a ovel technique for finding the boundary between the ... · 1 introduction magnetic resonance...

A �ovel Technique for Finding the Boundary between

the Cerebral Hemispheres from MR Axial Head Scans

K. Somasundaram

1 and T. Kalaiselvi

1

1 Department of Computer Science and Applications,

Gandhigram Rural University,

Gandhigram, Tamilnadu, India.

Abstract. We present a robust technique to detect a linear boundary

between the cerebral hemisphere using the knowledge of brain features

and magnetic resonance imaging (MRI) characteristics. We use two

approaches to extract the brain from T1 and T2 MR axial head scans to

find the brain contour. From the brain contour, we detect the boundary

between the hemispheres by joining the edges of the two portions of cleft

that corresponds to inter-hemispheric fissure present in the contour. The

detected boundary is used to separate the cerebrum into two hemispheres.

We tested our method over several scans collected from MRI centre and

authorized brain image web sites. The performance was validated with

medical experts using both normal and abnormal scans. The predictive

accuracy gives the quantitative measure calculated between the hand-

stripped and our result and is 99% in normal cases and lowered for

abnormal slices.

Keywords: cerebral hemispheres, inter-hemispheric fissure, mid-sagittal

plane, brain extraction algorithm, brain contour, knowledge system, slice

transformation

1 Introduction

Magnetic Resonance Imaging (MRI) is a diagnostic tool used to visualize the

body’s soft tissues. It is noninvasive and does not require ionizing radiation (X-

rays) or radioactive tracers. It uses magnetization and radio waves to produce

high quality three- or two-dimensional cross sectional images in any direction

from top to bottom (axial orientation), side to side (sagittal orientation), or front

to back (coronal orientation). Hydrogen imaging is the most widely used MRI

procedure. The intensity characteristic of a given tissue depends on the proton

density (PD) of the tissue. The higher the PD, the stronger the response signals.

4th Indian International Conference on Artificial Intelligence (IICAI-09)

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MR image contrast also depends on two other tissue-specific parameters: the

longitudinal relaxation time T1 and the transverse relaxation time T2. T1 images

are typically used for anatomic information whereas T2 images offer high

sensitivity to most pathologic processes. The rich anatomy information provided

by MRI has made it an indispensable tool for medical diagnosis especially human

brain analysis in recent years.

A human brain has three main components cerebrum, cerebellum and brain

stem. Cerebrum is the largest part of the brain and performs the higher level

functions of the brain. It contains two hemispheres known as left cerebral

hemisphere (LCH) and right cerebral hemisphere (RCH). These cerebral

hemispheres are divided by a deep cleft in lengthwise along the body’s median

plane and is known as longitudinal fissure as shown in Fig.1. The cleft is also

referred to as the inter-hemispheric fissure (IHF). The cleft between the two

cerebral hemispheres contains cerebro spinal fluid (CSF) and the falx cerebri, the

fold of dura matter that separates the cerebral hemispheres.

Fig. 1. Longitudinal Fissure or Inter-Hemispheric Fissure in human brain.

Edge 1 on upper

portion of cleft

Edge 2 on lower

portion of cleft


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The human body’s median plane is referred as mid-sagittal plane (MSP) by

the medical imaging modalities like computed tomography (CT), MRI, positron

emission tomography (PET) and single photon emission computed tomography

(SPECT) techniques due to its relative alignment with the sagittal plane of the

human body. MSP is defined as a planar surface which bisects the cerebrum into

two cerebral hemispheres at their point of bilateral symmetry and alternatively

defined as the plane that passes through the IHF [1] [2] [3]. The standard 3D

space for brain imaging proposed by Talairach [4] fixed the ideal MSP along the

vertical central line of each 2-D axial or coronal slice. But in real time imaging

space of patients, the partitioning plane between the hemispheres does not

coincide with the vertical central line, the ideal MSP, due to the tilt of patient’s

head in the device or the selection of different scanning angles during the image

acquisition phase. Hence the detection of translation and angle of rotation of the

partitioning plane i.e., patient MSP about the ideal MSP is useful for further

processing like image registration, anatomical standardization and spatial

normalization. This is also useful in brain diagnostic process to verify the

position of IHF whether it fits with or deviated from MSP. Usually a normal

brain exhibits symmetry about the IHF. The estimation of MSP is useful for

recognizing non healthy regions of brain and diagnosing several brain diseases

like schizophrenias [5], Creutzfeldt-Jakob disease in T2-weighted MR images

[6], Epilepsy [7] and Alzheimer [8].

