Improved Segmentation Technique to Detect Cardiac Infarction in
MRI C-SENC Images
Ahmad O. Algohary1, Ahmed M. El-Bialy2, Ahmed H. Kandil2 and Nael F. Osman3.

Abstract — Composite Strain Encoding (C-SENC) is a new
MRI technique for simultaneously acquiring cardiac functional
and viability images. It combines the use of Delayed
Enhancement (DE) imaging to identify the infracted (dead)
tissue inside the heart muscle and the ability to image
myocardial deformation from the Strain Encoding (SENC)
imaging technique. In this work, a new unsupervised multistage method is proposed to objectively identify infarcted heart
tissues in the functional and viability images provided by CSENC MRI. The proposed method is based on sequential
application of Bayes classifier, Otsu thresholding,
morphological opening, radial sweep boundary tracing and the
fuzzy C-means (FCM) clustering algorithm. This method is
tested on images of eleven patients with and without
myocardial infarction (MI) and on simulated heart images with
various levels of superimposed noise. The resulting clustered
images are compared with those marked up by expert
cardiologists who assisted in validating results coming from the
proposed method. Infarcted myocardium is correctly identified
using the proposed method with high levels of accuracy and
precision.
I. INTRODUCTION
T
HE accurate characterization of myocardial function and
viability following myocardial infarction (MI) is
important for therapeutical decision-making. Cardiac
functional images provide useful information about the
contractility patterns in the affected regions [1]. In contrast,
the viability images can be used to differentiate viable and
nonviable tissues [2].
By combining the functional and viability information,
three different myocardial tissue types can be identified: (i)
normally contracting tissue, which represents normal
myocardium; (ii) non-contracting yet viable tissue, which
represents “hibernating” myocardium; and (iii) nonviable
tissue, which represents infarcted myocardium. Notably, the
function of hibernating myocardium may improve after
revascularization, whereas that of infarcted myocardium
does not [3].
Previously, a method is proposed to identify different
heart tissues from MRI C-SENC images using an
unsupervised multi-stage fuzzy clustering technique. The
method was based on sequential application of the Fuzzy Cmeans (FCM) and iterative self-organizing data (ISODATA)
1
Ahmad Algohary is working as a Biomedical Software Engineer for
Diagnosoft Inc., Cairo International Office, Egypt.
E-mail: ahmad.algohary@diagnosoft.com.
2
Systems and Biomedical Engineering Dept., Faculty of Engineering, Cairo
University, Giza, Egypt.
3
Radiology Department, School of Medicine, Johns Hopkins University,
Baltimore, North Carolina, USA.
clustering algorithm [4]. In a more recent work [5],
Bayesian classifier was proposed to identify the background
region, then the filtered tissue regions were classified into
the different tissue types using FCM algorithm.
In this work, a new, unsupervised, multi-stage
segmentation method is proposed to objectively characterize
different heart tissues from tuned images provided by
Composite Strain Encoding (C-SENC) MR Images of shortaxis planes of the heart, and thereby identify infarcted
myocardial tissues. This method is based on the application
of Bayesian classifier, Otsu’s thresholding technique,
morphological opening, radial sweep boundary tracing and
the fuzzy C-means (FCM) clustering algorithm. Numerical
simulations, real MR images of patients and expert
cardiologists’ markings were used to validate the
segmentation technique, which showed excellent results with
respect to accuracy and precision.
II. THEORY
A. C-SENC
Recently, the Composite Strain Encoding (C-SENC) MRI
technique has been introduced for simultaneous cardiac
functional and viability imaging in a single short breathhold
[6]. No additional time, when compared with standard
Delayed Enhancement (DE) viability imaging, is required
for acquiring the additional functional images. This
technique results in three images: no-tuning (NT), lowtuning (LT), and high-tuning (HT). Bright regions in the NT,
LT, and HT images represent infarction (or blood), akinetic,
and contracting tissues, respectively. An anatomy (ANAT)
image of the heart can also be constructed by adding the LT
and HT images as described in [1] to show the anatomical
structure of the heart (both contracting and non-contracting
myocardium) with no signal from blood. Figure 1 shows an
example of acquired C-SENC images.
