Automatic detection of intracranial aneurysm using LBP and Fourier descriptor in angiographic images

  • Ines RahmanyEmail author
  • Mohamed El Arbi Nemmala
  • Nawres Khlifa
  • Houda Megdiche
Original Article



Intracranial aneurysms (IA) are abnormal dilatation of the arteries at the circle of Willis whose rupture can lead to catastrophic complications such as hemorrhagic stroke. The purpose of this work is to detect IA in 2D-DSA images. The proposed detection framework uses local binary patterns for the determination of initial aneurysm candidates and generic Fourier descriptor (GFD) for false positive removal.


Here, the designed framework takes DSA images including IA as input and produces images where the IA is clearly identified and localized. The multi-step approach is defined as the following: The first phase presents the determination of initial aneurysm candidates using the uniform local binary patterns (LBPs). The LBPs are calculated from these images in order to identify texture contents of both aneurysm and no-aneurysm classes. The second phase presents the false positives removal using a shape descriptor based on contours: the GFD.


We demonstrated that the proposed detection method successfully recognized morphological features of intracranial aneurysm. The results demonstrated excellent agreement between manual and automated detections. With the computerized IA detection framework, all aneurysms were correctly detected with zero false negative and low FP rates.


This study shows the potential of LBP and GFD as a feature descriptors and paves the way for a whole image analysis tool to predict intracranial aneurysm risk of rupture.


Intracranial aneurysm Computer-aided detection DSA LBP Fourier descriptor 



The results published here are wholly based upon data provided by the Radiology and Medical Images Department of Soukra Clinic, Tunisia.

Compliance with ethical standards

Conflict of interest

The authors declare that they have no conflict of interest.

Ethical approval

This article does not contain any studies with human participants performed by any of the authors.

Informed consent

This article does not contain patient data.


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Copyright information

© CARS 2019

Authors and Affiliations

  1. 1.Université de Tunis El ManarTunisTunisia

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