The document discusses the challenges and methodologies of brain tissue segmentation from MRI images, highlighting the need for automated processes due to the complexity of brain structures. It reviews various imaging modalities and their applications, and details the segmentation pipeline, including skull stripping, intensity inhomogeneity correction, and the evaluation of segmentation accuracy. Key techniques mentioned include binary morphology, deformable models, and expectation maximization for classification of different tissue types.

2. Contents
Brain tissue segmentation from MR images
1. Introduction
2. Magnetic Resonance Imaging
3. Brain tissue segmentation

3. Introduction
Manual segmentation of brain tissue is both challenging and time-consuming
due to of the large number of MRI slices for each patient which composes the
three-dimensional information and also due to intra/inter-observer variability
of manually segmented scans. Thus developing a robust automated brain tissue
segmentation method is an active research field.
However, automated segmentation of brain tissue is still a challenging
problem due to the complexity of the images, differences in tissue intensities,
noise, intensity non-uniformities, partial volume effects or absence of models
of the anatomy that fully capture the possible deformations in each structure.
Brain tissue segmentation from MR images
 Need for automatic segmentation

4.  Why to detect brain tissues?
Brain tissue is a particularly complex structure, and its segmentation is an
important step for tasks such as
Cortical labeling
Change detection
Visualization in surgical planning.
Introduction
Brain tissue segmentation from MR images

5.  Brain tissues
Introduction
Central nervous system (CNS) is the part of the human nervous network
which integrates the coordination and processing of receiving neural
information. CNS is contained by the
brain and the spinal cord and
constituted by two tissue
components: gray matter tissue
(GM), which is the main CNS
element and consists in neuronal
cell bodies; and white matter
tissue (WM), which is the second
CNS component and it is mainly
composed of militated axon
tracts.
Brain tissue segmentation from MR images

6. Introduction
 Modalities of Medical Image Acquisition
 MRI (Magnetic Resonance Imaging)
 PET (Positron Emission Tomography)
 SPECT (Single Photon Emission Computed Tomography)
 MRS (Magnetic Resonance Spectroscopy)
 Ultrasound
 X-ray imaging
 CT (Computed Tomography)
Brain tissue segmentation from MR images

7. Introduction
The modalities have different strengths and thus are used under different circumstances.
• CT captures bone with accuracy, and is used for dosage planning in radiation therapy.
• PET, SPECT, and MRS are typically used to provide functional information with MRI
also gaining usage in that domain.
• MRI produces high contrast between soft tissues, and is therefore useful for detecting
lesions in the brain.
MRI scan
CT scan
Brain tissue segmentation from MR images

8. Magnetic Resonance Imaging
 How MRI works?
 Patient is bathed in a magnetic field 5000 times stronger than the earths.
This field causes some of the body’s nuclei to behave like tiny compasses and line
up
 Then the nuclei are hit by pulsing radio
 Once the pulses stop the nuclei go back to their state induced by the magnet
 The energy now released by the nuclei acts like miniature radio stations giving out a
signal
 These radio waves are picked up by a computer where they are translated into an
image.
Brain tissue segmentation from MR images

9. Magnetic Resonance Imaging
Nuclear
Magnetic
Resonance in
MRI
Brain tissue segmentation from MR images

10.  Construction
Magnetic Resonance Imaging
Brain tissue segmentation from MR images

11. MRI is based on the magnetization properties of atomic nuclei.
A powerful, uniform, external magnetic field is employed to align the protons that are
normally randomly oriented within the water nuclei of the tissue being examined.
This alignment (or magnetization) is next disrupted by introduction of an external Radio
Frequency (RF) energy. The nuclei return to their resting alignment through various
relaxation processes and in so doing emit RF energy.
After a certain period following the initial RF, the emitted signals are measured. Fourier
transformation is used to convert the frequency information contained in the signal from
each location in the imaged plane to corresponding intensity levels, which are then displayed
as shades of gray in a matrix arrangement of pixels. By varying the sequence of RF pulses
applied & collected, different types of images are created.
Repetition Time (TR) is the amount of time between successive pulse sequences
applied to the same slice.
Time to Echo (TE) is the time between the delivery of the RF pulse and the receipt of
the echo signal.
Magnetic Resonance Imaging
Brain tissue segmentation from MR images

