Improving the Otsu method for MRA image vessel extraction via resampling and ensemble learning

Yuchou Chang

Improving the Otsu method for MRA image vessel extraction via resampling and ensemble learning

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Author(s): Yuchou Chang¹
- Affiliations: 1: Computer Science and Engineering Technology Department , University of Houston-Downtown , Houston 77002 , USA
Source: Volume 6, Issue 4, August 2019, p. 115 – 120
DOI: 10.1049/htl.2018.5031 , Online ISSN 2053-3713

This is an open access article published by the IET under the Creative Commons Attribution -NonCommercial License (http://creativecommons.org/licenses/by-nc/3.0/)

Received 27/05/2018, Accepted 05/03/2019, Revised 30/01/2019, Published 17/07/2019

Accurate extraction of vessels plays an important role in assisting diagnosis, treatment, and surgical planning. The Otsu method has been used for extracting vessels in medical images. However, blood vessels in magnetic resonance angiography (MRA) image are considered as a sparse distribution. Pixels on vessels in MRA image are considered as an imbalanced data in classification of vessels and non-vessel tissues. To extract vessels accurately, a novel method using resampling technique and ensemble learning is proposed for solving the imbalanced classification problem. Each pixel is sampled multiple times through multiple local patches within the image. Then, vessel or non-vessel tissue is determined by the ensemble voting mechanism via a p-tile algorithm. Experimental results show that the proposed method is able to outperform the traditional Otsu method by extracting vessels in MRA images more accurately.

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Improving the Otsu method for MRA image vessel extraction via resampling and ensemble learning

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