Several methods have been developed for MSP extraction. They are

classified into two categories: feature based and image similarity based methods

[1]. Feature based methods define the MSP as the plane that best fits the

longitudinal fissure [3] [9] [10] [11]. Image similarity based methods mostly

depend upon symmetry measure like cross-correlation about a point or line or

block to produce the optimal MSP [1] [2] [12] [13] [14] [15]. Cross correlation is

an expensive operation in general [1]. So we prefer for the feature based methods.

The existing feature based methods have their own limitations. They require

either human intervention or complex functions for optimizing the result. But we

propose a fully automatic intelligent system that solely depends on the brain

anatomy and image intensity information.

In a 2D slice, MSP is a line, mid-sagittal line (MSL) that passes through the

IHF. In this paper, we present a knowledge based method to detect MSL in a

MRI scan. Our method aims to generate the MSL by joining the edges of upper

and lower portions of cleft as shown in Fig.1. These cleft portions are visible

clearly only after removing the surrounding non-brain regions from the MR axial

head scans. So we employ brain extraction algorithms as a preprocessing tool for

our approach. The proposed scheme is simpler than the existing methods and is

based on image intensity and brain features.

The paper is organized as follows. First, we present our method. Then, the

materials used in our experiments are given. The experimental results and

discussions are given finally.


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2 Method

Our method comprise of three stages as shown in Fig 2. In stage-1, the brain

portion is extracted from MR Head scans by eliminating the non-brain portions

like scalp, eyes, fat, muscle, background clutter that are superimposed on the slice

in the form of acquisition parameters and patient’s data. In stage-2, the contour of

extracted brain is utilized to generate the boundary between the cerebral

hemispheres. This boundary corresponds to MSL. Finally in stage-3 the image is

transformed to fix the detected MSL to middle vertical line, the world coordinate

of brain space.

Input: MR axial Head scan

Stage-1: Brain Portion Extraction

Fig. 2. Flowchart of our proposed method

Output: Mid-Sagittal Line (MSL)

Stage-2: Detection of Boundary line

between Hemispheres

Stage-3: Slice transformation to

Standard Space

START

STOP


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2.1 Stage-1: Brain Extraction

In this stage the brain portion is separated from the surrounding non-brain

regions. We have developed two Brain Extraction Algorithms (BEA) for T1 and

T2 scans respectively [16] [17] [18] referred as T1-BEA and T2-BEA hereafter.

The T2-BEA makes use of the combined effect of anisotropic diffusion

process [19], optimal thresholding and morphological processes [20] to separate

the brain from non-brain portions. The diffusion process is used to highlight the

brain from T2 head scan followed by optimal thresholding technique to generate

a rough binary brain portion. The morphological operations, erosion and dilation,

and connected component analysis are used to produce the brain mask by

removing the weakly connected non-brain regions like eyes, neck and etc. Finally

the brain mask is used to extract the brain from T2 scans. The results of T2-BEA

at different stages for a sample slice are given in Fig.3a-3e.

Fig. 3. The results of T2-BEA (a) Original Image (b) Diffused Image (c) Binary form of

rough brain (d) Brain mask produced by morphological operations: erosion and dilation,

and connected component analysis (e) Extracted Brain

The T1-BEA initially detects three main features of human head scalp, skull

and brain using spatial and intensity information of T1 scans and produce a 3-

labeled image as shown in Fig.4b with label 0 for background, label 1 for scalp

and brain and label 2 for skull and other non-brain tissues. Then using run length

identification scheme for labeling [20], it either discards the scalp or extracts the

brain portion to select the rough portion of brain region from T1 scans. The rough

brain region is shown in Fig.4c. Finally the morphological operations and

connected component analysis are employed to produce brain mask as given in

Fig.4d. The brain mask is used to extract the fine brain (Fig.4e) from the original

image (Fig.4a).


