Improving the Otsu method for MRA image vessel extraction via resampling and ensemble learning.
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.
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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46636
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0x95644003c57E6F55A65596E3D9Eac6813e3566dA
Article ID:
46636
Unique Identifier:
10.1049/htl.2018.5031
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Scimatic Chain (ID: 481)
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