12. Tissue can be characterized by two different relaxation times – T1 and T2.
T1 (longitudinal relaxation time) is the time constant which determines the rate at
which excited protons return to equilibrium. It is a measure of the time taken for
spinning protons to realign with the external magnetic field.
 T2 (transverse relaxation time) is the time constant which determines the rate at
which excited protons reach equilibrium or go out of phase with each other. It is a
measure of the time taken for spinning protons to lose phase coherence among the
nuclei spinning perpendicular to the main field.
Magnetic Resonance Imaging
Brain tissue segmentation from MR images

13. Magnetic Resonance Imaging
 T1 and T2 weighted imaging
T1- and T2-weighted images can be easily differentiated by looking the CSF.
CSF is dark on T1-weighted imaging and bright on T2-weighted imaging
In T1, CSF tissue has the darkest
intensities while WM has the
brightest. On the contrary, in T2
CSF has the brightest intensities
while WM is the darkest. On
both sequences, GM has an
intermediate gray level.
Brain tissue segmentation from MR images

14. Magnetic Resonance Imaging
T1-weighted images are produced by using short TE and TR times. The contrast and
brightness of the image are predominately determined by T1 properties of tissue.
T2-weighted images are produced by using longer TE and TR times.
Fluid Attenuated Inversion Recovery (Flair)
The Flair sequence is similar to a T2-weighted image except
that the TE and TR times are very long. By doing so,
abnormalities remain bright but normal CSF fluid is attenuated
and made dark. This sequence is very sensitive to pathology
and makes the differentiation between CSF and an abnormality
much easier. In FLAIR , WM and GM have an intermediate
grey level and lesions seems brighter.
Brain tissue segmentation from MR images

15. Magnetic Resonance Imaging
Most common MRI Sequences and their Approximate TR and TE times.
 Approximate TR and TE time
Brain tissue segmentation from MR images

16. Brain tissue segmentation
Pipeline structure for brain tissue segmentation
Brain tissue segmentation from MR images

17. Brain tissue segmentation
Skull stripping
BET brain extraction tool
The brain surface extractor (BSE)
statistical parametric mapping (SPM2)
The above mentioned algorithms are all MATLAB based tools and use T1
weighted images as inputs to extract skull and remove it from the MR image
• BET (Brain Extraction Tool) deletes non-brain tissue from an image of the whole
head. It can also estimate the inner and outer skull surfaces, and outer scalp surface,
if you have good quality T1 and T2 input images.
Brain tissue segmentation from MR images

18. Automated brain segmentation pipelines usually incorporate a preprocessing step by
which image inhomogeneities are removed.
Sources of inhomogeneities have been studied extensively. The artifacts causes have been
divided into two main groups by classifying them as inherent to the same MRI device or
provoked by the same scanned object.
Main causes in first group are especially derived from radio frequency (RF) transmissions
and receptions but also differences in the magnetic field or eddy currents driven by field
gradients.
Cause derivations in the second group are related to the imaged object itself (position,
shape, and orientation of the object inside the magnet) or dielectric properties of the object.
Brain tissue segmentation
Intensity inhomogeneity
Brain tissue segmentation from MR images

19. Brain tissue segmentation
 Partial Volume effects
Automatic brain tissue segmentation algorithms
classify the voxels into their possible classes (CSF,
GM and WM). However, one of the most important
problems are the classification of voxels where more
than one tissue is present. This phenomenon is
referred to as partial volume effects (PVE).
PVE blur the intensity distinction between tissue
classes at their border.
For example, a T1 image voxel containing a boundary between CSF and WM can be
misclassified as GM because of the increase in the blur. To solve this we use Partial
volume correction (PVC) method.
Brain tissue segmentation from MR images

