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Comparison of vessel enhancement algorithms applied to time‐of‐flight MRA images for cerebrovascular segmentation
Author(s) -
Phellan Renzo,
Forkert Nils D.
Publication year - 2017
Publication title -
medical physics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.473
H-Index - 180
eISSN - 2473-4209
pISSN - 0094-2405
DOI - 10.1002/mp.12560
Subject(s) - segmentation , algorithm , computer science , medical imaging , computer vision , image segmentation , image enhancement , artificial intelligence , image (mathematics)
Purpose Vessel enhancement algorithms are often used as a preprocessing step for vessel segmentation in medical images to improve the overall segmentation accuracy. Each algorithm uses different characteristics to enhance vessels, such that the most suitable algorithm may vary for different applications. This paper presents a comparative analysis of the accuracy gains in vessel segmentation generated by the use of nine vessel enhancement algorithms: Multiscale vesselness using the formulas described by Erdt (MSE), Frangi (MSF), and Sato (MSS), optimally oriented flux (OOF), ranking orientations responses path operator (RORPO), the regularized Perona–Malik approach (RPM), vessel enhanced diffusion (VED), hybrid diffusion with continuous switch (HDCS), and the white top hat algorithm (WTH). Methods The filters were evaluated and compared based on time‐of‐flight MRA datasets and corresponding manual segmentations from 5 healthy subjects and 10 patients with an arteriovenous malformation. Additionally, five synthetic angiographic datasets with corresponding ground truth segmentation were generated with three different noise levels (low, medium, and high) and also used for comparison. The parameters for each algorithm and subsequent segmentation were optimized using leave‐one‐out cross evaluation. The Dice coefficient, Matthews correlation coefficient, area under the ROC curve, number of connected components, and true positives were used for comparison. Results The results of this study suggest that vessel enhancement algorithms do not always lead to more accurate segmentation results compared to segmenting nonenhanced images directly. Multiscale vesselness algorithms, such as MSE, MSF, and MSS proved to be robust to noise, while diffusion‐based filters, such as RPM, VED, and HDCS ranked in the top of the list in scenarios with medium or no noise. Filters that assume tubular‐shapes, such as MSE, MSF, MSS, OOF, RORPO, and VED show a decrease in accuracy when considering patients with an AVM, because vessels may vary from its tubular‐shape in this case. Conclusions Vessel enhancement algorithms can help to improve the accuracy of the segmentation of the vascular system. However, their contribution to accuracy has to be evaluated as it depends on the specific applications, and in some cases it can lead to a reduction of the overall accuracy. No specific filter was suitable for all tested scenarios.