A New Method of Echocardiographic Edge Detection Using Velocity Maps

S Khoor, F Szaboki*, J Nieberl**, M Khoor**, E Kekes*** Hosp.Merenyi, * Centr. Railway Hosp, ** Profilaxis Ltd, ***Postgraduate Med. School

Budapest, Hungary

Abstract

Our automated image processing method czates the velociq maps of the small ~ g w m of echocardiographic 2 0 images. The separation and matching of thk objects aiE based on their velocity profiles. The object-oriented programing method allows the handling such a complex pmblem The automated analysk showed a good performance comparing with the traditional wall motion detection : dze specfzcity of cornputer scoling war 84.4%, the sensitivity 81.2% .

1. Introduction

The automated edge detection of endocardial borders of 2D echocardiograms has some limitations : problems related to the pour acoustic window, the effect of valve motions etc. Both the traditional methods - using the gray level values of digitalized video images [ 11 either the image segmentation or the edge detection technics - and the new, on-line method of signal pawer detection [2] based on the series of fixed images. Our method simulating the cardiologists' visualisation mode creating different velocity maps of moving objects (the myocardium, its two borders, the valves). The separation of these regions based on their velocity features.

2. Methods

Conventional two-dimensional echocardiograms were performed in three standard views (parastemal short-axis, apical two- and four-chambers) with IREX Meridian echocardiograph in 28 patients (20 men and 8 women with a mean age of 54.3 years) with IHD who undenvent cardiac catheterisation. The video-images were evaluated by two independent cardiologists manually digitalised each views' frames at end-diastole and end-systole [4].

0276-6547193 $3.00 0 1993 IEEE

2.1. Image processing

The automated 2D image processingwas programmed by the object-oriented method of Turbo Pascal 6.0. The various whole images and their smaller parts (down to the smallest ones, the pixels ) are structurated as objects with their attributes. There are three main features of OOP determinating the significant flexibility and for this reason favorable in the domain of artificial intelligence technics:

- the encapsulation of the objects and their methods (procedures or functions),

- the possibility of the inheritance between the objects,

- the polymorphism of the objects and methods.

I x = l x=4 x=8

I PO-I-ti

Figure 1. Gray levels of the images.

The preprocessing of digitized video-images is similar to our previous method [3]. In the first step of image processing a special picture-object (PO-xt) is determined for all frames, where x means the gray level, t is the index of time-series, (Figure 1. and 2.).

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The sampling rate was 50 ms. Eight gray levels were determined after the visual control of the cardiologists (x(i= 1 to 8)). The other objects are the local-picture-objects (LPO). The smallest objects (LPOP1) ,which are the pixel-objects , were determined from all PO-xt objects and inherited the "xt" attributes form PO. Their own attributes are the x-y coordinates of the pixels.

PO-&t( 1)

Figure 2. Frame sequencies of the same gray level.

Next step the LPOP2 were determined from a 3*3 pixel-matrix LPOP2 is inherited all the LPOP2's attributes apart of the original coordinates. The new attribute of LP02 is the pixel density. It's important to note, that up to this stage our method includes many methods of conventional image processing L P 0 3 objects - determined by the pixel density - are represent the end-stage of "fix-images" analysis.

The next group of objects consists of the clusters of the similarly changing ( namely changing within one gray level and between the eight gray levels ) 3*3 pixels ("mavi ng- I oca 1 -objects": MLO). By our experience creating 10 clusters (MLO(C=l to 10)) is sufficient for the analysis. The next objects, the moving-mixed-objects (MMO) inherited their attributes from PO, LPO, LPOP and MLO objects and are used for linking the areas changing in the same manner. MMOA represents the unchanging areas below the lower gray level, MMOB is the sum of unchanging areas above this level, MMOCn(n= 1 to i) are the objects with changing features (Figure 3.). The median cluster of MMOC objects characterize the true wall motion of the heart.

The end-systolic and end diastolic contour were determinated from MMOC the method commonly used in quantitative echocardiography - dividing the image of left ventricle into equiangular areas, with the center of gravity as both origin and reference point of the coordinate system (fix-reference system) was applicated.

