Abstract
Aiming to feature redundancy problem in MRI Prostate Tumor ROI high dimension representation, a model, Prostate Tumor CAD Model based on NN with PCA feature-level fusion in MRI, is proposed in this paper. Firstly, geometry feature, statistical features, Hu invariant moment features, GLCM texture features, TAMURA texture features, frequency features are extracted from MRI prostate tumor ROI; Secondly PCA are used to obtain 8 dimension features in cumulative contribution rate 89.62%, and reducing the dimension of the feature vectors; Thirdly neural network is regarded as classifier to classify with BFGS, Levenberg-Marquardt, BP and GD training algorithm, Finally, MRI images of prostate patients are regarded as original data, prostate tumor CAD model based on NN with feature-level fusion are utilized to aid diagnosis. Experiment results illustrate that the ability to identify benign and malignant prostate tumor are improved at least 10% through Neural network with PCA feature-level fusion, and the strategy is effective, redundancy among features are reduces in some degree. There are positive significance for MRI prostate tumor CAD.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
References
Shi, F., Wei, J., Wang, Z.: High-field magnetic resonance imaging characteristics of normal and benign prostatic hyperplasia. Chin. J. Geriatr. 4(16), 79–83 (1997)
Hua, L., Ju, X., Fei, W., et al.: The expression of androgen receptor in benign prostatic hyperplasia and prostate cancer. Chin. J. Geriatr. 22(7), 405–408 (2003)
Yang, Z.: Computer-aided diagnosis of prostate lesions based on ultrasound images. University of Science and Technology of China Ph.D. thesis, Hefei (2009)
Doi, K.: Computer-aided diagnosis in medical imaging: historical review, current status and future potential. Comput. Med. Imaging Graph. 31(4), 198–211 (2007)
Niaf, E., Rouvière, O., Mège-Lechevallier, F., et al.: Computer-aided diagnosis of prostate cancer in the peripheral zone using multiparametric MRI. Phys. Med. Biol. 57(12), 3833–3851 (2012)
Llobet, R., Toselli, A.H., Perez-Cortes, J.C., Juan, A.: Computer-aided prostate cancer detection in ultrasonographic images. In: Perales, F.J., Campilho, A.J.C., de la Blanca, N.P., Sanfeliu, A. (eds.) IbPRIA 2003. LNCS, vol. 2652, pp. 411–419. Springer, Heidelberg (2003). doi:10.1007/978-3-540-44871-6_48
Zöllner, F.G., Emblem, K.E., Schad, L.R.: SVM-based glioma grading: optimization by feature reduction analysis. Z. für Med. Phys. 22(3), 205–214 (2012)
Liu, H., Mei, G.D., Liu, X.: Cirrhosis classification based on MRI with duplicative-feature support vector machine (DFSVM). Biomed. Signal Process. Control 8(4), 346–353 (2013)
Langley, P.: Selection of relevant features in machine learning. In: Proceedings of the AAAI Fall Symposium on Relevance, Menlo Park, CA, pp. 140–144. AAAI Press (1994)
Zhuo, L., Yang, M.: Using PCA algorithm arbors hyperspectral data dimensionality reduction and classification. (2013). http://www.cnki.net/kcms/detail/11.4415.P.20130603.1602.003.htm
Zhou, T., Lu, H.: Multi-features prostate tumor aided diagnoses based on ensemble-SVM. In: Proceedings of IEEE International Conference on Granular Computiong 2013, Bei**g China, pp. 297–302 (2013)
Acknowledgements
This work is supported by the Natural Science Fund of China (Grant Nos. 61561040, 81160183), Scientific Research Fund of Ningxia Education Department (Grant No. NGY2016084).
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2017 Springer International Publishing AG
About this paper
Cite this paper
Lu, H., Zhou, T., Shi, H. (2017). CAD Model Based on NN and PCA in Prostate Tumor MRI. In: Sun, Y., Lu, H., Zhang, L., Yang, J., Huang, H. (eds) Intelligence Science and Big Data Engineering. IScIDE 2017. Lecture Notes in Computer Science(), vol 10559. Springer, Cham. https://doi.org/10.1007/978-3-319-67777-4_50
Download citation
DOI: https://doi.org/10.1007/978-3-319-67777-4_50
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-67776-7
Online ISBN: 978-3-319-67777-4
eBook Packages: Computer ScienceComputer Science (R0)