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Using Fuzzy Inference system for detection the edges of Musculoskeletal Ultrasound Images

Ibraheem Jabbar, Shaima; Day, Charles R.; Chadwick, Edward K.

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Authors

Shaima Ibraheem Jabbar

Edward K. Chadwick



Abstract

Edge detection in Musculoskeletal Ultrasound Imaging readily allows an ultrasound image to be rendered as a binary image. This facilitates automated measurement of geometric parameters, such as muscle thickness, circumference and cross-sectional area of the tendon. In this work, we introduced a new method of edge detection based on a fuzzy inference system and apply it to the ultrasound image. An anisotropic diffusion filter was used to reduce speckle noise before implementation of the edge detection method, which consists of three characteristic steps. The first step entailed fuzzification, for which three fuzzy membership functions were applied to the image. The parameters of these functions were selected based on an analysis of the standard deviation of grey level intensities in the image. Secondly, 12 fuzzy rules for identifying edges were constructed. Thirdly, defuzzification was carried out using the Takagi-Sugeno method. Furthermore, a reference-based edge measurement was quantitatively determined by comparing edge characteristics with a standard reference. We made two inferences from our observations. Firstly, the ability to automatically identify the important details of a musculoskeletal ultrasound image in a very short time is possible. Secondly, this method is effective compared with other methods.

Start Date Jun 23, 2019
End Date Jun 26, 2019
Acceptance Date Mar 18, 2019
Publication Date Oct 10, 2019
Publicly Available Date Mar 29, 2024
Publisher Institute of Electrical and Electronics Engineers (IEEE)
Series Title FUZZ-IEEE 2019
Book Title 2019 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE)
ISBN 978-1-5386-1729-8
DOI https://doi.org/10.1109/FUZZ-IEEE.2019.8858971
Keywords edge detection, fuzzy inference technique,musculoskeletal ultrasound imaging, musculoskeletal modelling
Publisher URL http://doi.org/10.1109/FUZZ-IEEE.2019.8858971

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