3D segmentation of the whole heart vasculature using improved multi-threshold Otsu and white top-hat scale space hessian based vessel filter

Bukenya, Faiza; Ehling, Josef; Kalema, Abdu Kiweewa; Eyoh, Imo; Robert, John; Bai, Li

Abstract

Quantification of vessel density helps to know the stage of the disease during diagnosis and patient's response to treatment. However, this requires presence of all vessels in the image. The available segmentation techniques that are manual based are prone to errors, tiresome and slow, while some that are automated do face difficulty in distinguishing the vessel tissue from the non-vessel tissue due to the presence of intensity inhomogeneity and noise in images. Therefore, there is need for improved segmentation methods that can extract all sizes of vessels for better quantification of the vessel density and improved decision making during diagnosis. In this paper, a 3D hybrid approach for segmentation has been developed, based on white top hat scale space hessian vessel enhancement filter and multi-threshold Otsu method. The hybrid method can address the intensity inhomogeneity, as a result, more vessels of different sizes are detected. The method is also robust and able to detect abnormalities in the vessels.

Citation

Bukenya, F., Ehling, J., Kalema, A. K., Eyoh, I., Robert, J., & Bai, L. (2016, December). 3D segmentation of the whole heart vasculature using improved multi-threshold Otsu and white top-hat scale space hessian based vessel filter. Presented at 2016 IEEE Symposium Series on Computational Intelligence (SSCI), Athens, Greece

• Presentation Conference Type: Edited Proceedings
• Conference Name: 2016 IEEE Symposium Series on Computational Intelligence (SSCI)
• Start Date: Dec 6, 2016
• End Date: Dec 9, 2016
• Acceptance Date: Oct 31, 2016
• Online Publication Date: Feb 13, 2017
• Publication Date: 2016-12
• Deposit Date: Aug 5, 2020
• Publicly Available Date: Aug 5, 2020
• Publisher: Institute of Electrical and Electronics Engineers
• Pages: 1-7
• Book Title: Proceedings - 2016 IEEE Symposium Series on Computational Intelligence (SSCI)
• ISBN: 978-1-5090-4241-8
• DOI: https://doi.org/10.1109/SSCI.2016.7850137
• Public URL: https://nottingham-repository.worktribe.com/output/4812320
• Publisher URL: https://ieeexplore.ieee.org/abstract/document/7850137
• Additional Information: © 2017 IEEE.Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes,creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.