Real-time Anterior Mitral Leaﬂet Tracking using Morphological

Operators and Active Contours

Malik Saad Sultan

1,2

, Nelson Martins

1,2,3

, Eva Costa

, Diana Veiga

, Manuel Jo

ao Ferreira

3,4

Sandra Mattos

and Miguel Tavares Coimbra

1,2

Faculdade de Ci

encias, Universidade do Porto, Porto, Portugal

Instituto de Telecomunicac¸

oes, Porto, Portugal

Enermeter, Sistemas de Medic¸

ao, Lda, Braga, Portugal

Centro Algoritmi, University of Minho, Guimar

aes, Portugal

ırculo do Corac¸

ao de Pernambuco, Recife PE, Brazil

Keywords:

Ultrasound Images, Medical Image Processing, Active Contours, Segmentation and Tracking, Mitral Valve.

Abstract:

The mitral valve plays a vital role in our circulatory system. To study its functionality, it is important to

measure clinically relevant parameters, such as its thickness, mobility and shape. Since manual segmentation

is impractical, time consuming and requires expert knowledge, an automatic segmentation tool can have a

signiﬁcant clinical impact, providing objective measures to clinicians for understanding the morphology and

behaviour of the mitral valve. In this work, a real time tracking method has been proposed for ultrasound

videos obtained with the Parasternal Long Axis view. The algorithm is semi-automatic, assumes manual

Anterior Mitral Leaﬂet segmentation in the ﬁrst frame and then it uses mathematical morphology algorithms

to obtain tracking results, further reﬁned by localized active contours during the whole cardiac cycle. Finally,

the medial axis is extracted for a quantitative analysis. Results show that the algorithm can segment 1137

frames extracted from 9 fully annotated sequences of the real clinical video data in only 0.89 sec/frame, with

an average error of 5 pixels. Furthermore, the algorithms exhibited robust tracking performance in the most

difﬁcult situations, which are large frame-to-frame displacements.

1 INTRODUCTION

1.1 Motivation

Mitral valve diseases are widespread and are com-

monly affected by Rheumatic Heart Disease (RHD)

(Bisno, 2004). RHD is an autoimmune disease that

usually begins in childhood that results from repeated

episodes of acute rheumatic fever, which slowly dam-

ages the heart valves. Following one of the most

relevant published studies (W HO and W HF, 2011,

2012), about 15.6 million people are affected glob-

ally from RHD, and require medical follow-up, being

responsible for 233,000 deaths per year. Earlier de-

tection is considered vital to control disease progres-

sion and to estimate disease burden in low-resource

regions of the world (Bisno, 2004). The RHD thick-

ens the Anterior Mitral Leaﬂet (AML) that results into

stenosis, regurgitation, change the shape of the leaﬂet

and shows abnormal motion patterns. Quantifying the

degree of abnormal change (morphological features)

will help to identify early cases with RHD. Heart

valve diseases create a massive economic burden on

health authorities. The average surgery cost to treat

mitral regurgitation was 24.871 ± 13.940 dollars per

patient in Europe (Trochu, Ribeiro and Ceber, 2015,

2012, 2014). The heart valve treatments and opera-

tions are not only expensive, but also a highly risky

cardiac process (Mirabel, 2007).

Echocardiography is a non-invasive, non-ionizing

and comparatively low cost imaging modality that is

capable of analysing fast moving valve structures in

real time. It is available as portable tool and thus it

is considered an appropriate choice for the diagno-

sis of heart diseases, especially in low-resource ar-

eas (Rem

enyi, 2012). The Parasternal Long Axis

view is the most suitable view to access the mitral

valve and its structures (Figure 1). It provides the

means to measure the clinically relevant parameters

such as, thickness, mobility and valvular anatomy

(Omran, 2010). Manual segmentation of these videos

Sultan M., Martins N., Costa E., Veiga D., Ferreira M., Mattos S. and Coimbra M.

Real-time Anterior Mitral Leaﬂet Tracking using Morphological Operators and Active Contours.

DOI: 10.5220/0006244700390046

In Proceedings of the 10th International Joint Conference on Biomedical Engineering Systems and Technologies (BIOSTEC 2017), pages 39-46

ISBN: 978-989-758-215-8

Figure 1: Parasternal long axis view, (A): showing Mitral

valve (MV), Anterior Mitral Leaﬂet (AML), Posterior Mi-

tral Leaﬂet (PML) and other structures. (B): Shows the MV

in Diastole/Systole phase, the thickened and hockey shape

leaﬂet.

is undesirable, given its impracticality, subjectivity

and expert knowledge required. Automatic and semi-

automatics methods to identify and track mitral valve

structures can improve the diagnostic process, pro-

viding quick and objective measurements of clinically

relevant parameters, even without any expert cardiol-

ogy knowledge.

