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

• blood vessels
• learning (artificial intelligence)
• medical image processing
• biomedical MRI
• image classification
• image sampling
• nonvessel tissue
• traditional Otsu method
• extracting vessels
• MRA image vessel extraction
• medical images
• blood vessels
• magnetic resonance angiography image
• ensemble learning
• resampling learning
• surgical planning
• p-tile algorithm