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Fig. 4. The results of T1-BEA (a) Original image (b) 3-labeled gray image with

background (label 0) as black, scalp and brain (label 1) as white and skull (label 2) as gray.

(c) Rough brain portion (d) Brain mask produced by morphological operations: erosion

and dilation, and connected component analysis (e) Extracted brain

2.2 Stage-2: Generation of Boundary between LCH and RCH

The aim of this stage is to identify a boundary between the cerebral

hemispheres. The boundary surface between the hemispheres lies in IHF. The

following expert knowledge are used to trace the boundary surface between

cerebral hemispheres.

1. Intensity information of CSF: The IHF is filled with CSF and CSF

voxels should therefore be good candidates to form boundary surfaces.

2. Spatial information: The IHF, known as longitudinal fissure, divides

the brain in the middle in lengthwise and approximately appears as a

line between the hemispheres

Since each CSF voxel lies between two GM surfaces (falx cerebri) in the IHF

the boundary is strongly affected by partial volume effect (PVE). So the

boundary surface may not be correctly determined using the CSF intensity

information. Hence the shape of boundary is treated as a line and our algorithm is

aimed to generate this line that corresponds to MSL.

A knowledge system is proposed to detect the edges of the IHF present in the

extracted brain. In each 2D axial slice, a mark, MU is made at the nadir of the

upper portion of cleft and another mark ML is fixed at the zenith of the lower

portion of cleft as shown in Fig.5. Then a line, MSL is generated by joining these

two marks MU and ML.


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Fig. 5. Brain with ideal MSL and Proposed MSL

Table 1. The description and general assumption of proposed knowledge system used to

detect the edges of IHF.

�o. Description General

Assumption

For upper edge

(MU)

For lower edge

(ML)

1 Search /

State Space

Within the bounded

rectangle of the

extracted brain

where W is the width

and H is the height

of the rectangle

x∈ [1, H/4]

y∈ [4W/10,6W/10]

x ∈ [3H/4, H]

y∈ [4W/10, 6W/10]

2 Initial /

Start

State

The intersection

point of the initial

MSL (central

vertical line of

bounded rectangle)

and the brain contour

Upper point of

intersection

Lower point of

intersection

3 Goal

State

Two end points of

IHF

nadir of the upper

portion of cleft

zenith of the lower

portion of cleft

4 Rule Consider Cx, Px and

Nx are the x-

coordinates of the

current pixel,

previous pixel and

next pixel

IF

(Px≤Cx & Nx≥Cx)

then

Store the pixel as a

representative pixel

IF

(Px≥Cx & Nx≤Cx)

then

Store the pixel as a

representative pixel

Ideal MSL

MU on upper portion of cleft

Line that connects the marks MU

and ML of the cleft

(Proposed MSL)

ML on lower portion of cleft

Brain contour


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Fig. 6. The general assumption of knowledge system to detect the marks MU and ML of

cleft

The descriptions and assumptions of the proposed knowledge system are

given in Table 1 and in Fig.6. The proposed algorithm for the knowledge system

is as follows:

Steps of Searching Algorithm

1) Start the process from initial state (Table 1)

2) Do the search in the state space along the brain contour

3) For each pixel apply the rule and detect the representative pixels

4) Constructs a separate list of representative pixels of each edge,

i.e., MU-list and ML-list respectively

5) Sort the lists based on x-values of pixels in a descending order

for MU-list and ascending order for ML-list

6) Take the top most pixels as edge points (MU and ML) in each sorted list

respectively.

Special Case: If successive pixels are present at the beginning of the

sorted list then the median pixel will be taken as the edge pixel.