20. Brain tissue segmentation
 How to evaluate accuracy segmentation?
 Quantitative evaluations are commonly based on the comparison between the
segmentation results and a manually expert labeled volume or ground truth.
 Usually, intra-inter observer variability is avoided by the utilization of labeled volumes
from more than one expert. Still, this is not a sufficient condition and it is difficult to find a
consensus among experts.
 Thus we use an algorithm to find simultaneous truth and performance level
estimation.(STAPLE).
The method take a collection of segments of an image, and compute simultaneously a
probabilistic estimate of the true segmentation and a measure of the performance level
represented by each segmentation.
Brain tissue segmentation from MR images

21. Brain tissue segmentation
Generally we report evaluation based on statistical analysis measures derived from
classification rates with respect to the ground truth such as true positive (TPR), true negative
(TNR), false positive (FPR) and false negative (FNR) rates. In a single tissue classification,
these rates are defined as:
 TPR is the percentage of voxels classified as tissue by the method that are labeled as
tissue by the expert.
 TNR is the percentage of voxels classified as non-tissue by the method that are labeled
as non-tissue by the expert.
 FPR is the percentage of voxels classified as tissue by the method that are labeled as
non-tissue by the expert.
 FNR is the percentage of voxels classified as non-tissue by the method that are labeled
as tissue by the expert.
Brain tissue segmentation from MR images

22. Sensitivity or true positive fraction (TPF) is the classifier ability to correctly identify
tissue voxels . It can be defined as:
 Similarly, specificity is defined as the classifier ability to identify non-tissue:
The accuracy of the classifier is usually computed as the rate of correct predicted voxels
over all predicted voxels. Hence
Brain tissue segmentation
Brain tissue segmentation from MR images

23. Brain tissue segmentation
 Conversely, the error rate of the classifier is given by the misclassified voxels over all
predicted voxels as:
 Furthermore, similarity indexes can be used to compute the accuracy of the method.
Dice coefficient is defined as the set agreement between classification and ground truth :
 Analogously, the Jaccard similarity index, measures the overlap between the segmentation
results and the ground truth as:
Brain tissue segmentation from MR images

24. Brain tissue segmentation
 Other measures based on intensity, distance or connectivity can be are
As we know, Dice coefficient is the most broadly used measure to quantitatively evaluate
the accuracy of brain tissue segmentation. The Fractional Brain Tissue of a given class
returns the normalized fraction of the given tissue in the brain.
It is defined as the amount of voxels which are classified as the given class divided by all
brain voxels. Hence:
Brain tissue segmentation from MR images

25. We measure the atrophy in MS lesion tissues using the Brain parenchyma
factor coefficient which is defined as the number of GM,WM voxels and tissue
lesion voxels L divided by the all the brain voxels. A decrease in BPF over time
might give early diagnostic clues about the onset of MS disease:
Brain tissue segmentation
 Diagnosis of MS Lesion
 What is Multiple Sclerosis?
Brain tissue segmentation from MR images

26. Brain tissue segmentation
Image morphology provides a way to incorporate neighborhood and distance information.
The basic idea in morphology is to convolve an image with a given mask and to binarize
the result of the convolution using a given function. Choice of convolution mask and
binarization function depend on the particular morphological operator being used.
Binary morphology has been used in several segmentation systems, their functional
description is given on next slide.
 Mathematical Morphology
Brain tissue segmentation from MR images

27. Brain tissue segmentation
 Mathematical Morphology
Erosion: An erosion operation on an image I containing labels 0 and 1, with a structuring
element S, changes the value of pixel i in I from 1 to 0, if the result of convolving S with I,
centered at i, is less than some predetermined value
Dilation: Dual to erosion, a dilation operation on an image I containing labels 0 and 1, with a
structuring element S, changes the value of pixel i in I from 0 to 1, if the result of convolving S
with I, centered at i, is more than some predetermined value.
Brain tissue segmentation from MR images

28. Brain tissue segmentation
 Deformable Models
Deformable Models are used for object recognition.
There are two type of deformable models – Parametric deformable model and
Geometric deformable model
Snakes and Balloons are the two deformable models we will need for brain tissue
segmentation.
Snakes model is a 2 D model which maps the curves.
Balloons model is a 3 D model which is implicit.
Brain tissue segmentation from MR images

