ANÁLISIS DEL OBJETIVO Nº 01

Subjective visual estimates of vessel diameter from X-ray angiographic images have been shown to be associated with substantial inter- and intra-observer variability (Detre et al 1975; Zir et al 1976; Fisher et al 1982; see review in Colchester 1984).

Early attempts to assess blood vessel diameter objectively from X-ray films employed mechanical or electromechanical callipers to measure the vessel diameter (RafflenbeuI et al 1980; Chikos et al 1983). Isner and colleagues (1981) compared mechanical calliper measurements of per cent diameter stenosis from cine-film images of the left main coronary artery with post mortem histologic cross-sectional area measurements in 84 angiograms. They observed an under- or over-estimation in 71% of these cases.

Later Scoblionko et al (1984) developed an electronic hand-held calliper wired to a programmable calculator that could be applied directly to projected cine-film images.

Although calliper measurements of percent diameter stenosis improve accuracy and reproducibility relative to subjective interpretation, three important limitations still remain: first, relative narrowing depends on the subjective definition of the 'normal' diameter, which may be complicated by the presence of post-stenotic dilation and/or diffuse disease; secondly, the definition of vessel boundaries is subjective, and is influenced by display contrast and brightness, quantum and structural noise, and radiographic scatter; and finally, single view measurements of diameter, even if accurate, produce erroneous estimates of lumen area for eccentric stenoses. Since the human variability inherent in visual interpretation is unlikely to diminish, callipers have not achieved widespread clinical application.

The logical extension of calliper measurement was the derivation of the digital coordinates from hand tracings of vessel boundaries (Gensini et al 1971 ; Brown et al 1977). Since the film image is optically magnified prior to tracing, the precision of these techniques is limited only by the inherent resolution of the film and the visual determination of the vessel edges.

Although the accuracy and reproducibility of arterial quantification is improved by digital hand tracing, these measurements are limited by vessel foreshortening, due to non-orthogonal imaging, blood vessel curvature and overlapping blood vessels. These features prevent accurate estimation by these techniques of cross- sectional area from a single projection.

In addition, the projected angiograms of the vascular segment have to be traced manually, and thus the key input data to be analysed by a computer must still be based on a subjective visual interpretation.

To overcome the limitations of these manual procedures, a number of computer assisted artériographie techniques have been developed to reduce the variability of manual tracing of vascular segments by using computer analysis to provide a more accurate measurement of blood vessel diameter (Brown et al 1986). Using automated edge detection would eliminate subjective bias. The Canny edge detection operator (Canny 1986) is considered to be a very accurate edge detection technique by most computer scientists. As the Canny edge detector is customised to give the highest sensitivity detecting true edges, the lowest sensitivity for detecting false edge and multiply defining a single edge, while monitoring the highest accuracy in edge location. Currently the Canny detector has not been optimised for the blurred semielliptical profiles of blood vessel of the X-ray angiogram. However, at present there is no widely agreed algorithm for finding the true vessel edges. Most computer edge detection programs consist of the following steps:

1 ) Operator selection of the blood vessel segment. This is accomplished by using a mouse controlled cursor to interactively identify the approximate centre line of the vessel segment of interest by defining points along the vessel segment.

used for accurate definition of vessel diameter require the analysis of cross-sectional profiles of the contrast density obtained perpendicular to the blood vessel axis the transverse density profile (TOP). The TDP is defined as the plot of grey level intensity across the X-ray image in a direction perpendicular to the image of the blood vessel axis after logarithmic transformation of image intensity. Centre line definition permits the acquisition of these profiles for vessels which curve within the image plane.

The TDP of a vessel can be approximated by a Gaussian plot. A method of least squares can then be applied to the profile to estimate the Gaussian parameters. The median of the Gaussian spread could then be used as an estimation of the centre line (Fessier and Macovsky 1991). Others compute the centre of gravity of the grey level distribution perpendicular to the operator definition centre line. The true position of the vessel centre line is estimated as the locus of this centre of gravity (Reiber et al 1984 and Hawkes et al 1988a).

3) Automatic detection of the vessel edges. Previous determination of the true centre line allows extraction and subsequent analysis of cross- sectional densitometric profiles perpendicular to the defined axis. Several empirical algorithms, which calculate the true blood vessel luminal boundaries from these densitometric profiles, have been investigated (Alderman et al 1981 ; Pfaff et al 1985; Reiber et al 1984; Fujita et al 1987; Hawkes et al 1988a).

The first and most common of these edge detection algorithms finds the peak of the first derivative; i.e. the TDP is differentiated and then used to identify the position of maximum slope identified as the ’edge’. This method assumes that the vessel edges coincide with the positions of greatest intensity change within the densitometric profile (Alderman et al 1981 ; O’Handley et al 1973). This technique works well for sharp images of larger vessels with circular cross-section, but is less applicable for smaller or diseased vessels because of image blurring which alters the position of the maximum edge gradient, resulting in an underestimation of the true vessel diameter (Pfaff et al 1985). As there is no reason why the actual vessel edge should correspond exactly to the point at the maximum of the first derivative, calibration curves have been employed, which can be obtained with

the help of phantom measurements (Wong et al 1986).

The position of distinct points such as the maximum and minimum second- derivatives has also been investigated (Doriot et al 1985). In this second derivative approach, the edge points tend to be too wide to fit the arterial segment and the inherent sensitivity of the second derivative to noise limits the accuracy and precision of these methods. The ability to reduce this sensitivity to noise by image smoothing is limited since the errors due to blurring will only be increased.

Reiber et al (1984) developed a combined first and second derivative technique to compensate empirically for the observed under- and overestimation of vessel diameter associated with each technique. This technique is empirical in the sense that the row measurements are appropriately weighted to obtain the best correlation between measured and true vessel dimensions. The alternative is to rigorously define the radiographic factors which produce inaccurate measurements and then develop techniques to compensate for these errors. They reported a precision of 120 /xm in digitized film images of a phantom when identifying the edge as a weighted sum of the modules of the first and second derivatives.

As described above, methods for determination of cross-sectional area using computerised edge detection techniques require a criterion to define the vessel edge, which may be highly subject to interference by noise, and may depend upon iodine concentration.