7) Stop the process

The line that connects the MU and ML is corresponding to the boundary line

as shown in Fig.5.

Initial state for MU

Search space for MU Brain Contour

Vertical Central Line of Bounded Rectangle

Bounded rectangle of brain

Initial state for ML

Search space for ML


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2.3 Stage-3: Slice Transformation to standard space

The detected MSL is used to transform the MRI scans to the ideal world co-

ordinates system. For the slice, shown in Fig.3a, the detected MSL (white) and

the 2D world co-ordinate system with slice centre as origin (light gray) are shown

in Fig.5. The detected MSL can be used to compute the required transformation

that maps the MSL to the ideal MSP. The mapping procedure (M) to move from

the imaging coordinates (IC) to world coordinates (WC) is given by:

MWC, IC = R(90-θ) . T(d) (1)

where T is the translation matrix to map the origin of two coordinate systems, R

is the rotation matrix that align the MSL with ideal MSP, d is the distance along

the horizontal axis and θ, the angle of deviation of MSL about the horizontal axis.

The value (90-θ) gives the required rotation angle to fix the MSL with the vertical

axis (ref. Fig.7). The angle θ is calculated as:

θ = tan -1

(m) (2)

where m is the slope of MSL. Fig.7b shows the transformed image shown in of

Fig.7a that makes the MSL to perfectly coincide with the ideal MSP. This is the

required preprocessing step for image registration and spatial normalization

processes in brain imaging pipelines.

Fig. 7. (a) MR scan with detected MSL (in bright color) and world coordinate (in light

gray color) (b) Transformed image with coordinates θ and d

The transformation to world co-ordinate system made to the sample image

(Fig.3a) is shown in Fig.8. The rotated image given in Fig.8c is the final

d

θ 90-θ


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transformed image to standard space that makes the detected MSL coincides with

the ideal MSL.

Fig. 8. (a) Sample image given in Fig.3a with standard space (gray lines) and detected

MSL (white line) (b) Translated image along the horizontal axis (c) Rotated about the

vertical axis

The brain portions appearing on both sides of central line of transformed

image correspond to the two cerebral hemispheres. Using the ideal MSL

generated by the above procedure, the cerebral hemispheres are separated and are

shown in Fig.9.

Fig. 9. Cerebral Hemispheres separated by MSL. (a) Hemisphere at the left side (b)

Hemisphere at the right side


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2.4 Evaluation parameters

Both qualitative and quantitative validations are considered for the

performance evaluation. The qualitative evaluation is simply the visual inspection

of the result done by four experts of the field. The quantitative evaluation based

on sensitivity (S), specificity (Sp) and predictive accuracy (PA) as given in

equations (3)-(5), are performed between hand-drawn brain regions, hemispheres

by the experts and the respective portions produced by our proposed methods.

These parameters are used to measure the performance of the algorithm against

the manual extraction. The sensitivity (S) is the percentage of region of interest

(ROI) voxels recognized by the algorithm and specificity (Sp) is the percentage

of non-ROI voxels recognized by the algorithm using the True Positive (TP),

False Positive (FP), True Negative (TN) and False Negative (FN) values

extracted by an algorithm. Here ROI is either brain or cerebral hemispheres. The

predictive accuracy (PA) is the percentage of both ROI and non-ROI regions

recognized by the proposed method. TP and FP are the total number of pixels

correctly and incorrectly classified as ROI by the automated algorithm. TN and

FN are defined as the total pixels correctly and incorrectly classified as non-ROI

tissue by the automated algorithm.

S = F"TP

TP

+

, (3)

Sp = FPT"

T"

+

, (4)

PA = 100 × F"FPT"TP

T"TP

+++

+ . (5)

3 Materials

We used 40 real MRI volumes of both normal and abnormal subjects for our

experiments. These 3D volumes with anisotropic nature of voxel dimension are

available as a stack of 2D slices. Low contrast and intensity inhomogenity scans

were present with some volumes. Few volumes were affected either by wrap

around artifact or by zipper artifact.

