ArticleInternational journal of computer assisted radiology and surgery2019
Automatic detection of intracranial aneurysm using LBP and Fourier descriptor in angiographic images.
Article in International journal of computer assisted radiology and surgery, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers, 1 of them a synthesis that pooled it.
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2 citing papers in PubMed, 1 synthesis or guideline pooled it, 11 citations in OpenAlex.
- Detection of cerebral aneurysms using artificial intelligence: a systematic review and meta-analysis.Journal of neurointerventional surgery · 2023Pooled it
- A deep learning framework for intracranial aneurysms automatic segmentation and detection on magnetic resonance T1 images.European radiology · 2024Article
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Authors and funding
4 authors at 1 institution in 1 country.
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No grant is acknowledged in the PubMed record.
Abstract
purposeIntracranial 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.
methodsHere, 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.
resultsWe 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.
conclusionThis 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.
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