Brain image pathological segmentation method based on a deep network

Shuzhan Zheng; Gang Xu; Nana Fu

doi:10.1117/12.2636666

10 June 2022 Brain image pathological segmentation method based on a deep network

Shuzhan Zheng, Gang Xu, Nana Fu

Proceedings Volume 12179, Second International Conference on Medical Imaging and Additive Manufacturing (ICMIAM 2022); 121790A (2022) https://doi.org/10.1117/12.2636666
Event: Second International Conference on Medical Imaging and Additive Manufacturing (ICMIAM 2022), 2022, Xiamen, China

Abstract

The automation of the medical industry has become one of the necessary conditions in today's medical field. Radiologists or physicians need this automation technology for accurate diagnosis and treatment. Automatic segmentation of tumor parts from magnetic resonance (MR) brain images is a challenging task. Several methods have been developed to improve the segmentation efficiency of automation systems. However, in the process of medical image segmentation, there is always room for improvement. This paper proposes a brain tumor image segmentation method based on deep learning, which includes the concepts of stationary wavelet transform (SWT) and growing convolutional neural network (GCNN). The vital goal of this work is to improve the accuracy of traditional systems. In this paper, the support vector machine (SVM) and convolutional neural network (CNN) are compared and analyzed. The experimental results show that this method is superior to SVM and CNN inaccuracy, peak signal-to-noise ratio, mean square error, and other performance indicators.

Citation Download Citation

Shuzhan Zheng, Gang Xu, and Nana Fu "Brain image pathological segmentation method based on a deep network", Proc. SPIE 12179, Second International Conference on Medical Imaging and Additive Manufacturing (ICMIAM 2022), 121790A (10 June 2022); https://doi.org/10.1117/12.2636666

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KEYWORDS

Image segmentation

Brain

Tumors

Neuroimaging

Magnetic resonance imaging

Image processing

Feature extraction

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