Brain image segmentation based on improved BP-Adaboost neural network

Zhen Chao; Dohyeon Kim; Hee-joung Kim

doi:10.1117/12.2521246

27 March 2019 Brain image segmentation based on improved BP-Adaboost neural network

Zhen Chao, Dohyeon Kim, Hee-joung Kim

Proceedings Volume 11050, International Forum on Medical Imaging in Asia 2019; 110500Q (2019) https://doi.org/10.1117/12.2521246
Event: 2019 Joint International Workshop on Advanced Image Technology (IWAIT) and International Forum on Medical Imaging in Asia (IFMIA), 2019, Singapore, Singapore

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.

Citation Download Citation

Zhen Chao, Dohyeon Kim, and Hee-joung Kim "Brain image segmentation based on improved BP-Adaboost neural network", Proc. SPIE 11050, International Forum on Medical Imaging in Asia 2019, 110500Q (27 March 2019); https://doi.org/10.1117/12.2521246

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