Fast segmentation of ultrasound images by incorporating spatial information into Rayleigh mixture model. Issue 12 (4th October 2017)

• Record Type: Journal Article
• Authors: Bi, Hui; Tang, Hui; Yang, Guanyu; Li, Baosheng; Shu, Huazhong; Dillenseger, Jean‐Louis
• Is Part Of: IET image processing. Volume 11:Issue 12(2017)
• Page Start: 1188
• Page End: 1196
• Publication Date: 2017-10-04
• Subjects: ultrasonic imaging -- image segmentation -- medical image processing -- biomedical ultrasonics; spatial information -- Rayleigh mixture model -- fast segmentation -- finite mixture model -- medical ultrasound image segmentation -- intensity distribution -- inhomogeneous regions -- improved RMM‐neighbour information -- improved RMMN information -- mean template -- spatial information incorporation -- window size -- local gradient distribution -- high‐intensity focused ultrasound therapy -- segmentation accuracy -- computation time; Image processing -- Periodicals; 621.36705
• DOI: 10.1049/iet-ipr.2017.0166
• Languages: English
• ISSNs: 1751-9659

Abstract

Abstract : 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.