a)
b)
c)
Fig. 1. C-SENC Images of a patient suffering myocardial
infarction: a) NT, b) LT, c) HT
B. Bayes Classifier
Bayes classifier is based on the statistical model of classes
need to be classified when their probability density functions
are known [7]. In this work the Bayesian classifier is used to
differentiate between background and tissue signals in the
ANAT image. In order to account for the noise effect, a
probabilistic model is used to model the C-SENC signal
intensities. The well-known MRI signal model that uses
Rician and Rayleigh probability density functions to model
the tissue and the background signals, respectively, was used
[8]. Using the fact that the LT and HT images are acquired
independently, we can easily show that the joint density
function for their signal intensities (at the same pixel
location) can be written as follows [9],
fS1,S2(S1S2|tissue)= fS1(S1|tissue) . fS2 (S2|tissue)
=
S1 +S2
.e
p2.sinc(∂ω)2 + p2.sinc(1−∂ω)2
2σ2
σ4
p.sinc(∂ω).S1
.e
S12+S22
2σ2
.
p.sinc(1−∂ω).S2
I0(
) .I0(
)
(1)
σ2
σ2
fS1,S2(S1S2|background)=fS1(S1| background) . fS2 (S2| background)
fS1,S2(S1S2| background)=
S1 +S2
σ4
.e
S12 + S22
2σ2
(2)
where S1 and S2 are the signals acquired from the LT and
HT images, respectively; σ is the standard deviation of the
background region, ∂ω represents the contractility of the
heart tissue at the pixel location where S1 and S2 are
acquired, and I0 is the first kind zero order Bessel function.
A feature vector ν = [S1(x,y), S2(x,y)] is used to represent
the information available for each pixel in the ANAT image.
All feature vectors are then classified into two classes
(background and tissues) as follows. First, a Bayesian
discriminant function is built using the joint probability
functions in Eq. (1) and (2):
∂ν = log (fS1,S2(S1,S2|tissue))–log (fS1,S2(S1,S2|background))
(3)
So, the decision rule for the classification becomes:
bckground, ∂ν < 0
pixel(x, y) ∈ {
tissue
, ∂ν > 0
(4)
The Bayesian classifier identifies the tissue (non
background) [2] in the C-SENC images.
C. Fuzzy C-Means
Fuzzy C-means (FCM) is a data clustering technique
wherein each data point belongs to a cluster to some degree
that is specified by a membership grade. This technique
groups data points that populate some multidimensional
space into a specific number of different clusters. FCM
attempts to cluster feature vector by iteratively minimizing
an objective function. FCM generates for each data element
x in the feature space, a membership vector µx = [µ1x,
µ2x…µcx], that describes how strong it belongs to each
cluster. µkx denotes the similarity of pattern x to the cluster k
and is calculated as follows:
1
µik =
(5)
1
∑cq=1 (
dik
)
diq
images. Otsu’s method uses an exhaustive search to evaluate
the criterion for maximizing the between-class variance.
An image is a 2D grayscale intensity function, and
contains N pixels with gray levels from 1 to L. The number
of pixels with gray level i is denoted ni, giving a probability
of gray level i in an image of
p i = ni ⁄ N
(6)
In the case of bi-level thresholding of an image, the pixels
are divided into two classes, C1 with gray levels [1, …, t]
and C2 with gray levels [t+1, …, L]. Then, the gray level
probability distributions for the two classes are
C1 : p1 ⁄ω1 (t) , … pt ⁄ω1 (t)
C2 : pt+1 ⁄ω2 (t) , pt+2 ⁄ω2 (t)
where ω1 (t) = ∑ti=1 pi
and ω2 (t) = ∑Li=t+1 pi
, and
… pL ⁄ω2 (t),
(7)
(8)
Also, the means for classes C1 and C2 are
μ1 = ∑ti=1 i pi ⁄ω1 (t)
, and
μ2 = ∑Li=t+1 i pi ⁄ω2 (t)
(9)
(10)
Let µT be the mean intensity for the whole image. It is easy
to show that
ω1 μ1 + ω2 μ2 = μT
μ1 + μ2 = 1
(11)
(12)
Otsu defined the between-class variance [11] of the
thresholded image as
2
σB 2 = ω1 (μ1 − μT ) + ω2 (μ2 − μT )
2
(13)
For bi-level thresholding, Otsu verified that the optimal
threshold t* is chosen so that the between-class variance σB 2
is maximized; that is,
t ∗ = Arg Max{σB } where
1≤t<𝐿
(14)
III. MATERIALS AND METHODS
A. Simulated C-SENC Images
Simulated C-SENC MR images were created in
MATLAB R2009B (MathWorks, Inc.) to test the proposed
clustering technique. The simulated images were designed to
represent short-axis with infarcts of different sizes. In order
to prove the robustness of the proposed method, different
levels of white Gaussian noise (0 – 50 %) were added to the
images. And the smoothing filter was chosen to be
Butterworth filter of first order. The clustering technique
was tested on the simulated images. Fig.2 shows the
resulting simulated images.
