Recognition of Region of Interest from Facial Nerve Image Sequences

doi:10.3969/j.issn.1006-2475.2017.11.010

Abstract

Abstract: The computer recognition of facial nerve MR image sequences in the region of interest, can help doctors identify specific functional areas in the surgical planning process and performe the operation more targeted to avoid additional damage of facial nerve area. In this paper, facial nerve MR images are segmented into different functional areas, and a large number of case samples are studied by convolutional neural network, and the recognition model with high accuracy is obtained. The experimental results show that the convolutional neural network model can effectively recognize the region of interest in the image recognition of facial nerve, and it has a certain medical assistant effect.

Key words: facial nerve MR image sequences, convolutional neural network, image processing, region of interest in image recognition

CLC Number:

TP391.41

LIN Qin-zhuang1, ZHONG Ying-chun1, LUO Wei-shi2. Recognition of Region of Interest from Facial Nerve Image Sequences[J]. Computer and Modernization, 2017, 0(11): 51-54.

References

[1] Heaton J T, Knox C J, Malo J S, et al. A system for delivering mechanical stimulation and robot-assisted therapy to the rat whisker pad during facial nerve regeneration[J]. IEEE Transactions on Neural Systems and Rehabilitation Engineering, 2013,21(6):928-937.

[2] May M, Schaitkin B M. History of facial nerve surgery[J]. Facial Plastic Surgery, 2000,16(4):301-307.

[3] Lu Ping, Barazzetti L, Chandran V, et al. Facial nerve image enhancement from CBCT using supervised learning technique[C]// Proceedings of the 37th Annual International Conference of the IEEE Engineering in Medicine and Biology Society. 2015:2964-2967.

[4] Bengio Y, Delalleau O. On the expressive power of deep architectures[C]// Proceedings of the 2011 International Conference on Algorithmic Learning Theory. 2011:18-36.

[5] 吕国豪,罗四维,黄雅平,等. 基于卷积神经网络的正则化方法[J]. 计算机研究与发展, 2014,51(9):1891-1900.

[6] Lecun Y, Bottou L, Bengio Y, et al. Gradient-based learning applied to document recognition[J]. Proceedings of the IEEE, 1998,86(11):2278-2324.

[7] Yang Weixin, Jin Lianwen, Liu Manfei. Chinese character-level writer identification using path signature feature, DropStroke and deep CNN[C]// Proceedings of the 13th International Conference on Document Analysis and Recognition. 2015:546-550.

[8] 蔡娟,蔡坚勇,廖晓东,等. 基于卷积神经网络的手势识别初探[J]. 计算机系统应用, 2015,24(4):113-117.

[9] 李岳云,许悦雷,马时平,等. 深度卷积神经网络的显著性检测[J]. 中国图象图形学报, 2016,21(1):53-59.

[10] Tivive F H C, Bouzerdoum A. A new class of convolutional neural networks (SICoNNets) and their application of face detection[C]// Proceedings of the 2003 International Joint Conference on Neural Networks. 2003,3:2157-2162.

[11] Chen Ying-Nong, Han Chin-Chuan, Wang Cheng-Tzu, et al. The application of a convolution neural network on face and license plate detection[C]// Proceedings of the 18th International Conference on Pattern Recognition. 2006,3:552-555.

[12] Ji Shuiwang, Xu Wei, Yang Ming, et al. 3D convolutional neural networks for human action recognition[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2013,35(1):221-231.

[13] 赵志宏,杨绍普,马增强. 基于卷积神经网络LeNet-5的车牌字符识别研究[J]. 系统仿真学报, 2010,22(3):638-641.

[14] 黄琳,张尤赛. 应用深层卷积神经网络的交通标志识别[J]. 现代电子技术, 2015,38(13):101-106.

[15] Sukittanon S, Surendran A C, Platt J C, et al. Convolutional networks for speech detection[C]// Proceedings of the 8th International Conference on Spoken Language Processing. 2004:1077-1080.

[16] Simard P Y, Steinkraus D, Platt J C. Best practices for convolutional neural networks applied to visual document analysis[C]// Proceedings of the 7th International Conference on Document Analysis and Recognition. 2003,2:958-962.

[17] Mobahi H, Collobert R, Weston J. Deep learning from temporal coherence in video[C]// Proceedings of the 26th Annual International Conference on Machine Learning. 2009:737-744.