Automated Estimation of Cardiac Motion Using Mri

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AUTOMATED ESTIMATION OF

CARDIAC MOTION USING MRI

Guide : Prof. J.B. Jeeva,

Division of Biomedical Engg.

Place of work : VIT University

By

Sudhakar K (09MBE014),

M.Tech., Biomedical Engg.,

VIT University

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AIM/OBJECTIVE

To segment the left ventricle from the Cardiac

MRI

To extract the clinically relevant parameters

capable of determining normal and abnormal

heart

To analyze the left ventricle motion using Optical

flow technique

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METHODOLOGY

Read the Cardiac MRImages

Segment the Left

Ventricle Using

Morphological Operation

Find different

parameters

Find the MotionDirection of Left

Ventricle Wall

Analyze the normal and

abnormal motion of the

Left Ventricle

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SEGMENTATION

Convert the RGB Image toBinary Image

Apply the Closing Operation

Find the Connected Pixels

Apply the IMFILL operation

to fill the opening in the leftventricle

Find the Edge of the Left

Ventricle 4


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SEGMENTATION RESULTS

Fig.1 Short axis view

of Cardiac MRI

Fig. 2 Binary Image

Fig. 3 After appliedClosing Operation

Fig. 4 SegmentedLeft Ventricle

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ABNORMAL LEFT VENTRICLE

Fig. 5 Ischemia

Fig. 6 Marfans

Fig. 7 VentricularTachycardia

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PARAMETERS

Area: Number of pixels inside the contour of the

left ventricle

Perimeter: Number of pixels on the contour of

the left ventricle

2D Cross correlation: Computes the

correlation between two matrix

Variance: Computes the variance between the

two matrix

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AREA OF LV

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PERIMETER OF LV

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2D CROSS CORRELATION BETWEEN TWO

SUCCESSIVE FRAMES

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VARIANCE BETWEEN TWO SUCCESSIVE

FRAMES

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MOTION ESTIMATION

Left Ventricle wall motion is estimated by optical

flow technique

Optical flow is the distribution of apparent

velocities of movement of brightness pattern in

an image

Optical flow method is used to calculate the

motion between two image frames which are

taken at time t and t+t at every pixel point

The basis of this method is intensity conservation

between consecutive images

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FLOW CHART TO DETERMINE THE OPTICAL

FLOWTake 2 consecutive

Images

Preprocessing

Compute initialvelocity

Compute L1 & L2

from Initial velocity

Ite


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C

Built Main Matrix

Solve Equation

Obtain Velocity and

set initial

velocity=velocity

Ite=Ite+1

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PRE PROCESSING

Setting an external contour to the images of 2

pixels width, by doubling the pixels of the

original contour

To avoid getting a wrong result of thederivatives at the border of the image, and

thus, propagating this wrong result over the

pixels in the neighbourhood

Obtain the derivatives in in all the axes

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COMPUTE INITIAL VELOCITY

The initial velocities computed are given by the

following equation, which is used to solve the

optical flow equation

minv(I

xv

1+I

yv

2+I

t)2

Where Ix,Iy- intensity of image at time t.

Taking the derivatives with respect to v1 and v2 (Ixv1+Iyv2+It)Ix=0

(Ixv1+Iyv2+It)Iy=0

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CONTD.,

Clearing the Variables v1 and v2 so obtain

the initial velocitiesv1=IxIx.IyIt-IxIt.IxIy/-(IxIx.IyIy-IxIy

2)

v2=IyIy.IxIt-IyIt.IyIx/-(IxIx.IyIy-IxIy2

)

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OPTICAL FLOW ESTIMATION

oTo solve this equation, will get the optical

flow between two consecutive images

oWhere v1TL1 (z)v1 = 0

v2TL2(z)v2= 0

From this, L1 & L2can obtain

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RESULTS

Optical flow ofLeft Ventricle

wall (Frames

b/w 7th & 8th)

Optical flow

of Left

Ventricle

wall

(Frames b/w

11th & 12th)

