ADAPTIVE HISTOGRAM EQUALIZATIONIN DIABETIC RETINOPATHY DETECTION

Daniela A. Parletta danielaangela.parletta@akkodisgroup.com
Giovanna Purgato giovanna.purgato@akkodisgroup.com

ARTIFICIAL INTELLIGENCE & DATA SCIENCE UNIT

DIABETIC RETINOPATHY DETECTIONWPROPOSED APPROACH

e consider the Diabetic Retinopathy Detection problem where, given a raw image of the eye fundus,
the objective is to automatically detect the presence of a Diabetic Retinopathy (DR) disease. Suchsystems are usefull to support the specialists in the diagnosis and the treatment of this pathology.

EMPIRICAL RESULTS

Experiments with real-world datasets (MESSIDOR-2, IDRiD)

We train a deep network to predict, given a raw image and a specific image equalization

using CLAHE for image equalization.

algorithm, which parameters should be used by the algorithm for the equalization.

IDEA

This network, is trained jointly with the predictive deep learning model used for the DR
detection, so its predictions are optimized for the final accuracy.

• According to the most recent medical standards, DR can be classified into 5 different stages

ADAPTIVE HISTOGRAM EQUALIZATION WITH CLAHE• Detection is the binary classification version of this problem, where one only want todistinguish between an healthy subject (stage 0 or 1) from a sick subject (stage 2,3 or 4).

• Deep learning methods have showed to be state-of-the-art for this problem.
REMARK

The performances of deep learning methods for DR detection are
extremely sensitive to an appropriate equalization of the input images.

Tuned-CLAHE

D1

0.859

20.656Predicted-CLAHE

D10.842

1.753

No-CLAHE

D20.702

1.359

Tuned-CLAHE

D20.72138.844

Predicted-CLAHED2D1

AC

0.810

TIME (S)

0.799

0.869

1.879

Method: No-CLAHE corresponds to not making any equalization;

The overall system is composed of a series of two networks, a ResNet and an EfficientNet.
After the system is trained in an end-to-end fashion, a raw image is fed to ResNet which selects theCL for the CLAHE. Second, the image is equalized with the tuned CLAHE method; finally, the processed
image is fed into EfficientNet for retinopathy classification. The green bounding boxes reported on theIMAGE EQUALIZATIONDATA

No-CLAHEwith 0 and 1 corresponding to healthy subjects, and the other corresponding to the presenceof DR ordered by severity.

METHODequalized image have an illustrative purpose and are not applied in practice: they illustrates the many

Tuned-CLAHE refers to a grid-search procedure to find theparameters for CLAHE; Predicted-CLAHE denotes the proposed method. In
Data, D1 referes to training on MESSIDOR-2 and testing on IDRiD; D2 thevice-versa. Ac stands for accuracy. Time is the average inference time
across the predictions made on the test set.

fine-grained details highlighted by the equalization process.

Image Equalization. Process the image pixel distribution to make it as close as possible to the uniform
distribution. This usually results in an improved perceived contrast. In the context of DR detection,equalization highlights fine-grained details of the eye fundus, such as micro-aneurysms, a type of lesionthat is typical of the advanced DR stages.

• The first network is implemented with a relatively simple model in order to not

LIMITATIONS OF THE ENTROPY BASED CRITERIA

excessively increase the inference time.
Training time is increased w.r.t. to a traditional DR detection method.
The prediction time is strongly reduced since the computation of the equalization parameters
is made through a feedforward pass, instead that an expensive search procedure.

On the left, a raw eye fundus image of a subject
suffering from a severe diabetic retinopathy.
On the right, the equalized image where there arevisible and highlighted in the white edged boxes manyof the typical lesions caused by the pathology.

Many state-of-the-art algorithms for image equalization require input parameters thatare tuned ad-hoc through computationally expensive grid-search procedures. Moreover,

PROBLEM many of the proposed quality criteria are not directly related to the predictive accuracy

The overall accuracy can be even better than a grid-search based systemsince equalization is optimized for the predictions.

REMARK

Joint training is realized by replacing, at training time only, the hardthresholding

implemented by the output layer of the first network with a Gumbel Soft-max Activation.

GUMBEL SOFT-MAX ACTIVATION

image equalization. It is based on local equalizations performed at the level of images patches calledtiles; the tile size (TS) is a parameter. Additionally it relies on a second parameter, known as clip limit (CL),to limit the level of introduced contrast.

severe diabetic retinopathy. In the middle, the equalized image with

CL = 19 as predicted by our method Predicted-CLAHE. On the right, theequalized image with CL = 2 as tuned by Tuned-CLAHE.
Notice that Tuned-CLAHE, differently from Predicted-CLAHE, miss-classifies
this image as that of an healthy subject. This comparison shows that whileof the detection system that uses the equalized images.

Contrast limited adaptive histogram equalization (CLAHE) is one of the most popular algorithms forO

n the left, a raw eye fundus image of a subject suffering from a

the first approach is beneficial to improve the perceived quality of the• This activation is differentiable and then allows for the use of back-propagation.
• It approximates the hard-thresholding at a degree controlled by the temperature τ > 0. Small valuesof τ lead to very good approximations of the hard-threshold;large values of τ lead to poor approximations but to a greater numerical stability of the gradients.

image, it may lead to sub-optimal predictive performance, since it doesn’t
sufficiently highlight the finer details (e.g. many micro-aneurysms present in
the eye fundus of this patient) that are important to detect the pathology.

DanielaA.

Parletta

Giovanna

Purgato