Medical Image Segmentation Based on Fractional-Order Derivative

DOI

10.2991/ap3er-15.2015.107How to use a DOI?

Keywords

medical image; image segmentation; fractional-order; frequency characteristic; Sobel operator

Abstract

Objective: In the traditional edge detection differential operators, the first-order derivative masks are easy to loss image details information, and the second-order derivative masks are more sensitive to noise. As for these problems, this paper proposes a fractional-order mask for medical image segmentation. Methods: Combining the frequency characteristic and the memorability of fractional differential, the classical first-order Sobel operator is generalized to fractional-order mode; A fractional-order differential mask is constructed for extracting the edge feature of medical images. Results: The experiment results show that compared with the integer order differential mask, the fractional-order differential mask can detect more edge details feature of the medical images, and is more robust to noise. Conclusion: Based on the global characteristic of the fractional differential, the proposed fractional-order Sobel mask can extract more image edge feature details. Experiment results show that the proposed fractional-order mask yields good visual effects for brain MRI image segmentation.

Copyright

© 2015, the Authors. Published by Atlantis Press.

Open Access

This is an open access article distributed under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).

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TY  - CONF
AU  - Dan Tian
AU  - Dapeng Li
AU  - Yingxin Zhang
PY  - 2015/06
DA  - 2015/06
TI  - Medical Image Segmentation Based on Fractional-Order Derivative
BT  - Proceedings of the 2015 Asia-Pacific Energy Equipment Engineering Research Conference
PB  - Atlantis Press
SP  - 453
EP  - 456
SN  - 2352-5401
UR  - https://doi.org/10.2991/ap3er-15.2015.107
DO  - 10.2991/ap3er-15.2015.107
ID  - Tian2015/06
ER  -