1.2 State-of-the-art in Mitral Valve

Segmentation

Deformable models such as active contours were ex-

tensively used by the research community in med-

ical image segmentation and tracking. The reason

to adopt this kind of approach is their robustness

against image noise and shape fragmentation, abil-

ity to track non-rigid motion and its capability to in-

corporate geometric constraints, such as the expected

shape (Sheng, 2008). (Mikic, 1998) have proposed

the use of active contours with optical ﬂow to seg-

ment and track the AML in echocardiography. The

algorithm fails in large frame to frame displacements,

requiring user initialization in the ﬁrst frame. Also,

the algorithm was found computationally expensive

(20 min to compute a single cardiac cycle). (Mar-

tin, 2006) have used transformation ﬁtting with two

connected active contours, optimized using dynamic

programming. The algorithm requires extensive ini-

tialization and several parameters need to be tuned.

Moreover, it failed in high displacements (>10 pix-

els) and requires a mean processing time of about 1.8

seconds with a restricted number of iterations (10)

to process a single frame. (Zhou, 2012) proposed

an algorithm for mitral leaﬂet detection and track-

ing based on outlier detection in a low-rank matrix

and was tested on 2D and 3D ultrasound. The algo-

rithm was automatic and unsupervised (no initializa-

tion is required). However, the user needs to crop the

original sequence, requires parameter adjustment, is

very sensitive to rank and is computationally expen-

sive. Literature review demands a real time segmen-

tation and tracking algorithm with less user interac-

tion and the ability to efﬁciently track the mitral valve

when faced with large frame to frame displacements

(Sheng, Mikic, Martin and Zhou, 2008, 1998, 2006,

2012).

Mathematical morphology is widely used in im-

age processing for analysis of shapes, geometrical

and topological structures. (Yun-gang, 2015) used

morphological operations to roughly segment the left

ventricle followed by a snakes active contours. Mor-

phological features were efﬁciently used for the fast

segmentation of ischemic viable, ischemic nonviable,

and normal myocardium in echocardiographic images

(Lascu, 2008).

1.3 Objective and Contributions

The objective of this work is to obtain robust and real-

time tracking of the AML in ultrasound videos.

Our key contribution in this work is the novel use

of combined morphological operators and active con-

tours to address robust AML tracking in frames with

large displacement.

The remainder of the paper is organized as fol-

lows. Section II provides the methodology adopted

in this paper. In section III we report the results that

demonstrate the accuracy of the proposed algorithm

and ﬁnally section IV concludes the paper with a dis-

cussion on the problem, our contribution to it and the

future work.

2 METHODOLOGY

In this work, the echocardiography video is split into

frames and we assume perfect (manual) segmentation

in the very ﬁrst frame. The two successive frames are

iteratively selected for the analysis. The thin regions

of the successive images are extracted, followed by

extracting the regions with large displacement. These

regions are then merged with the segmentation re-

sult of the preceding frame and ﬁltered, in the can-

BIOIMAGING 2017 - 4th International Conference on Bioimaging

didate region part. Regions are then classiﬁed based

on their shapes and geometrical properties. The re-

sults are ﬁnally reﬁned using localized active con-

tours. Skeletonization and AML analysis is used as

a post-processing step. A summary of this processing

pipeline is depicted in Figure 2, and each step will

now be explained in detail.

Figure 2: AML tracking pipeline.

2.1 Thin Region Extractor

In this stage, two consecutive frames were extracted

iteratively until the whole cardiac cycle was covered.

For the resolution of the videos used in this papers

experiments the maximum recorded thickness of the

AML was 24 pixels. Following this, all structures with

width less than 24 pixels of thickness are extracted as

potential regions.

The AML region (Figure 3C) is extracted by tak-

ing the difference between the grayscale input image

(Figure 3A) and the grayscale opened image (Figure

3B) with the ﬂat disk shape structuring element of 24

pixel diameter.

Figure 3: A) Grayscale image B) Morphological opening

C) Top-hat transform.

2.2 Displaced Region

Based on the analysis of the PLAX videos, the thin

AML region shows a very large displacement in suc-

cessive frames compare to other regions in an image.

