Article

Journal of Digital Imaging

, Volume 21, Issue 4, pp 433-445

First online:

Computerized Analysis of Digital Subtraction Angiography: A Tool for Quantitative In-vivo Vascular Imaging

  • George C. KagadisAffiliated withDepartment of Medical Physics, School of Medicine, University of Patras Email author 
  • , Panagiota SpyridonosAffiliated withDepartment of Medical Physics, School of Medicine, University of Patras
  • , Dimitris KarnabatidisAffiliated withDepartment of Radiology, School of Medicine, University of Patras
  • , Athanassios DiamantopoulosAffiliated withDepartment of Radiology, School of Medicine, University of Patras
  • , Emmanouil AthanasiadisAffiliated withDepartment of Medical Physics, School of Medicine, University of Patras
  • , Antonis DaskalakisAffiliated withDepartment of Medical Physics, School of Medicine, University of Patras
  • , Konstantinos KatsanosAffiliated withDepartment of Radiology, School of Medicine, University of Patras
  • , Dionisios CavourasAffiliated withDepartment of Medical Instrumentation Technology, Technological Education Institute of Athens
  • , Dimitris MihailidisAffiliated withCharleston Radiation Therapy Cons
    • , Dimitris SiablisAffiliated withDepartment of Radiology, School of Medicine, University of Patras
    • , George C. NikiforidisAffiliated withDepartment of Medical Physics, School of Medicine, University of Patras

Rent the article at a discount

Rent now

* Final gross prices may vary according to local VAT.

Get Access

Abstract

The purpose of our study was to develop a user-independent computerized tool for the automated segmentation and quantitative assessment of in vivo-acquired digital subtraction angiography (DSA) images. Vessel enhancement was accomplished based on the concept of image structural tensor. The developed software was tested on a series of DSA images acquired from one animal and two human angiogenesis models. Its performance was evaluated against manually segmented images. A receiver’s operating characteristic curve was obtained for every image with regard to the different percentages of the image histogram. The area under the mean curve was 0.89 for the experimental angiogenesis model and 0.76 and 0.86 for the two clinical angiogenesis models. The coordinates of the operating point were 8.3% false positive rate and 92.8% true positive rate for the experimental model. Correspondingly for clinical angiogenesis models, the coordinates were 8.6% false positive rate and 89.2% true positive rate and 9.8% false positive rate and 93.8% true positive rate, respectively. A new user-friendly tool for the analysis of vascular networks in DSA images was developed that can be easily used in either experimental or clinical studies. Its main characteristics are robustness and fast and automatic execution.

Key words

DSA image processing quantification angiogenesis experimental