(β−1)
where β is called the exponent, diq can be calculated by any
distance measure. Here, we use the Euclidean distance [10].
D. Otsu Thresholding
Thresholding is a well-known technique for image
segmentation that tries to identify and extract a target from
its background on the basis of the distribution of gray levels
in image objects. Otsu’s method chooses the optimal
thresholds by maximizing the between-class variance with
an exhaustive search [11].
Reference [12] concluded that Otsu’s method was one of
the best threshold selection methods for general real world
NT
LT
HT
Infarction Map
Fig. 2. Simulated C-SENC images and the resulting infarction map.
Intensity gray levels are predefined by the user. Bright regions in the NT,
LT, and HT images represent: infarction (or blood), akinetic, and
contracting tissues, respectively. Infarction size is 2.94 cm2.
The following table shows the controls and input ranges
for the simulator. All mappings between pixels and
millimeters were made by assuming mm/pixel value of 1.28.
TABLE I
INPUT CONTROLS RANGES OF THE SIMULATOR
Type
Image Dimensions
ROI
Depth
Inner Radius (Endocardium)
Outer Radius (Epicardium)
in pixels
256 x 256
64 x 64
0 - 10
20
30
in mm
327.68 x 327.68
81.92 x 81.92
0 – 12.8
25.6
38.4
intensities
of different tissues in the No-Tuning (NT), Anatomy
B. Signal
Clustering
Technique
(ANAT), and High-Tuning (HT) C-SENC images. High = signal intensities
greater than 0.8 inDetecting
normalized images; and Low = signal intensities less
Myocardium
than 0.2 in normalized
images.
Masking
Simple
Addition
Stage 1
Stage 2
Stage 3
Fig. 3. Classification and Clustering Procedure
Figure 3 shows a schematic diagram of the proposed
clustering technique, which consists of three main stages.
The first stage of the proposed technique aims to detect and
extract the left ventricle. This is done by the successive
application of four sub-steps: Denoising, thresholding,
morphological opening and boundary detection.
TABLE II
TISSUE TYPES OCCUPY DIFFERENT AND THEIR CHARACTERISTIC SIGNAL
INTENSITIES IN THE USED IMAGES
Type
Blood
Infarcted
Functional
Background
ANAT
Low
High
High
Low
NT
High
High
Medium
Low
LT
Low
High
low
Low
HT
Low
Low
High
Low
Signal intensities of different tissues in the No-Tuning (NT), Anatomy
(ANAT), and High-Tuning (HT) C-SENC images. High = signal intensities
greater than 0.8 in normalized images; and Low = signal intensities less
than 0.2 in normalized images.
Denoising is performed on LT and HT images using
Bayes Classifier to get denoised ANAT images with
improved
contrast-to-noise
ratio
(CNR)
between
myocardium and background. Then, automatic thresholding
is applied using Otsu’s thresholding. Morphological opening
is applied on the thresholded image. This aims to remove
more noise pixels and to improve the myocardium
boundaries so that they can be traced successfully in the next
step. It removes holes with areas less than 50 pixels. The
value 50 was chosen to be less than the minimum detectable
infarction size that was defined experimentally. This step is
applied for two successive times; the first time removes the
small holes in the tissue while the second time does the same
for the small holes in the background region.
