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.
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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).
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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
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DOI: https://doi.org/10.1007/978-3-319-67777-4_50
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