access icon free Fast segmentation of ultrasound images by incorporating spatial information into Rayleigh mixture model

© The Institution of Engineering and Technology
Received 15/02/2017, Accepted 07/06/2017, Revised 24/05/2017, Published 27/06/2017

As a particular case of the finite mixture model, Rayleigh mixture model (RMM) is considered as a useful tool for medical ultrasound (US) image segmentation. However, conventional RMM relies on intensity distribution only and does not take any spatial information into account that leads to misclassification on boundaries and inhomogeneous regions. The authors proposed an improved RMM with neighbour (RMMN) information to solve this problem by introducing neighbourhood information through a mean template. The incorporation of the spatial information made RMMN more robust to noise on the boundaries. The size of the window which incorporates neighbour information was resized adaptively according to the local gradient distribution. They evaluated their model on experiments on synthetic data and real US images used by high-intensity focused ultrasound therapy. On this data, they demonstrated that the proposed model outperforms several state-of-the-art methods in terms of both segmentation accuracy and computation time.

Inspec keywords: ultrasonic imaging; medical image processing; biomedical ultrasonics; image segmentation

Other keywords: local gradient distribution; intensity distribution; mean template; medical ultrasound image segmentation; fast segmentation; Rayleigh mixture model; segmentation accuracy; improved RMM-neighbour information; finite mixture model; inhomogeneous regions; computation time; window size; spatial information incorporation; high-intensity focused ultrasound therapy; spatial information; improved RMMN information

Subjects: Sonic and ultrasonic radiation (biomedical imaging/measurement); Biology and medical computing; Computer vision and image processing techniques; Sonic and ultrasonic radiation (medical uses); Patient diagnostic methods and instrumentation; Optical, image and video signal processing

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