ETR-1 is a multi-purpose test tool to evaluate the image quality of all types of radiographic and fluoroscopic systems using films. It is manufactured by Scanditronix Wellhöfer GmbH [1]. The test-plate merely consists of a contrast module and a resolution module. The resolution module is moreover, consists of a resolution measurement unit, the low contrast details and a measuring area of the optical density. There are some alignment marks and centering rings in different formats. A centimeter scale and a centering tube is also available in this test tool.

Exposure geometryis also useful for various types of investigations. Adjustment of image intensifier size, as well as, the alignment of the light field edges to the X-ray field are controlled by this parameter. Moreover, it is helpful for the alignment of light field center with the center of the image. Magnification (scale length of the test pattern vs. the actual scale length) is also maintained by the exposure geometry. Centering tube for checking the perpendicular beam is also available in the test tool ETR-1.

The canny algorithm works based on three criteria namely good detection, good localization and only one response to a single edge.

Assuming the finite impulse response of the filter is f(x), bounded by [−w, +w]. The edge itself is referred by G(x). The root mean square noise amplitude per unit length is n₀. Then the signal to noise ratio (SNR) criterion [2] is given by Eq. (1).

$$\mathrm{SNR}=\frac{\left|{\int }_{-w}^{+w}G\left(-x\right)f\left(x\right)dx\right|}{n_0\sqrt{{\int }_{-w}^{+w}{f}^2\left(x\right)dx}}$$(1)

The higher SNR ensures that a real edge is not missed, while the non-edge point is not detected as the edge.

The localization accuracy criterion [2], is followed by Eq. (2).

$$\mathrm{Localization}=\frac{\left|{\int }_{-w}^{+w}{G}^{\prime }\left(-x\right)f^{\prime }\left(x\right)dx\right|}{n_0\sqrt{{\int }_{-w}^{+w}{f}^{\mathrm{\prime }2}\left(x\right)dx}}$$(2)

Finally, the one response criterion [2] is considered which minimizes multiple responses to a single edge. The mean distance between zero-crossing of f^′ is x_zc. Then we write,

$$x_{zc}\left(f\right)=\mathrm{\pi }{\left[\frac{{\int }_{-\mathrm{\infty }}^{+\mathrm{\infty }}{f}^{\mathrm{\prime }2}\left(x\right)dx}{{\int }_{-\mathrm{\infty }}^{+\mathrm{\infty }}{f}^{\mathrm{\prime \prime }2}\left(x\right)dx}\right]}^{1/2}$$(3)

The Auto threshold was used to visualize the test-plate image. Canny edge detector and image dilation allows to observe the geometric shapes of the plate properly, especially the central ring of ETR-1. The entire canny algorithm works based on these three criteria given by Eq. (1–3).

In a two dimensional space, a circle can be represented as ${\left(x-a\right)}^2+{\left(y-b\right)}^2=r^2$, where, (a, b) is the center of the circle in cartesian coordinate and r is the radius. Thus, the parameter space would be (a, b, r), i.e. three dimensional. Circle detection process can be divided into two stages. The first stage is finding the optimal center of circles in a 2D parameter space. The second stage is to find the optimal radius in a one dimensional parameter space [3].

Once we have the detected edge of cental ring by canny edge detection, the central ring of ETR-1 image quality test tool was detected by circle Hough transform. The center was then extracted for further processing.

Assuming, $\mathrm{\Delta }x=x_2-x_1$ and $\mathrm{\Delta }y=y_2-y_1$. Bresenham algorithm begins with a starting point and then it increments the x-coordinate by one. It is not interested to track the y-coordinate (which increases by the $m=\mathrm{\Delta }y/\mathrm{\Delta }x$, each time the x increases by one). At the same time, it keeps an error bound ϵ at each stage, which represents the negative of the distance from the point where the line exits the pixel to the top edge of the pixel. The value of ϵ is first set to m − 1, and is incremented by m each time the x-coordinate is incremented by one. If ϵ becomes greater than zero, it means that the line has moved one pixel upwards. For this reason, we must increment the y-coordinate and adjust the error to represent the distance from the top of the new pixel. It is done by subtracting one from ϵ [4].

We used this algorithm both for the treatment of the contrast module, as well as, the resolution module to estimate the high contrast line-pairs. The intensity curve of the contrast module is found from the bresenham lines. In resolution module unit, two sets of bresenham lines were created to evaluate the lp/mm.

Two binary masks were created to get a mask difference. This mask difference as a circle touches the lower corners of the contrast module. The dynamic location of the test-plate was determined mathematically to track the test-plate. This tracking is useful for the unexpected orientation of the test-plate. The highest intensity point of the entire image was chosen as reference. With help of this point and the test-plate center, another lower corner of contrast module was calculated.

We evaluated three types of image parameters by our developed program. The contrast (C) of the contrast module was calculated from the intensity curve which was found by applying the bresenham line algorithm. Finally, the contrast was calculated by Eq. (4).

$$C=\left(L_{max}-L_{min}\right)/\left(L_{max}+L_{min}\right)$$(4)

The notation L_max and L_min denotes the maximum and minumum brightness of the most neighbouring units of the contrast module.

The low contrast details were handled by creating five binary masks. Rectangular grids in 2-D space were created for this purpose. A reference contrast unit onto the optical density measurement area was considered to compare the contrast details graphically.

For high resolution line-pairs, the conventional way of finding the lp/mm was followed. Bresenham algorithm permits fix it onto the resolution module unit. Both line-pairs columns were considered. The important image parameters were extracted as numerical figures and showed in a graphical manner.