Abstract
In [1], a variant version of the fuzzy cellular neural network, called FCNN, is proposed to effectively segment microscopic white blood cell images. However, when applied to the segmentation of serial CT liver images, it does not work well. In this paper, FCNN is improved to be the novel neural network —Advanced Fuzzy Cellular Neural Network AFCNN. Just like FCNN, AFCNN still keeps its convergent property and global stability. When applied to segment serial CT liver images, AFCNN has the distinctive advantage over FCNN: it can keep boundary integrity better and have better recall accuracies such that the segmented images can approximate original liver images better.
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References
Shitong, W., Min, W.: A New Algorithm NDA Based on Fuzzy Cellular Neural Networks for White Blood Cell Detection. IEEE Trans. Information Technologies in Biomedicine (accepted with the revised version)
Duan, F., Shitong, W., et al.: Improved Fuzzy Cellular Neural Network IFCNN and Its Application in White Blood Cell Detection. Chinese J. Control and Decision (Accepted)
Lirong, Y., Dewen, H., et al.: Image Segmentation of Serial CT liver Image Based on the Deformable Model. J. of SanXia University 24(6), 529–532 (2002)
Lee, W.L., Hsieh, K.S., et al.: A Study of Ultrasonic Liver Images Classification with Artificial Neural Networks Based on Fractal Geometry and Multiresolution Analysis. Biomedica Engineering-Applications, Basis & Communications 16(2), 59–67 (2004)
Shitong, W., Hongjun, L.: On new fuzzy morphological associative memories. IEEE Trans. Fuzzy systems 12(3), 316–323 (2004)
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© 2005 Springer-Verlag Berlin Heidelberg
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Wang, S., Fu, D., Xu, M., Hu, D. (2005). Applying Advanced Fuzzy Cellular Neural Network AFCNN to Segmentation of Serial CT Liver Images. In: Wang, L., Chen, K., Ong, Y.S. (eds) Advances in Natural Computation. ICNC 2005. Lecture Notes in Computer Science, vol 3612. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11539902_142
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DOI: https://doi.org/10.1007/11539902_142
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-28320-1
Online ISBN: 978-3-540-31863-7
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