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Astron. Astrophys. 358, 741-748 (2000)

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2. Observations and data reduction

2.1. Observations

The EUV observations were obtained with the Coronal Diagnostic Spectrometer (Harrison et al. 1995) operating in high telemetry mode. The observing sequence INT[FORMULA]DIST (see Table 1) was designed primarily to provide CDS /NIS (Normal Incidence Spectrometer) images of the quiet Sun at disc centre in nineteen wavelength regions. The lines identified together with the maximum temperature of formation, are given in Table 2. The data were corrected for cosmic ray events, CCD readout bias, pixel-to-pixel variations and tilted spectral lines. This was done using the standard CDS/NIS calibration software. A radiometric calibration was also performed to convert the original photon count rates into ergs cm-2 s-1 sr-1 Å-1. Note that we did not use the full 240 pixels in the Solar-Y direction. It was necessary to remove the top 37 pixels due to a small brightening event occurring at this location.


[TABLE]

Table 1. Details of the high telemetry INT_DIST observing sequence.



[TABLE]

Table 2. The lines identified in the observing sequence INT_DIST .


Using the Michelson Doppler Imager (MDI ) (Scherrer et al., 1995) magnetograms were also obtained at 08:44 UT on 11 April. MDI was operating in the high resolution mode. In this mode 10 arcmin [FORMULA] 10 arcmin images with a spatial resolution of 1-2 arcsec were taken at the same location as the CDS observations.

2.2. Image segmentation

In this section we expand upon previous image segmentation algorithms discussed by Gallagher et al. (1998). We use a histogram modification technique based on detecting pixels within the images which show high edge values (Weszka & Rosenfeld, 1979; Gupta & Sortrakul, 1998).

We assume that the image consists of three components; internetwork, network, and bright network, each having a unimodal grey-level intensity distribution. Therefore, the intensity histogram of the total image will be a mixture of three unimodal histograms. The choice of three distributions seems reasonable as the quiet Sun has been shown to contain not only the internetwork and network components (Gallagher et al., 1998), but also an enhanced network or bright point component (Dowdy, 1993). Habbal & Grace (1991) find that the enhanced emission covers between 10 and 25% of the surface depending on the temperature. In most situations a threshold level between each of the populations can be difficult to detect due to the fact that they are not well separated in intensity.

There are several methods that can be used to produce a transformed grey-level histogram in which the valley between populations is deeper, and is thus easier to detect. Suppose we have an image, [FORMULA], composed of three regions closely grouped in intensity. We can use the Laplacian operator to detect the threshold between populations. In a continuous image the Laplacian operator

[EQUATION]

forms a second partial derivative of the image. In regions where there is little or no variation in intensity, the absolute value of the Laplacian is zero while large values occur on the borders between objects. The histogram of high Laplacian points should therefore have two peaks, representing the internetwork-network boundary ([FORMULA]) at low intensities and the network-bright network boundary ([FORMULA]) at high intensities (see Fig. 1).

[FIGURE] Fig. 1. The original and Laplacian transformed histograms for the 240[FORMULA] 240" OV (629.73 Å) image. The threshold levels for the internetwork-network ([FORMULA]) and network-bright network boundaries ([FORMULA]) are also shown.

The Laplacian modified histogram can then be fitted with two Gaussians in order to establish the thresholding levels to be used in pixel classification. In our case the following classification rules are then applied;

[EQUATION]

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© European Southern Observatory (ESO) 2000

Online publication: June 8, 2000
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