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Automatic Functional Brain MR Image Segmentation Using Region Growing and

Seed Pixel

Imran Siddique1, Imran Sarwar Bajwa1 , M. Shahid Naveed2 and M. Abbas Choudhary3

1 Facuty of Computer an EmergingSciences Balochistan University of Information Technologyu and Management Sciences

Quettai, Pakistan. Phone: +92 (81) 2880163 Fax: +92 (81) 9201064

E-mail: imransbajwa@yahoo.com

2 Computer Science Department Islamia University Bahawalpur, Pakistan.

Phone: +92 (62) 9255466 E-mail: shahid_naweed@hotmail.com

3 Balochistan University of Information Technologyu and Management Sciences

Quettai, Pakistan. Phone: +92 (81) 9202463 Fax: +92 (81) 9201064

E-mail:abbas@buitms.edu.pk

Abstract: Magnetic Resonance Imaging (MRI) is used to visualize the anatomy and structure of a body organ for assistance in medical diagnostics of certain disease or conditions and to evaluate a particular disease. Magnetic resonance images of a specified anatomy are constructed by using radio waves, a magnetic field and compute. This technical paper demonstrates the segmentation process of brain MR images by using Region Growing and Seed Pixel methods. Segmentation is a noteworthy phase in the various image processing applications. Automatic Brain MRI Segmentation is a simple, robust and efficient image segmentation algorithm for classifying brain tissues form dual echo Magnetic Resonance (MR) Images. The designed system incorporates this robust ability of the described algorithm to segment the various parts of brain MR image automatically. The utilized algorithm consists of an assortment of components as adaptive histogram

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analysis, threshold, and region growing segmentation. These vigorous techniques are used for the sake of accurate categorization of assorted brain regions such as the brain white, gray matter, cerebrospinal fluid and ventricular regions. The orthodox techniques exploited for the analysis of a sequence of MR images was time consuming and inefficient. The conducted research minimizes this overhead by using the semi-automated designed system which has been tested successfully on multiple Dicom standard MRI real brain images.

Keywords: Brain MR Image Segmentation, Region Growing, Seed Pixel,

Automatic Image segmentation

1. INTRODUCTION

Brain is one of the most complex organs of a human body so it is a vexing problem to discriminate its various components and analyze it constituents. Common image processing and analysis techniques provide ineffective and futile outcomes. Magnetic resonance images are very common for brain image analysis. MRI is non-incision approach, which is used to produce a detailed image of human body organ as brain by utilizing the magnetic filed and radio waves. MR images are used to diagnose medical disorders. Besides the classification of the various parts of a brain image, changes in human brain volume due to the aging factor can also be studied vigorously. There are a wide variety of approaches that have been incorporated for the segmentation of a brain MR image. The used techniques can be divided into two categories as structural and statistical [2].

Structural techniques are based on the spatial characteristics of the underlying image. Spatial characteristics comprise various components such as edges and regions. Various edge detection algorithms have been applied to extract boundaries between different brain tissues. However such algorithms are vulnerable to underlying artifacts and discriminations. Region growing is another popular structural approach. This approach principally divides an input image into small discriminated regions which are mainly termed as “seeds” [3]. Then, all defined boundaries between the distinctive contiguous regions are analyzed. Analysis phase endures the areas with strong boundaries are endured and discards the feeble boundaries and the adjacent regions are merged with each other. This boundary analysis practice is carried out

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iteratively until no boundaries are weak enough to be abandoned. This method can be employed in to extract surface layers of a brain. The commercial software package ANALYZE has also incorporated the segmentation ability of the region growing approach [7]. Contrary to these abilities, this approach is not considered robust as the performance of this approach depends on seed selection and definition of the regions.

On the other hand, statistical methods mark pixels according to the computed probability values by using thresholding-based methods. These probability values are determined on the basis of intensity distribution of the image. The image scenes containing solid objects in the background with higher intensities are well separated from the objects at foreground using various thresholding-based methods. Generally, these thresholding-based methods are unlikely to produce reliable and consistent results. Therefore, these statistical methods are not adequately effective for brain MR image analysis.

This research deals with a semi-automatic region-growing segmentation technique. This method only needs one seed inside the Region of Interest (ROI). ROI has been applied in the conducted research for the segmentation of the spinal cord region. Moreover, it could be successfully applicable in other computer vision domains then medical imaging as it is a general image segmentation method. A human brain has multifaceted and convoluted anatomical details. There are various constituting component such as the brain white matter, gray matter, cerebellum, cerebrospinal fluid and ventricular regions as shown in the side and upper view of the brain MR image in the following figures.

