Open AccessFast segmentation of ultrasound images by incorporating spatial information into Rayleigh mixture model
Author(s) -
Bi Hui,
Tang Hui,
Yang Guanyu,
Li Baosheng,
Shu Huazhong,
Dillenseger JeanLouis
Publication year - 2017
Publication title -
iet image processing
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.401
H-Index - 45
eISSN - 1751-9667
pISSN - 1751-9659
DOI - 10.1049/iet-ipr.2017.0166
Subject(s) - segmentation , computer science , artificial intelligence , image segmentation , pattern recognition (psychology) , spatial analysis , computation , rayleigh distribution , computer vision , algorithm , mathematics , statistics , probability density function
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.