Finally, boundary tracing is applied to select the
myocardium borders. This is done by using the radial sweep
technique with 8-connectivity [13], [14]. All regions are
traced, and then the most centralized region is selected to be
the myocardium as shown in figure 4.
a)
b)
In the second stage, anatomy (ANAT) image of the heart
is constructed by adding the LT and HT images as described
in [1]. Then, NT and ANAT Images are masked by
myocardium boundaries to exclude all pixels outside the
myocardium. This restricts the clustering to only two pixel
types; functional and infarcted tissue. This is applied by
ANDING the binary image that contains the myocardium
only with the original ANAT and NT images, respectively.
This results in two images (ANAT and NT) that contain only
pixels corresponding to the myocardium tissue.
The third stage of the proposed technique consists of
further clustering the myocardium through the application of
Fuzzy C-Means algorithm. In this stage, FCM is applied to
divide the myocardium into two major clusters: contracting
and non-contracting tissue, from which we can extract the
infarcted regions.
Each data point is represented by a 2D vector in the NTANAT normalized intensity space. This intensity vector is
the input to the clustering technique, which assigns to each
pixel some degree of membership between zero and one.
The infarcts are extracted from the clustered images by
assigning the pixels with membership value > 0.5 to the
infarct cluster.
C. Validation
A consultant cardiologist helped in the validation of the
proposed technique by marking the infarcted tissue regions
in the real C-SENC MRI images. Infarcted regions were
marked three times for each set. Infarctions extracted from
the clustered images were compared with those extracted
from the marked images on a pixel-by-pixel basis and
statistical results were calculated.
IV. RESULTS
A. Simulated Images Results
Proposed technique was applied to the simulated C-SENC
images shown in figure 2. White Gaussian noise with
various levels (0 – 50 %) was added. Different infarction
sizes were assigned with various levels of added noise to get
a total of 300 cases. Each case consisted of three images,
NT, LT and HT with predefined grayscale levels. Each case
was created and tested separately for 100 times. Then
average results were taken. Accuracy and precision of the
identified infarction were measured versus each noise level
as shown in figure 5.
c)
Fig. 4. a) Morphologically opened image, b) image with
boundary traced regions, c) Image showing the most centralized
region that represents the left ventricle
Fig. 5. Accuracy and precision corresponding to different Noise levels for
detected Infarctions.
It was observed that infarcted regions can be extremely
small for an automated system to detect. So, we aimed not
only to automate and improve the infarction detection level,
but also to specify the minimum size of infarction that can
be detected given some statistics of the images and their
qualities. We selected a value of 85% to be our threshold.
Any detection of the infarcted tissue with accuracy and
precision equal to or greater than this value was considered
as successful detection. Accordingly, the minimum size of
infarction that was detected by the proposed technique
according to this criterion was found to range from 76 to 177
pixels (124.52 mm2 – 290 mm2) at noise levels up to 30%.
exceeds 30 % of the maximum intensity in simulated
images, the technique is not able to detect the infarction with
accuracy more than or even equal to 85%.
Tissue of the myocardium detected as infarcted tissue
indeed includes two clusters; infarct and hibernated (tissue
that is not-contracting while still viable). It is clear that we
are able to differentiate between contracting and noncontracting regions in the heart tissue, but still cannot
differentiate between the hibernating and the infracted tissue
as both of them are non-contracting tissues. Further
clustering may be applied to differentiate between these two
regions.
In conclusion, a new technique is proposed for identifying
different heart tissues from C-SENC functional and viability
images. The technique is based on the consecutive
application of the Otsu’s thresholding and FCM techniques.
The technique is successfully applied to short-axis images of
the heart as well as to simulated C-SENC images. It gives
very good results even if the images are suffering from high
noise levels.
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Fig. 6. Minimum Detectable Infarction size vs Noise Level.
[1]
B. Real MR Patient Results
[2]
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NT
LT
HT
Infarction Map
Figure 7. Representative C-SENC MRI images from a patient with
myocardial infarction. The resulting infarcted regions are colored in red.
The first row shows the images and the infarction map for the proposed
technique. The second row shows the markings of the consultant
cardiologist who marked the infarcted regions in the C-SENC Images.
Figure 7 shows real short-axis C-SENC images and the
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V. DISCUSSION AND CONCLUSION
The proposed technique was found to be robust in
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analyzed images, the technique correctly determines
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the black-blood property in the LT and HT images. Thus, the
resulting clustered images would allow for accurate
measurement of the infarct size. But when the noise level
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