Kluwer - Handbook of Biomedical Image Analysis Vol

Figure 7.22: The steps of the algorithm for the detection of the contours.

gray-level saturation Sα 3:

This measure determines the percentage of pixels in the subimage whose gray level is larger than tmax( fr) − α. If this percentage is too high, the channel is saturated and does not contain any exploitable information. We use the red channel, if for α = 30, Sα < 0.5 (this has been found experimentally), if not, the luminance channel is used. We call the used channel fc in the following.

For finding the contours of the optic disk, we shall make use of the watershed transformation applied to the gradient image of a filtered version of the channel fc (see also Fig. 7.22).

3 The gray-level saturation Sα should not be confounded with the color saturation.

First, we attenuate the noise in the image using a Gaussian filter G (type and parameters of the filter are not crucial, we used a 9 × 9 filter with σ = 4). Then, the vessels interrupting the circular shape of the optic disk are filled using a morphological closing:

with s1 such that the largest vessels are filled (as explained in the previous section). In order to remove irregularities within the papillary regions that may also produce a high-gradient value, we apply an opening by reconstruction:

Calculating the watershed transformation of this gradient would lead to a strongly oversegmented result. Once again, we have to find a marker and impose it (see section 7.3). With only one source within the optic disk, the algorithm gives exactly one catchment basin which—if the filtering process has been efficient— coincides exactly with the optic disk. We use the approximated center c as “inner marker.” As external marker, we use a circle centered in c with a diameter larger than two times the largest possible diameter of the papilla (factor 2 for the case that the approximation was bad and the approximation of the center c lies on the border of the optic disk).

With this marker, we can now calculate the watershed transformation:

7.5.2.5 Results

The algorithm has been tested on 60 color fundus photographs (640 × 480) taken with a Sony color video 3CCD camera on a Topcon TRC 50 IA

7.6.1 The Detection of Microaneurysms

7.6.1.1 Motivation

Microaneurysms are the first ophthalmoscopic sign of diabetic retinopathy [1]. Over and above that, their number is an indication of the progression of the disease. Their detection is therefore crucial for the diagnosis of diabetic retinopathy, for the mass screening, and for the monitoring of the disease.

7.6.1.2 Properties

Microaneurysms are tiny dilations of the capillaries. They appear as small reddish isolated patterns of circular shape in color fundus images of the human retina [1]. Their diameter normally lies between 10 and 100 m, but it is always smaller than 125 m. As they come from capillaries, and as capillaries are not visible in color fundus images, they appear as isolated patterns, i.e. disconnected from the vascular tree.

Microaneurysms are sometimes hard to detect: Their contrast is often very low and sometimes, they are hardly visible and difficult to distinguish from noise. Their reddish color can hardly be used for their detection, because it is far from being constant in different images (see Fig. 7.24).

7.6.1.3 State of the Art

The first algorithm for the detection of microaneurysms has been presented Lay¨ [19]. The author introduced the radial opening γ sup = 0 γ Li , i.e. the supremum

A

B A B A B

(a) (b) (c)

Figure 7.24: Microaneurysms in color images. (a) Sure microaneurysms;

(b) doubtful cases.

Figure 7.25: The principle of the algorithm for microaneurysm detection.

of openings with linear structuring elements Li in different directions in order to remove the microaneurysms but to preserve the piecewise linear vessels. This technique has been used by nearly all the authors working on the automatic detection of microaneurysms; important improvements have been proposed in [20, 21].

7.6.1.4 The Algorithm

The algorithm presented in this section is based on the strategy shown in Fig. 7.25. First, the shade correction method described in section 7.4 is applied, and then candidates are detected by means of the diameter closing and an automatic threshold; features calculated for these candidates allow their classification into real microaneurysms and false positives. A first version of this algorithm has been presented in [22].

Prefiltering and shade correction: The objective of this step is to attenuate the noise, to enhance the contrast, and to correct the nonuniform illumination.

As it has been stated in section 7.2, microaneurysms—like all blood containing elements—appear best contrasted in the green channel. First, the shade correction operator described in section 7.4 is applied to the green channel. It is crucial that this algorithm does not introduce new dark regions which would cause a lot of false positives.

Besides the shade correction, the operator SCnorm enhances the contrast of structures in the image depending on their size. In order to privilege small vessels and microaneurysms more than larger hemorrhages and large vessels, an adapted size of the window used in SCnorm can be chosen. A small gaussian filter attenuates the noise, but enhances microaneurysms; it can be seen as a matched filter [20]. With G a gaussian filter, we obtain the prefiltered image by (see also Fig. 7.26):

for each threshold, we use hierarchical queues in order to simulate a flooding of the image. We explain this technique for the diameter closing.

The flooding starts with the lowest local minima in the image (i.e. with the global minima). We determine the diameter of all the lakes with gray level s. If the diameter of a lake exceeds λ, the output image takes the value s for all the points belonging to this lake (“the flooding stops for this lake”). Then s is incremented, the new local minima at this level are added, and the existing lakes are extended until there is no more pixel x in the image with f (x) ≤ s not belonging to a lake. If two lakes meet, they fuse and it is considered as one lake from now on. Then, when the flooding has been finished for this level, the diameter of all lakes are calculated. If the diameter of a lake exceeds λ, but has not exceeded λ for the previous level, the output image is set to s for all the pixels of this lake. In this way, we flood the whole image until there is no more lake with a diameter inferior to λ.

This algorithm can be implemented very efficiently with hierarchical queues. See [8] for details.

In Fig. 7.29, we show the application of the diameter closing to the detection of microaneurysms. The prefiltered image is shown in Fig. 7.29(a); its closing by diameter in Fig. 7.29(b). We note that the distinction of holes and ditches (microaneurysms and vessels) works very well: The microaneurysms are completely filled whereas the vessels are not touched. The associated top-hat φλ◦ f − f is shown in Fig. 7.29(c). The small details visible in this image and not corresponding to microaneurysms are “parasite holes” and are due to irregularities and noise in the image. From this image, it is easy to get the candidates by a threshold. The applied threshold technique is shown in the next paragraph.

Figure 7.29: Detection of dark details by means of the diameter closing.