Semi-automatic detection and segmentation algorithm of saccular aneurysms in 2D cerebral DSA images

To detect and segment cerebral saccular aneurysms (CSAs) in 2D Digital Subtraction Angiography (DSA) images. Ten patients underwent Intra-arterial DSA procedures. Patients were injected with Iodine-containing radiopaque material. A scheme for semi-automatic detection and segmentation of intracranial...

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Bibliographic Details
Published in:Egyptian journal of radiology and nuclear medicine Vol. 47; no. 3; pp. 859 - 865
Main Authors: Sulayman, Nisreen, Al-Mawaldi, Moustafa, Kanafani, Qosai
Format: Journal Article
Language:English
Published: Elsevier B.V 01.09.2016
SpringerOpen
Subjects:
Digital subtraction angiography
Image enhancement
Image segmentation
Saccular aneurysms
Shape features
Digital subtraction angiography
Image segmentation
Shape features
Saccular aneurysms
Image enhancement
ISSN:0378-603X
Online Access:Get full text
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Summary:To detect and segment cerebral saccular aneurysms (CSAs) in 2D Digital Subtraction Angiography (DSA) images. Ten patients underwent Intra-arterial DSA procedures. Patients were injected with Iodine-containing radiopaque material. A scheme for semi-automatic detection and segmentation of intracranial aneurysms is proposed in this study. The algorithm consisted of three major image processing stages: image enhancement, image segmentation and image classification. Applied to the 2D Digital Subtraction Angiography (DSA) images, the algorithm was evaluated in 19 scene files to detect 10 CSAs. Aneurysms were identified by the proposed detection and segmentation algorithm with 89.47% sensitivity and 80.95% positive predictive value (PPV) after executing the algorithm on 19 DSA images of 10 aneurysms. Results have been verified by specialized radiologists. However, 4 false positive aneurysms were detected when aneurysms’ location is at Anterior Communicating Artery (ACA). The suggested algorithm is a promising method for detection and segmentation of saccular aneurysms; it provides a diagnostic tool for CSAs.
ISSN:0378-603X
DOI:10.1016/j.ejrnm.2016.03.016