The in order to go for exudates detection

The method we have
applied to detect exudates on human retina is inspired by the work described in
35.  Since the data set is of
completely different characteristic we have changed in various sides. That is
why we are going to describe every step and the reason behind taking it. We
have used MATLAB version 2017a for this project and this detection consists of
the following steps :

Preprocessing the image.

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Detection of Optic disc and other artifacts.

 (c) Detection of exudates in terms of optic
disc and artifacts.

In the preprocessing
step first we extract intensity constituents from an image. Here we are going
to work with gray-scale images because exudates are mostly visible in such images.
We then apply Median Filtering for reducing the noise and apply Histogram
Equalization to enhance contrast and brightness. The resulting image helps us
to detect optic disc and accordingly exudates. This works as input image.
Exudates are high intensity values as well as optic disk. Therefore in order to
go for exudates detection we need to find optic disc and then we need to
differentiate between optic disc and exudates near and inside the optic disc
area. To do this we consider that optic disc is the largest and most circular
part in brightest portion of the image. We apply Gray Scale Closing to remove
blood vessels in the retina mostly in the optic disc area. Here we take a flat
disc shaped structure element and consider the radius is eight. We threshold
the image to binaries it and use the resulting image as a mask. Then the mask
is inverted by pixels before overlaying into the original image. We then apply
morphological reconstruction by dilation was on the overlaid image. We
threshold the image and find the difference between the original image and the
reconstructed image by the Otsu algorithm. Consequently, high intensity optic
disc is detected and rests are removed.

In this part we faced a
big problem of this approach. At the beginning of the process, vessels were
removed by the Gray Scale Closing and  reconstruction was applied on the image
created from the original image. Therefore we are going to reconstruct vessels
in the optic disc area. But we face a problem is that we are not getting one circular
optic disc. Rather we are actually detecting two or three connected components
in this step. To solve this problem we applied an addition dilation of the
final mask. As a result the independent areas are connected together into a
circular shape. Here we note that we have already detected artifacts and other
bright spots in the image. That is why if we use too big dilation, it can lead
to merge the optic disc with those areas.

For the proper
additional dilation we have considered a flat disc shaped structured element
with a radius of four. Since the optic disc and also some bright artifacts are
detected in this process, we have estimated for every component of the mask in
order to distinguish between the features some extra values. These additional
values are termed as scores. Thus we have,

                                    Score =



Here we have some case
to give attention. Since we have situation that the feature rather than optic
disc can become much larger than optic disc, we needed to give circularity more
importance. We take elements of size more than 1100 pixels as an optic disc
keeping the rest as artifacts. Here we do not classify small areas which can
become exudates as artifacts. At this stage after optic disc extraction and
artifacts detection we are going to detect exudates. As before, high intensity
blood vessels are removed by Grey Scale Closing. Then we go for to get a
standard deviation image which shows the main characteristics of nearly
arranged  exudates. The resulting image
is being threshold by taking the radius is six. We than remove the outside
shape of the retina and fill the holes by imfill(). We consider threshold to
remove optic disc and artifacts. Finally the result is achieved when we apply a
threshold at a level 0.01 between the original and the reconstructed one. The
produced exudates mask image is overlaid into the main image to get a proper