Novel network-based methods for retinal fundus image analysis...

Novel network-based methods for retinal fundus image analysis and classification

Presenter:

ESR14: Pablo Amil, UPC

Co-authors:

Fabián Reyes-Manzano (UPIITA, SEPI), Lev Guzmán-Vargas (UPIITA, SEPI), Irene Sendiña-Nadal(UPM) and Cristina Masoller (UPC)

Biophysics by the sea, Alcúdia, Mallorca, October 12, 2018

Outline

Fundus images analysis

Segmentation Result

Network analysis

Network-based features

Results



Screening ocular pathologies

Glaucoma and diabetic retinopathy are two of the top-5 most prevalent causes of visual impairment.

Its screening and fast early diagnosis is very important, automatic tools for detecting such pathologies could be very useful to optimize the oculists time.


Our work

We propose fully unsupervised methods for classification of images according to the patients pathology.

We analyze the images and retrieve information about the vessel network connectivity.

Then we use complex-network tools and nonlinear dimensionality reduction techniques to extract features from each image.

We show how this novel features are useful for classification.


The data

We used a public database with 15 healthy subjects, 15 glaucomatous, and 15 with diabetic retinopathy.

For every subject there is a fundus photography available as well as a manual segmentation of the vessel network done by a human expert.


Start: Retinal fundus photography


Vessel network segmentation result


Segmentation result

Information retrieved:

Vessel connectivity

Vessels’ length

Vessels’ width


Analysis: Path example


Analysis: Weighted degree distribution

We set all the links in the networks to have strength equal to the with of the vessel.

We calculated the weighted degree distribution, a.k.a. strength distribution: 𝑃𝑊𝐷𝐷 𝑠 , the fraction of nodes whose sum of weights over all its links is 𝑠.

We compared all the distributions 𝑃𝑊𝐷𝐷 𝑠 from images in the database using the Jensen-Shannon divergence, and using nonlinear dimensionality reduction we extracted 2 features for each image.


Result with automatic segmentation


Result with manual segmentation


Analysis: Central mean weight distribution

We set all the links in the networks to have strength equal to 𝑙 𝑎2. Where 𝑙 is length of the vessel, and 𝑎 is its width

We calculated the Central mean weight distribution:𝑃𝐶𝑀𝑊𝐷 𝑚 , the fraction of nodes whose mean weight in the shortest path to the central node is 𝑚.

We compared all the distributions 𝑃𝐶𝑀𝑊𝐷 𝑚 from images in the database using the Jensen-Shannon divergence, and using nonlinear dimensionality reduction we extracted 2 features for each image.


Result with automatic segmentation


Result with manual segmentation


Other images

We also tested the method with other databases that have lower resolution.

The results show that the presented methods result in features that have statistically significant different values for the different groups.

However, in this cases the features perform poorly for classification purposes.


Conclusions

We demonstrated that these novel network features are useful for analyzing the vessels on the retina.

We were able to achieve perfect classification of healthy and non-healthy patients using high resolution images and manual segmentation. Showing that our network features are useful for classification but our unsupervised segmentation algorithm needs to be improved.

More in a soon to be published paper entitled:“Novel network-based methods for retinal fundus image analysis and classification”


Ongoing work: automatic outlier detection

We are working on methods for automatic outlier detection in medical images.

Such methods can be useful to discard images with artifacts from a training database and boost the performance of the methods applied.


Thank you for your attention

[email protected]

UPC

Novel network-based methods for retinal fundus image analysis...

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