Fig – 01. Sagittal orientation of Brain

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The cerebrum is responsible for the control of purposeful motor movement, speech, intelligence, memory, emotion, and sensory processing. Within the brain, there are interconnected hollow spaces filled with cerebrospinal fluid (CSF). These hollow spaces are known as ventricles. The grey matter is the areas where the actual "processing of brain" is done. A grey colors due to all the grey nuclei in the cells that make it. About 40% of the human brain is made up of gray matter. White matter is responsible for communication between the various grey matter regions and between the grey matter and the rest of the body. Cerebrospinal fluid (CSF) is found within the brain and surrounds the brain and the spinal cord. It is a clear, watery substance that helps to cushion the brain and spinal cord from injury. This fluid circulates through channels around the spinal cord and brain, constantly being absorbed and replenished.

Generally, to fully automate the image segmentation procedure is a vexing problem and intricate to address. In Magnetic Resonance Images, there are two relaxation processes [12], T1 and T2, going on at the same time. The image contrast is highly dependent on these relaxation processes. Image contrast depends on how much of each process is allowed to happen. MR image of various contrasts are available for processing as T1, T2 and PD.

T1 relaxation images can be acquired from MR machine by setting repetition time (TR) to 600 milliseconds and echo time (TE) 10 milliseconds.

T2 relaxation images can be acquired by setting TR to 3000

milliseconds and TE 120 millisecond. Proton density (PD) images can be acquired by setting TR to 2000

milliseconds and TE to 10 milliseconds. T2 is the image which is used in various experiments conducted in

this research.

Fig – 02. T1, T2 and PD image samples

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2. PROBLEM DEFINITION MRI has several advantages over other imaging techniques, as they

are more conspicuous and obvious due to 3-dimensional representation of data with high contrast between soft tissues. However, the amount of data is too much for manual analysis/interpretation. A typical MR study of a brain image involves multi-modal information in three cross sections as sagittal, coronal and axial. Each cross section contains 10 to 30 2D slices. Even for a single study typically there are hundred or more images to be acquired and analyzed and this has been one of the major obstacles in the effective use of MRI. It is very difficult for the neurosurgeon and radiologist to view the structure of the various brain tissues separately and a good radiologist and neurosurgeon needs to view the tissue structure for the diagnostic purposes. To address this issue, automatic or semi-automatic techniques of computer-aided image analysis are necessarily required for the segmentation of Brain MR Images into different classes of tissues, especially gray matter (GM), white matter (WM) and cerebrospinal fluid (CSF).

3. PROBLEM SOLUTION

“Brain MRI Segmentation” provides facility of segmenting the various brain tissues such as CSF, Ventricular, White Matter, and Grey Matter. These tissues are primarily extracted from the dual echo MRI slices at a position in axial plane about 7 to 8 cm from the top of the head. (T1, T2, T3). An automatic brain MR Image segmentation algorithm, which is based on region growing and seed pixel methods can be used to address the specified problem.

4. USED METHODOLOGY

This segmentation algorithm consists of a sequence of processing steps, which combine structural information with image analysis techniques, which are summarized as following:

The cerebrum region is extracted using a sequence of image processing operations, which includes, thresholding, region growing segmentation, and masking functions.

The CSF regions are detected from T2 weighted images by adaptive thresholding for the further ventricular and extra ventricular regions identification

Ventricular region is depicted in the middle of the cerebrum using iterative thresholding method, and region growing segmentation ventricular is extracted.

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The brain matter is further classified into gray and white matter form the PD images using low-level knowledge based segmentation rules.

Following is the work flow model of the designed system.

output

T2

ventricular

Make T2 cerebrum mask

Extract T2 ventricular region using mask

Make mask for PD cerebrum by combining T2 cerebrum and T2 ventricular mask

Apply PD cerebrum mask on PD image to extract PD cerebrum region

Input T2 MR image

Extract T2 cerebrum region using mask

Make T2 CSF mask

Extract T2 CSF using CSF mask

Make T2 ventricular mask

Apply thresholding on PD cerebrum region to separate white and gray matter

T2 cerebrum

T2 CSF

output

output

output

Input PD MR image

PD gray matter

PD white

matterOutput

Fig – 03. Functional Brain MR Image Segmentation Using Region Growing and Seed Pixel

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5. DESIGNED SYSTEM WORK FLOW The segmentation algorithm consists of a sequence of processing

steps which combine structural information with image analysis techniques, and is summarized in the following

5.1. Extraction of the cerebrum region

The first step in the segmentation process is to isolate the cerebrum region form the bone and soft tissue. A single threshold is applied to the T2 weighted image. This separates all the brain tissues form the background noise. In first step we perform the following steps to extract the Cerebrum Region.

First of all we compute the threshold value from the histogram analysis. In our case below 30 is the pixels that belong to the background region, and the threshold value is 32 that separate the brain tissues form the background noise. The resultant image is shown in the following figure.

