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Medical Image Segmentation by Combining Adaptive Artificial Bee Colony and Wavelet Packet Decomposition

  • Muhammad Arif
  • Guojun WangEmail author
  • Oana Geman
  • Jianer Chen
Conference paper
Part of the Communications in Computer and Information Science book series (CCIS, volume 1123)

Abstract

Segmentation of MRI images plays a significant and helpful job in anticipation and treatment preliminaries. Be that as it may, the power in homogeneity, sporadic fringes and one of the most exceedingly terrible pieces of the difference may cause incredible challenges in the pieces of the seeping from brain MRI images. Heaps of specialists have made in therapeutic imaging. We proposed the novel technique for image segmentation. Our technique depends on the discrete wavelet packet decomposition and ant colony optimization to reduce the disadvantage of the conventional computations in handling of the surprising shapes in restorative images preparing. To improve the exhibition of our proposed procedure we utilize the artificial bee colony to optimize and classify the feature selected or extracted by the WPD. Results shows that our method perform better to segment the curvy shapes and haemorrhagic areas in MRI images.

Keywords

Segmentation MRI Brain Classification Optimization 

Notes

Acknowledgments

This work was supported in part by the National Natural Science Foundation of China under Grant 61632009, in part by the Guangdong Provincial Natural Science Foundation under Grant 2017A030308006, and in part by the High-Level Talents Program of Higher Education in Guangdong Province under Grant 2016ZJ01, and in part by National Natural Science Foundation of China under Grant 61872097.

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Copyright information

© Springer Nature Singapore Pte Ltd. 2019

Authors and Affiliations

  1. 1.School of Computer ScienceGuangzhou UniversityGuangzhouChina
  2. 2.Department of Health and Human DevelopmentStefan cel Mare University SuceavaSuceavaRomania

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