The regions of septum, inferior wall (Figure 1) do not

show signiﬁcant displacement in successive frames

and thus overlapped. This prior information is signif-

icant to overcome the problem of tracking in frames

with large AML displacement.

The focus of this module is to extract region that

showed large displacement from frame t-1 to frame

t (Equation 1). That can simply be achieved by tak-

ing the difference of successive frames followed by

selecting only the positive intensity values (Figure 4).

Hard threshold is then applied to get the binary image.

Disp

gray

= [I

(x,y) − I

t−1

(x,y)] Disp

gray

< 0 (1)

Figure 4: Regions with high displacement at four different

times (frames).

2.3 Candidate Image

The segmented region obtained at the time t-1 is ﬁl-

tered to remove the regions which belong to the blood

pool (black region) in frame at time t. Filtered region

is then summed up with the results of the displaced

region module. Small discontinuities (with a distance

of 2 pixels or less) were merged by a morphological

closing using a disk shape structuring element with a

radius of 2. The obtained results are shown in Figure

Figure 5: Candidate image for ﬁnal AML classiﬁcation.

2.4 Region Classiﬁcation

The regions extracted from the candidate image were

classiﬁed based on the morphological features, to ex-

tract the region that is most probably the AML. The

basic morphological and geometrical features such as

centroid, area, major and minor axis lengths were

Real-time Anterior Mitral Leaﬂet Tracking using Morphological Operators and Active Contours

used. These features are capable of providing signiﬁ-

cant structural and locality information.

These basic morphological features do not typi-

cally change signiﬁcantly in successive frames. In

ideal conditions, these features should be constant

throughout the cardiac cycle. The features obtained

from the manual segmentation in the ﬁrst frame is

used as a reference for the upcoming frame. After

processing each frame, the reference features are au-

tomatically updated with the average, by using the

feedback channel (Figure 6).

Figure 6: Classiﬁcation scheme.

A relative error matrix is created that contain four

vectors: centroid distance error, area error and ma-

jor/minor axis length error. For evaluation purposes,

the region with the minimum overall error is classiﬁed

as a true positive region (AML region) with a good

conﬁdence and all the other regions were classiﬁed as

false positive regions.

2.5 Reﬁning using Active Contours

2.5.1 Automatic Initialization

The segmentation result of the AML was obtained

through the morphological operators and was used as

a base to initialize the active contour framework. The

contour points of the initial curve are very close to

the real boundaries of the AML. Therefore, analyz-

ing local regions can provide robust and well deﬁned

boundaries, with a few iterations.

2.5.2 Localized Active Contour

Ultrasound images are very noisy and frequently con-

tain heterogeneous regions, and as such neither edge

based contours, nor region based contours are a suit-

able choice. A localized region-based active contour

framework was used in this work to reﬁne the initial

contour (Lankon, 2008). This hybrid region-based

curve evolution is robust to noise and doesn’t rely on

the global conﬁguration of the image.

The algorithm is based on the analysis of the lo-

cal circular regions with ﬁve pixels radius, at each

point on the curve. At each point the algorithm lo-

cally identiﬁes the background and foreground opti-

mally by their mean intensities. The formulation of

the local energy function along the curve is deﬁned

as:

∂φ

∂t

(x) = δφ (x)

Ω

B(x,y) δφ(y)



(I (y) − υ

)

− (I (y) − ν

)



+λδφ(x)div



Oφ(x)



(2)

Here, is the Dirac function, B(x,y) represents a region

that locally deﬁnes the interior and the exterior of the

region at point x and the radius of the local region is

speciﬁed by the user. The uniform modelling energy

is used as an internal energy (Chan, 1999). The lo-

calized version of the internal energy is deﬁned as the

local interior and exterior regions at every point on

the curve. (υ

,ν

) are the localized version of means

at each point x. The second term is the normalization

term that keeps the curve smoother. It penalizes the

arc length based on the weights λ tuned by the user.

2.6 AML Analysis

2.6.1 Skeletonization

The segmented AML region is skeletonized using

morphological thinning to get a line of one pixel

width. It helps to simplify the shape by preserv-

ing the topological (connectivity) characteristics. The

working principle is much the same as morphologi-

cal operators, requiring a binary image and a struc-

turing element. The central pixel of the structuring

element is translated to each pixel in an image. At

each step, the structuring element is compared with

the underlying pixels in an image. The Mark-and-

Delete based templates were found very reliable and

effective for thinning algorithms and thus used in this

work (Zhang, 1984). The ultrasound images contain

small irregularities due to speckle noise that results

into unessential small branches of the skeleton. The

branches need to be ﬁltered to extract only the funda-

mental part. This can be achieved by discarding all

those branches whose length are less than 6 pixels.