Fifteen subjects of MRI head scans were collected from KGS Advanced MR

and CT Scans, Madurai, Tamilnadu, India. For each subject, scans were taken in

the entire three orientations using1.5T Siemens machine.


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Another fifteen T1 test volumes were obtained from Brain Extraction

Evaluation (BEE) web service maintained by the International Neuroimaging

Consortium (INC), University of Minnesota (ftp://neurovia.umn.edu/pub/BEE).

These T1 subjects were acquired on a Siemens 1.5T scanner using 3D FLASH

with FOV 165×220 mm, matrix 192×256 and voxel dimensions

0.86×0.86×1.0 mm.

Ten datasets of normal and abnormal subjects including brain tumor

(Neoplastic disease) and multiple sclerosis taken from ‘The Whole Brain Atlas’

provided by the Department of Radiology and Neurology at Brigham and

Women’s Hospital, Harvard Medical School, The Library of Medicine and the

American Academy of Neurology

(http://www.med.harvard.edu/AANLIB/home.html). The dimension of each of

volume is 256×256 pixels and slice thickness varying from 2-5mm with 260mm

field of view. So the pixel dimension is fitted to 1×1 mm.

4 Results and Discussions

Our segmentation method was initially tested on few 2D normal and

abnormal axial slices that were chosen from real brain volumes collected from

MRI scan centres and websites. Both qualitative and quantitative evaluations are

done for validating our method. The results obtained are given in Fig.10. They

were inspected and appreciated by medical experts. Our method not only

separates the cerebral hemispheres but also removes the unwanted non brain

regions appearing around the hemispheres. Therefore our method will be useful

for further processing like tissue segmentation, image compression, registration

and bilaterally symmetry analysis procedures.

Ten sample slices are selected from our material pool for the quantitative

evaluation. The Reason for selecting these slices is that these slices have different

characteristics like type, size, contrast, location, position and pathology. The

parameter values in addition to clinical and other descriptions about the ten

chosen slices are given in Table 2. In case of sensitivity and specificity, the

values greater than 98% shows that our method produces the acceptable results

and even comparable to manual segmentation. The predictive accuracy is 99% in

normal cases and lowered for abnormal slices. This algorithm was applied on

more than 100 slices including some normal and abnormal brain volumes. The

results are almost the same on normal slices (ref. slice number 1 in Table 2)

whereas in some abnormal slices the hand-drawn IHF is deviated from our mid-

sagittal line. This variation is also noticeable from the result of slice number 5

and 8 of Table 2 that are lower than other values. The visual effect of the slice 5

and 8 is given in column 3 and 6 of Fig. 10. The single mass effect in the right

side pushes the IHF towards the left side and hence the IHF has a curve effect and

deviated from the MSL.


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Fig. 10. Sample scans selected from MR T1 and T2 brain volumes are given in row 1, the

brain extraction results and separated hemispheres of respective scans are shown in

column wise in row 2, row 3 and row 4.

Our method worked successfully on several normal and abnormal 2D slices.

The success of our algorithm depends on two factors. The primary one is related

to the accuracy of BEA. If the BEA failed to extract the brain portion clearly,

especially with the clefts, then the proposed procedure failed. But this will

happen only for very low contrast images and images with severe artifacts. Both

of our BEAs are developed for 2-D and 3-D spaces and produces acceptable

results for all orientations. Our BEAs successfully removed all the non-brain

regions including the imaging clutters automatically as shown in column 3 and

column 6 of Fig.10. This is considered to be one of the important merits of our

BEA algorithms. The second factor is concerned with the search space for the

MSL edges. It is actually approximated near the middle vertical line at each slice.

If the head is tilted much during the scanning time then the cleft portions will be

far away from the middle vertical line and it will not be captured correctly. Hence

we have to either increase the search space or use some other scheme to select the

initial line other than middle vertical line. But the broad head tilting occurs only

for patients who are not fit for MRI screening.


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Table 2. Quantitative evaluation of ten sample slices made between hand-drawn portions

done by an expert and portions produced by the proposed method

Slice

�o.