~ p o p 2 CHANGING PROFILE MMOC (Y/N) O F CLUSTER No. 7. (n =4)

Figure 3. Global and local picture-object. (See abbreviations in the text.)

3. Measurements

According to the standards of the American Society of Echocardiography Comittee (sixteen-segment two-dimensional echocardiographic model) the six segments of the two-chamber (basal-inf(BI2C), mid-in€ (MI2C), apical-in€ (A12C), apical-ant (AA2C), mid-ant (MA2C), basal-ant (BA2C)) and four-chamber (basal-sept (BS4C), mid-sept (MS4C), apical-sept (ASIC), apical-lat (AL4C), mid-lat (ML4C), basal-lat (BLX)) long-axis views, the six segments (mid-inf (MISAX), mid-sept (MSSAX), mid-ant.sept (MASSAX), mid-ant (MASAX), mid-lat (MLSAX), mid-post (MPSAX) of short-axis view at papillary muscle were determined and scored (normal= 1, hypokinetic=2, akinetic=3, dyskinetic=4) by both the (two) cardiologists and the computer.

4. Results

Table-1. shows the results of classification. Because of the limited data of the cells, the normal

scores were compared to the abnormal wall motion. The sensitivity was 88.4%, the specificity 81.2% ( p 0.01; N=504) , the false-negative rate 11.6% , the false-positive rate 18.8%.

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Table-1.: quantification the wall motion of the 18 segments in 28 patients by the cardiologists (M) and the computer (C).

Segment Wall motion normal hypokinetic akinetic dyskinetic

BI2C AI2c MA2C BS4C AS4C ML4C MISAX MASSAX MLSAX MI2C AA2C BA2C MS4C AL4C BL4C MSSAX MASAX MPSAX

M C --M C M C -M

12 8 13 18 3 2 0 9 1 2 9 8 6 5 4 10 8 7 7 10 11 1 9 10 11 10 8 8 0 8 10 16 16 4 2 0 14 13 8 11 6 4 0 10 11 11 11 7 6 0 9 13 11 8 8 7 0 12 12 11 11 5 5 0 11 10 10 11 7 7 0 1 0 8 7 9 7 7 4 13 11 9 12 6 5 0 8 8 1 3 1 2 7 8 0 14 13 11 13 3 3 0 18 16 6 7 4 5 CJ 9 10 13 12 6 6 0 13 11 6 7 9 10 2 10 11 12 10 6 7 0

C

0 3 2 0 0 0 0 0 0 0 4 0 0 0 0 0 2 0

5. Discussion

Our automated 2D echocardiographic image processing method was developed to avoid the greatest disadvantage of any border recognition programs depending heavily on the quality of the images. Apart of this, the evaluation of wall motion by velocity maps - as the other optical flow methods [5] - is nearer to the clinician’s way of seeing. Moreover using OOP technics this kind of more complex analysis could be performed. Further investigations is necessary for the validitation of the method including the separate analysis of segments‘ velocity profile in the population of normal subjects and in various heart disease groups.

References [l] Skorton DJ, Colins SM, Garcia E et al. Digital signal

and image processing in echocardiography. Am Heart J 1985:110:1266-83.

[2] Vandenberg BF, Rath LS, Stuhlmuller P, a t al. Estimation of left ventricular cavity area with an on-line, semiautomated echocardiographic edge detection system. Circulation 1992;86: 159-66.

[3] Schiller NB. Two-dimensional echocardiographic determination of left ventricular volume, systolic function, and mass. Circulation Suppl.1.

[4] Khoor S, Nieberl J, Szaboki F et al. The first derivative of systolic Doppler flow : methods and clinical applications. In: Computers in Cardiology 1991. Los Alamitos: IEEE Computer Society Press, 1991565-8.

1991;84:1-280-7.

[5] Mailloux GE, Bleau A, Bertrand M, Petitclerc R. Measurement of heart motion from two-dimensional echocardiograms. In: Computers in G i r d i o l o ~ 1987. Los Alamitos : IEEE Computer Society Press,

Adress for correspondence:

Sandor Khoor MD. Gyali ut 17-19. H-1097 Budapest, Hungary

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