The length of each branch was estimated by measur-

ing the Euclidean distance between the branch and the

end point.

BIOIMAGING 2017 - 4th International Conference on Bioimaging

2.6.2 Motion Patterns

In this part of the work, we were focused to obtain

the motion pattern of the AML. The tracking results

were analyzed to extract the important information. It

was observed that the motion in the x-axis doesnt give

any signiﬁcant information. However, the motion in

y-axis provides the base to analyze the motion of the

AML. The mean of the y-coordinates of the obtained

skeleton were saved for each frame and were plotted

against time (Figure 7). The minimum and maximum

peaks of the motion pattern were estimated, to clas-

sify the frame in systole and diastole phase. The pat-

tern obtained is also useful for identifying the frames

with the AML opened as well as closed. This informa-

tion is quite useful to analyze the opening and closing

of the valve. Further work can help to classify frames

in early ﬁlling and late ﬁlling phase (Figure 7). This

information will be helpful to identify each phase au-

tomatically. The late ﬁlling will be quite useful to ex-

tract frames in which the AML is perpendicular to the

ultrasound beam. This is the best position to measure

thickness of the AML tip that provides a strong clue

to identify patients with a disease.

Figure 7: Motion patterns generated by AML.

2.6.3 Shape

The obtained tracking results can also be used for the

shape analysis by calculating the curvature at each

point on the skeleton. This can be achieved by:

skeleton

(i) =



SKL (i)



(3)

The second derivative approximates the curvature

of the AML at each point i on the Skeleton SKL. This

way, we can identify the doming (hockey shape) of

the AML in the diastolic phase, which is one of the

strongest clue to identify cases with RHD.

3 RESULTS

3.1 Materials

A dataset of the mitral valve videos obtained from the

PLAX view in ultrasound has been collected during

the activities of Real Hospital Portugu

es, in Recife,

Brazil. The videos were obtained using a M-Turbo

model by SonoSite ultrasound system, with a P10

transducer. Nine of these exams were fully annotated

by a physician using support software, to validate the

proposed algorithm. These nine videos include a total

of 1137 frames with the dimensions of [351 × 441].

3.2 Extended Modiﬁed Hausdorff

Distance

The Modiﬁed Hausdorff Distance (Dubuisson, 1994)

was proposed to obtain a distance measure to match

two objects. In this work, we extended this approach

by categorizing the segmented region as false pos-

itive, false negative and true positive (Equation 4).

We assumed that the nearest point between Automatic

Segmentation (AS) and Ground Truth (GT ) with Eu-

clidean distance smaller than 2 pixels are true posi-

tives. The part of the AS that is falsely segmented as

AML were considered false positives and the parts of

the GT that were missed by the automatic segmenta-

tion were considered as false negatives, always using

2 pixels distance as reference T (Figure 8).

AS→GT

= min

{

AS,SEG

}

FP = d > T, T P = d < T

GT →AS

= min

{

AS,SEG

}

FN = d > T, T P = d < T

MHD

= max [avg(d

AS→GT

), avg (d

GT →AS

)]

(4)

Figure 8: Region classiﬁcation.

3.3 Segmentation and Tracking

The algorithm has shown good computational per-

formance and thus is suitable for the monitoring of

the structures during heart procedures. Results on

2D PLAX ultrasound videos are presented, where the

Real-time Anterior Mitral Leaﬂet Tracking using Morphological Operators and Active Contours

AML was detected accurately and tracked during the

whole cardiac cycle. The algorithm was robust and

capable of tracking the AML in large displacements

(around 35 pixels). The validation of the algorithm is

performed by comparing the segmentation result with

the physician annotation. Results were also compared

with the AML tracking approach using active contours

(Sultan, 2016).

3.3.1 Quantiﬁcation

The proposed algorithm can identify the AML struc-

ture with an average time of 0.17 sec/frame using

morphological operators and it consumes an average

time of 0.67 sec/frame to reﬁne the contour points us-

ing localized active contours. Thus, the total compu-

tational time to delineate true boundaries of the AML

consumes 0.89 sec/frame. The reference algorithm

(Sultan, 2016) takes 121 sec/frame. The algorithm

was able to completely segment the structure of the

AML with the sensitivity of 90.4% and thus the region

missed by our algorithm was not very signiﬁcant (av-

erage FN = 5 pixels). However, the main challenge

faced was the regions which were falsely segmented

as the part of AML (average FP = 17 pixels). This

is because the AML and its neighbouring structures

such as chordae tendineae and septum have the same

texture and intensity (Figure 9).