Type

Size

Clinical

and other

Description

Region

Sensitivity

(S)

Specificity

(Sp)

Predictive

Accuracy

(PA)

1 t2

scan 256x256 Normal

Brain 0.9880 0.9976 99.47

LCH 0.9930 0.9957 99.53

RCH 0.9614 0.9998 99.38

2 t2

scan 256x256

Glioma –

grade IV

astrocytoma

Brain 0.9738 0.9885 98.37

LCH 0.9601 0.9940 98.80

RCH 0.9656 0.9955 99.02

3 t2

scan 256x256

Glioma –

anaplastic

astrocytoma

Brain 0.9906 0.9920 99.16

LCH 0.9843 0.9942 99.28

RCH 0.9770 0.9987 99.56

4 t2

scan 256x256 Sarcoma

Brain 0.9923 0.9913 99.17

LCH 0.9812 0.9948 99.23

RCH 0.9840 0.9974 99.47

5 t2

scan 256x256

Mass effect

and

background

clutter

Brain 0.9859 0.9916 98.96

LCH 0.9313 0.9947 98.39

RCH 0.9767 0.9885 98.63

6 t1

scan 256x256 Mass effect

Brain 0.9819 0.9998 99.53

LCH 0.9838 0.9992 99.72

RCH 0.9590 1 99.49

7 t1

scan 256x256

Glioma –

gadolium

contrasted

Brain 0.9915 0.9937 99.31

LCH 0.9903 0.9926 99.23

RCH 0.9496 0.9976 99.06

8 t1

scan 512x512

Mass effect

and

background

clutter

Brain 0.9906 0.9961 99.40

LCH 0.9317 0.9984 98.56

RCH 0.9937 0.9871 98.84

9 t1

scan 500x383 Mass effect

Brain 0.9892 0.9935 99.13

LCH 0.9834 0.9869 98.60

RCH 0.9588 0.9978 98.70

10 t1

scan 148x127

Mass effect

and

low

contrast

image

Brain 0.9903 0.9414 96.31

LCH 0.9887 0.9763 97.91

RCH 0.9713 0.9817 97.94


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This method could be extended to 3D brain volumes. The proposed method

produces the MSL with the help of edges of cleft presents with the 2D slices. The

clefts at each 2D slices are more visible with the upper portions of 3D volume.

Hence the MSLs produced by upper slices could be useful for producing MSP of

any 3D brain volume. We can obtain more accurate results for 3D volume by

comparing the results of BEA and edges of MSL between adjacent slices. In

some abnormal volume the IHF is deviated from MSP due to swelling of

hemisphere. To overcome such situations, MSL could be used as initial line for

energy minimization procedure like active contour methods, to trace the shape of

inter-hemispheral membrane. Studies are underway extending this 2D approach

to generate MSP and to extract the shape of IHF for 3D brain volumes.

Acknowledgments

The authors wish to thank Dr. K.G. Srinivasan M.D., R.D., Consultant

Radiologist, KGS Advanced MR & CT Scan, Madurai, Tamilnadu, India and Dr.

N. Karunakaran DMRD., DNB., Consultant – Radiodiagnosis and Dr. S. P.

Salome Pritha, Department of Radiology, Meenakshi Mission Hospital and

Research Centre, Madurai, Tamilnadu, India for providing the MR Head scans

and for obtaining the qualitative as well as quantitative validation. The authors

would also wish to thank Dr. K. Selvamuthukumaran M.Ch. (Neuro), Sr.

Consultant, Department of Neuro Surgery, Meenakshi Mission Hospital and

Research Centre, Madurai, Tamilnadu, India and

Dr.S.P.Balachandran,M.D.,D.M.,(Neuro), Neurologist, Dindigul Neuro Centre,

Dindigul District, Tamilnadu, India for their help in verifying the results.

This work is catalysed and funded by Science and Society Divisions,

Department of Science and Technology, Government of India, Grant number:

SP/YO/011/2007.


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