Optical flowof Left

Ventricle

wall (Frames

b/w 21st &

22nd)

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ABNORMAL MOTION

IschemiaRVOT

Enlargement

Ventricular

Tachycardia

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PIXEL TRACKING

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PARAMETERS OF NORMAL HEART

Frame Area Perimeter Variance 2D Cross

Correlation

1 1.3993e+03 148.0833 0 0

2 1.3993e+03 148.0833 0 1

3 1.3993e+03 148.0833 0 14 1.3675e+03 147.5980 1.0107e-06 0.5804

5 1.3675e+03 147.5980 1.0107e-06 1

6 1.3675e+03 147.5980 0 1

7 1.3088e+03 145.7401 1.1862e-06 0.5330

8 1.3088e+03 145.7401 1.1862e-06 1

9 1.3088e+03 145.7401 0 1

10 1.2244e+03 135.4975 1.8725e-06 0.3937

11 1.2244e+03 135.4975 1.8725e-06 1

12 1.2244e+03 135.4975 0 1

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PARAMETERS OF ABNORMAL HEART

Frames Area Perimeter Variance 2D Cross

Correlation

1 859.1250 129.9828 0 0

2 690.2500 122.0833 2.1682e-06 0.1173

3 591 103.2548 1.9581e-07 0.30764 502 85.9411 2.0955e-09 0.2273

5 435.6250 75.6985 4.5635e-08 0.3125

6 387.6250 71.6985 8.3819e-09 0.1622

7 371.8750 70.5269 7.1304e-08 0.3901

8 367.7500 71.1127 7.5437e-08 0.6663

9 385.6250 73.6985 6.3388e-08 0.4181

10 486.3750 84.8701 8.8534e-08 0.2082

11 592.5000 99.5980 2.3283e-10 0.3250

12 709.2500 114.0833 5.8208e-11 0.3996

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GUI IMPLEMENTATION

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CONCLUSION

In this work the left ventricle is segmented using

thresholding and morphological operations

Optical flow technique is implemented to

estimate the left ventricle motion

The GUI is designed to help the clinician to

assess and analyse the motion of the left

ventricle

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REFERENCES

Carranza-Herrezuelo, N. Bajo, A. Sroubek, F. and Santamarta, C (2010). Motionestimation of tagged cardiac magnetic resonance images using variationaltechniques, Computerized Medical Imaging and Graphics vol 34 pp 514522

Horn, B. K. P. and B. G. Schunk (1981). Determining Optical-Flow, ArtificialIntelligence, vol 17(1-3), pp 185-203

Lynch, M. Ghita, O. and Whelan, P.F. (2006). Automated segmentation of the leftventricle cavity and myocardium in MRI data, Elsevier Computers in Biology andMedicine,vol 34(4), pp 389-407.

MathWorks-Matlab and Simulation for Technical computing,, Accessed on 10th Jan 2011

Petros A. Maragos, Ronald W. Schafer, Morphological Skeleton Representation andCoding of Binary Images In Proc. Of IEEE transactions on acoustics. speech, andsignal processing, vol. assp-34, no. 5, Oct 1986

Pujadas, S., Reddy, G.P., Weber, O., Lee, J.J., Higgins, C.B. (2004). MR imagingassessment of cardiac function, Journal of Magnetic Resonance Imagingpp:789799

Rafael C.Gonzalez and Richard E. Woods, (2008). Digital Image Processing, Pearsoneducation, pp 1-750.

Soroor behbahani, Keivan magholi (2007). Analysis of cardiac wall motion estimationmethods, IEEE Trans., on medical imaging, pp.1102-1107

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PUBLICATIONS

Sudhakar K and J.B. Jeeva (2011). Automated

Estimation of Cardiac Motion from tagged MRI

using CNN, International Conference on

Systemics, Cybernetics and Informatics vol 2(2),

pp 149-150

Sudhakar K and J.B. Jeeva (2010). Cardiac

Motion Analysis: A review VIT Science

Engineering and Technology Conference

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Documents

Automated Estimation of Cardiac Motion Using Mri