Application of neural network to liver magnetic-resonance-imaging study

Chin-Sing Ong; Wei-Kom Chu; Joseph C. Anderson; Hon-Wei Syh

doi:10.1117/12.139982

16 September 1992 Application of neural network to liver magnetic-resonance-imaging study

Chin-Sing Ong, Wei-Kom Chu, Joseph C. Anderson, Hon-Wei Syh

Proceedings Volume 1709, Applications of Artificial Neural Networks III; (1992) https://doi.org/10.1117/12.139982
Event: Aerospace Sensing, 1992, Orlando, FL, United States

Abstract

Magnetic resonance imaging (MRI) of the liver has demonstrated to be quite sensitive in showing Hepatic Hemangioma as high intensity lesions in T2 weighted imaging sequence. Hepatic Hemangioma is a non-malignant tumor and has relative high occurrence rate among the general population. It is of importance to differentiate this benign abnormality from other high intensity malignant lesions, such as hepatoma, adenocarcinoma, or metastasis. The objective of our study was to investigate the feasibility of applying neural network to assist in the differentiation of the liver MRI lesions. Thirty-seven liver MRI studies were collected, this including twenty-three cases of hepatic hemangioma and fourteen cases of malignant tumors. all cases were clinically proven with the diagnosed pathological condition and verified by biopsy. Four quantitative features, adopted from published literatures and used clinically on a routine basis, were measured from MRI images. In this study, a multilayer and two layer backpropagation networks were used for performance comparison. By attempting various training methods, the accuracy of the two layer network had been improved from 74% to 83% by selecting the proper boundary set based on the euclidean distance for each data set in both classes when training the network.

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Chin-Sing Ong, Wei-Kom Chu, Joseph C. Anderson, and Hon-Wei Syh "Application of neural network to liver magnetic-resonance-imaging study", Proc. SPIE 1709, Applications of Artificial Neural Networks III, (16 September 1992); https://doi.org/10.1117/12.139982

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KEYWORDS

Liver

Magnetic resonance imaging

Neural networks

Tumors

Biopsy

Artificial neural networks

Computing systems

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