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Proceedings Paper

Brain image segmentation based on improved BP-Adaboost neural network
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Paper Abstract

The segmentation of medical image applying in medical anatomy plays an important role in various application. So, the study of medical image processing is very important and necessary. Due to the presence of noise and complexity of structure, the existing methods have various shortcomings and the performances are not ideal. In this study, we propose a new method which based on back propagation (BP) neural network and AdaBoost algorithm. The BP neural network we created is 1-7-1 structure. then we trained the system by Gravitational search algorithm (Here, we use the segmented images which were obtained by classic fuzzy c-means algorithm as the ideal output data). Based on this, we established and trained 10 groups of BPNN (We also call it as weak classifier) by applying 10 groups of different data. subsequently, we adopted the AdaBoost algorithm to obtain the weight of each BPNN. Finally, we made up a new BPAdaboost system for image segmentation. In this experiment, we used one group of datasets: Brain MRI. A comparison with the conventional segmentation method through subjective observation and objective evaluation indexes reveals that the proposed method achieved better results based on brain image segmentation.

Paper Details

Date Published: 27 March 2019
PDF: 7 pages
Proc. SPIE 11050, International Forum on Medical Imaging in Asia 2019, 110500Q (27 March 2019); doi: 10.1117/12.2521246
Show Author Affiliations
Zhen Chao, Yonsei Univ. (Korea, Republic of)
Dohyeon Kim, Yonsei Univ. (Korea, Republic of)
Hee-joung Kim, Yonsei Univ. (Korea, Republic of)

Published in SPIE Proceedings Vol. 11050:
International Forum on Medical Imaging in Asia 2019
Feng Lin; Hiroshi Fujita; Jong Hyo Kim, Editor(s)

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