29. Brain tissue segmentation
Brain tissue segmentation from MR images

30. Brain tissue segmentation
Procedure of Segmenting Brain tissue
1. EM segmentation for correction of gain due to RF coil inhomogeneities in the data
2. binary morphology and connectivity to incorporate topological information, and
3. active contours to add spatial knowledge to the segmentation process.
Brain tissue segmentation from MR images

31. Brain tissue segmentation
 EM segmentation
• Expectation Maximization Segmentation (EM) is a Iterative method consisting of
two steps:
Expectation Step: Given the current bias field , E step computes likelihood of each
tissue class.
Maximization Step: Given the likelihood of each tissue class, the M step estimates
the image homogeneities
• EM segmentation does not incorporate any spatial information for the classification
of voxels.
Brain tissue segmentation from MR images

32. N3 is nonparametric non-uniform intensity normalization algorithm used for bias field
correction.
 Bias field signal is a low-frequency and very smooth signal that corrupts MRI images
specially those produced by old MRI machines, which makes it difficult for image
segmentation algorithms to produce satisfactory results.
 What is Bias field?
Input scan BET Output N3 output Bias field
Brain tissue segmentation
Brain tissue segmentation from MR images

33. Using the model described above, we divide the segmentation of the brain
into three steps.
The first step is to remove the gain artifact from the data and to classify the voxels
into four classes: white matter, grey matter, CSF, and skin (or fat), purely on the basis
of their signal intensities.
Due to natural overlap between intensity distributions of the various structures,
misclassifications are likely at this stage.
 In particular, muscle is likely to be classified as gray matter, fat classified as white
matter, and nerve fibers classified as white or gray matter.
Brain tissue segmentation
Brain tissue segmentation from MR images

34. The Second step aims to reduce some of the misclassification by using neighborhood
and connectivity information. It uses morphological operators to "shave off" the nerve
fibers and muscles connecting the brain tissue to the cranium, and then uses
connectivity to find the largest connected component of white and grey matter in the
image.
The strategy is that misclassified fat, muscle, and nerve fibers will get cut off from the
central largest component, which is the brain tissue.
Due to the variation in the size of the connectors from the brain tissue to the cranium,
often the brain tissue is not isolated at the end of this step, which is when we use the
third step.
Brain tissue segmentation
Brain tissue segmentation from MR images

35. The third step uses expert input to annihilate connections between the brain and
spurious structures in a few carefully chosen slices of the data, and then employs
region based deformable contours to propagate the manually drawn contours to the
rest of the volume.
Brain tissue segmentation
Brain tissue segmentation from MR images
Input
Image
Remove
Gain
artifact
Using Morphological
operators
Using
deformable
contours
Output
Image

36. 1.
2. 3.
4. 5. 6.
Step 1 – Iterations of EM segmenter
Brain tissue segmentation
Brain tissue segmentation from MR images

37. Brain tissue segmentation
Step 2 – Using different morphological operators
Brain tissue segmentation from MR images

38. Step 2 – Iterations using balloon deformable model
Brain tissue segmentation
Brain tissue segmentation from MR images

39. Brain tissue segmentation
Implementation of the EM segmentation, morphological operations, and connectivity
is done on IBM POWER Visualization Server (PVS).
Time required for each step is given below.
Brain tissue segmentation from MR images

40. Thank You for your time !
All the questions are welcome and appreciated !
Brain tissue segmentation from MR images

41. S.M. Smith. Fast robust automated brain extraction. Human Brain Mapping,
17(3):143-155, November 2002.
Segmentation of brain tissue from magnetic resonance images by Tina Kapur,
Massachusetts Institute of Technology, February1995
MRI brain tissue segmentation by Sergi Valverde, university of Girona ,2012
http://www.brainvoyager.com/bvqx/doc/UsersGuide/Segmentation/IntensityInhomog
eneityCorrection.html
Brain tissue segmentation from MR images
References