The next step in the extraction of the cerebrum region is to grow the

idle big white region of the thresholded image. For this purpose we use Region Growing Segmentation technique. In which we provide the seed pixel, the middle pixel of the thresholded image, dividing the image WIDTH and HEIGHT by 2. The Region Growing Segmentation technique grows the middle big white region, dividing the image WIDTH and HEIGHT by 2. The Region Growing Segmentation technique grows the middle big white region.

Fig – 04. Threshold T2 weighted image

Fig – 05. Cerebrum Region

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The next step in the extraction of Cerebrum Region is masking. In the previous step we made a cerebrum mask. The pixels that have 255 intensities (means white) belong to the cerebrum region. When we apply masking on the original T2 weighted then the cerebrum region is extracted form the T2 weighted image. The resultant image is shown in the figure 5.

5.2. Extraction of the CSF (Cerebrospinal Fluid) Region

CSF is probably the easiest to identify in the MR image. The threshold needed to identify CSF form the T2 weighted image can be computed form the appropriate histogram.

First a histogram of the T2 weighted image is constructed using only the pixels in the cerebrum region. The threshold value Tcsf is set at the 40% of this histogram. This value of 40% is arrived at based on the results of clinical studies on the ventricular to brain volume ratio published in the literature [22] [23]. The CSF Regions close to or touching the cerebrum boundary are recognized as extra ventricular CSF

The white pixels belong to the CSF Region. Then we apply the masking on the Cerebrum image and the CSF Region is extracted form the MR T2 weighted image.

5.3. Extraction of the Ventricular Region

The ventricular is located in the middle of the cerebrum region. First we apply the Iterative Threshold Method on the T2 weighted image and compute the threshold value. In our case the threshold value is 74. Then we threshold the T2 weighted image.

Fig – 07. Ventricular Mask

Fig – 06. CSF Regions

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If we observed the thresholded image the middle region belongs to the ventricular region. Using the Region Growing Segmentation technique grows the middle region of the thresholded image. We provide the seed pixel the middle of the image.

Apply this ventricular mask on the previously detected CSF. Finally

the ventricular region is extracted form the CSF map.

5.4. Gray and White Matter Detection The next step in the brain segmentation process is separate the white

and gray mater. To perform this step we apply the following sequence of operations. Fist we load the MR PD weighted image.

Make a mask only for the cerebrum (excluding ventricular). This is

very simple. We have cerebrum mask and ventricular mask. The pixels that have 255 intensities (means white) in the cerebrum mask and not have intensity 255 (means white) in the ventricular mask are the cerebrum region pixels in the PD weighted image.

We apply this mask on the PD weighted image and extract the cerebrum region (excluding ventricular).

Fig – 09. PD Weighted Image

Fig – 08. Ventricular Region image

Fig – 10. Cerebrum region of the PD image

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Now if we make a histogram of the cerebrum region of PD image

there may be a two distinguished peaks in the histogram. However, due to the image acquiring degradation, commonly known as shading artifact, magnetic field non homogeneities, and partial volume effects, the transition between gray and white matter is often smooth and simple threshold operation is not give reliable results. In order to segment the brain matter we use the fact that regions immediately surrounding the CSF are the gray matter. The statistics of the pixel intensities in the surrounding regions are estimated form the PD weighted image and used in computing a Threshold Tgm for separating the gray matter form the white matter

Tgm= µ - ½ δ

Where µ and δ are the mean and standard deviation, respectively, of

the pixel intensities. This value is often very close to the threshold which separates these two peaks.

After calculating the Tgm we threshold the T2 weighted cerebrum image that is constructed in the first step. The thresholded image is called white and gray matter mask. In the WG mask the white region belongs to the gray matter and black region belongs to the white matter.

Using masking applies this WG mask on the PD image and separates

the gray matter and white matter. The resultant images are:

Fig – 11. Threshold image

Fig – 12 Gray Matter White Matter

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6. CONCLUSION In this paper, the problem of the region segmentation in MR images

due to the brain image complexity has been addressed using seed point and region growing algorithm. The accomplished research deals with a semi-automatic region-growing segmentation based system. Conventionally, for brain MR image segmentation, there are various structural and statistical approaches. Structural methods are based on the spatial properties of the image, such as edges and regions. Region growing is a structural approach that is used for spinal cord segmentation.

7. FUTURE WORK

The designed system has robust ability to read spatial information from a brain image and classify the various parts of the brain image. This system specifically works for the major components of brain as the brain white matter, gray matter, cerebellum, cerebrospinal region, ventricular region, etc. Besides to these issues, the segmentation of the various brain tissues is also worthless in the examination of MRI Brain Images. It has a wide range of applications form data compression and revelation to providing quantitative information for medical analysis. This can help in the detection of various changes in the brain volume due to the aging factor. The work in these areas can also be a significant enhancement in the currently carried out research.

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