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

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ID: 46636
2019
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Abstract
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.
Reference Key
chang2019improvinghealthcare Use this key to autocite in the manuscript while using SciMatic Manuscript Manager or Thesis Manager
Authors Chang, Yuchou;
Journal healthcare technology letters
Year 2019
DOI
10.1049/htl.2018.5031
URL
Keywords
image classification medical images mra image vessel extraction biomedical mri blood vessels ensemble learning extracting vessels image sampling learning (artificial intelligence) magnetic resonance angiography image medical image processing nonvessel tissue p-tile algorithm resampling learning surgical planning traditional otsu method

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