SEGMENTATION OF MAJOR VESSEL IN X-RAY ANGIOGRAPHIC IMAGES WITH IMAGE PROCESSING TECHNIQUE

Recently, image processing techniques, particularly hypertension, diabetes, and cardiovascular risk in a wide variety of vascular disorders such as disease, target images abnormalities problems, early detection is very important that the time factor to explore and is used in various medical fields for image improvement in the medical treatment. There are many methods to detect vascular from x-ray angiographic images. The proposed method, where image quality and accuracy are the key factors of research, depends on the process of image quality assessment and enhancement, as the enhancement phase where image processing methods are used with frangi filter. The segmentation principles, and improved region of the object of interest is obtained, which is used as the basis for feature extraction. Normality comparison is made based on general features. The coronary angiographic images are obtained in literatüre and discuss how the image manipulation can be done to achieve better results from the angiographic images through various image processing with frangi filter in this research. The proposed method includes cancer and vessel structures with frangi filter, enhancement of the image and finally segmentation of the images.

___

  • [1] Rubin GD, Leipsic J, Joseph Schoepf U, Fleischmann D, Napel S. CT angiography after 20 years: A transformation in cardiovascular disease characterization continues to advance. Radiology. 2014; 271: 633-52.
  • [2] Kumamaru KK, Hoppel BE, Mather RT, Rybicki FJ. CT angiography: Current technology and clinical use. Radiol Clin North Am. 2010;48:213-35.
  • [3] Roos JE, Fleischmann D, Koechl A, Rakshe T, Straka M, Napoli A, Kanitsar A, Sramek M, Groeller E. Multipath curved planar reformation of the peripheral arterial tree in CT angiography. Radiology. 2007; 244:281-90.
  • [4] Yang S, Kweon J, Roh JH. et al. Deep learning segmentation of major vessels in X-ray coronary angiography. Sci Rep 9. 2019;9:16897.
  • [5] Nasr-Esfahani E, Karimi N, Jafari MH, Soroushmehr SMR, Samavi S, Nallamothu BK, Najarian K. Segmentation of vessels in angiograms using convolutional neural networks. Biomed. Signal Process Control. 2018;40:240–251. [6] Jo K, Kweon J, Kim YH, Choi J. Segmentation of the main vessel of the left anterior descending artery using selective feature mapping in coronary angiography. IEEE Access. 2019;7:919–930.
  • [7] Chaudhuri S, Chatterjee S, Katz N, Nelson M, Goldbaum M. Detection of blood vessels in retinal images using two-dimensional matched filters. IEEE Trans. Med. Imaging. 1989;8:263–269.
  • [8] Frangi AJ, Niessen WJ, Vincken KL, Viergever MA. Multiscale vessel enhancement filtering” in Medical Image Computing and Computer-Assisted Interventation. MICCAI’98. 1998;1496:130–137.
  • [9] Jiang X, Mojon D. Adaptive local thresholding by verification based multi threshold probing with application to vessel detection in retinal images. IEEE Trans. Pattern Anal. Mach. Intell. 2003; 25(1):131–137.
  • [10] Adel M, Moussaoui A, Rasigni M, Bourennane S, Hamami L. Statistical-Based Tracking Technique for Linear Structures Detection: Application to Vessel Segmentation in Medical Images. IEEE Signal Process. Lett. 2010;17(6):555–558.
  • [11] Fraz MM, Basit A, Remagnino P, Hoppe A, Barman SA. Retinal vasculature segmentation by morphological curvature, reconstruction and adapted hysteresis thresholding. 2011 7th International Conference on Emerging Technologies; 5-6 September 2011; Islamabad, Pakistan, 1-6.
  • [12] Wang F, Behrooz A, Morris M, Adibi B. High-contrast subcutaneous vein detection and localization using multispectral imaging. Brain Imaging Behav. 2013;18(5):50504.
  • [13] Ma H, Hoogendoorn A, Regar E, Niessen WJ, van Walsum T. Automatic online layer separation for vessel enhancement in X-ray angiograms for percutaneous coronary interventions. Med Image Anal. 2017; 39:145-161.
  • [14] Hao H, Ma H, van Walsum T. Vessel layer separation in x-ray angiograms with fully convolutional network. Proc. SPIE, Medical Imaging 2018: Image-Guided Procedures, Robotic Interventions, and Modeling, 2018; 10576:105761V.