Figure 9: Visual results for the AML segmentation. (A, B,

C) Shows results without outliers. (D, E, F) Shows results

with outliers (fused chordae tendineae and posterior mitral

leaﬂet (PML)).

The Table 1 show that our algorithm works

equally well in all videos except 2, 7 and the MHD

error difference between both the approaches is only

0.1 pixels. This happened because the false positive

tends to increase the MHD error.

The ﬁrst opening of the AML is very large. It

opens sharply before the LV diastole (Figure 7). In

the early ﬁlling, the leaﬂets maximally open to al-

low about 70 to 80% of the blood to ﬁll the LV . In

the ultrasound PLAX view this leaﬂet motion is very

large and thus difﬁcult to track for the present tracking

Table 1: MHD error in Pixels. (Ref.* (Sultan, 2016).

Patient No. No. of frames Our Approach Ref.* Approach

1 131 5.2 5.3

2 360 6.8 4.6

3 66 4.4 5.2

4 131 4.0 4.3

5 66 5.5 5.6

6 66 5.5 5.7

7 120 5.4 4.9

8 66 4.5 4.9

9 131 3.6 3.6

Average 1137 5 4.9

schemes (Mikic, Martin, 1998, 2006). In the major-

ity of the published work, researchers had used active

contour frame work that requires initialization. The

critical limitation of the active contours while track-

ing is its incapability to recover from failure. The

reference algorithm (sultan, 2016) undergoes tracking

failure that is overcome in this work. The proposed al-

gorithm successfully copes with the large leaﬂet dis-

placement in all videos, with the average MHD error

of only 3.7 pixels. One can see the signiﬁcant im-

provement from the average of 9.5 pixel error to 3.7

pixels error (Figure 10).

Figure 10: MHD error in Frames With Large Leaﬂet Dis-

placement (FWLLD*) – In pixels.

Our algorithm outperform with respect to time

consumed (difference of 1.13 minute/frame), and

frame-frame displacement with the improvement of

5.8 pixels and sensitivity to noise.

4 DISCUSSION AND FUTURE

WORK

In this paper, a new approach based on the morpho-

logical operators and the localized active contour is

proposed. Based on the morphological operators, the

algorithm ﬁnds the best match of AML in succes-

sive frames. It was observed that the displacement

of the structures in a PLAX view is not signiﬁcant,

except the AML that shows the average displacement

of 35 pixels in frames with fully open AML. The pro-

BIOIMAGING 2017 - 4th International Conference on Bioimaging

posed algorithm has successfully handled the frame-

to-frame displacement of the AML.

A 0.89 sec/frame is still slow for a real time ap-

proach, but we believe that this value can be drasti-

cally reduced by optimizing the code, converting it to

C/C + + and using multiple core processing.

The biggest difﬁculty found during the segmen-

tation and tracking was to identify, where the AML

starts, where it ends and the location where the chor-

dae tendineae connects with anterior and posterior mi-

tral leaﬂet. This is because all the tissues consist of

elastic and collagen ﬁbers that result into quite similar

texture and intensity in ultrasound. The low quality is

another obstacle that produces false positives (Figure

9).

In the future, we will focus more to improve com-

putational time and delineate the boundaries of the

AML correctly by ﬁltering irrelevant regions such as

chordate tendinae and posterior mitral leaﬂet. After

having good segmentation and tracking results, we

will be capable to automatically assess the function-

ality of the mitral valve in echocardiography.

ACKNOWLEDGEMENTS

This article is a result of the project (NORTE-

01-0247-FEDER-003507-RHDecho), co-funded by

Norte Portugal Regional Operational Programme

(NORTE 2020), under the PORTUGAL 2020 Part-

nership Agreement, through the European Regional

Development Fund (ERDF). This work also had the

collaboration of the Fundac¸

ao para a Ci

encia a e Tec-

nologia (FCT) grant no: PD/BD/105761/2014 and has

contributions from the project NanoSTIMA, NORTE-

01-0145-FEDER-000016, supported by Norte Portu-

gal Regional Operational Programme (NORTE 2020),

through Portugal 2020 and the European Regional

Development Fund (ERDF).

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