 0.9182 to 0.9536. The BET results [20] for same volumes are shown in Table 1.For qualitative evaluation, we give

the original image of T1 coronal taken from IBSR (202_3), in Figure. 2. Figure. 3 show images segmented by our proposed method. The Figure.4 shows T2 coronal images taken from Devaki Scans & Diagnostics Pvt Ltd, Madurai, Tamil Nadu, and India. Figure. 5 shows segmented images of Figure. 4 by our method.

Figure. 4

Figure. 4 Images taken from Devaki Scans & Diagnostics Pvt. Ltd, Madurai.

Figure. 5

Figure. 5 Brain portion extraction from images of Figure. 4

The algorithm is developed using Java 2.0. Using a system with 512MB RAM, AMD athlon with a processor speed 1.8GHz.

4. CONCLUSION

In this paper we have proposed a new algorithm to extract brain portion from T1 coronal and T2 coronal images. We have made use of a novel idea of using bond number used of bubbles. Largest Connected Component analysis and morphological operations were used to extract brain portion from MRI of head scans. Qualitative performance evaluation of our method measured in terms of Jaccard and Dice similarity indices show that the proposed method gives comparable results to that of the popular method Brain Extraction Tool (BET).

5. ACKNOWLEDGEMENTS

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This work is funded by the University Grant Commission, New Delhi, and Grant No: FNo 37-154-2009(SR). The Internet Brain Segmentation Repository (IBSR) provided manually-segmented results along with 6 volumes of MRI and Devaki Scans and Diagnostics Pvt. Ltd, Madurai, Tamil Nadu, India provided 5 volumes of MRI brain images.

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