Applying neural networks to ultrasonographic texture recognition

Jean-Francois Gallant; Jean Meunier; Robert Stampfler; Jocelyn Cloutier

doi:10.1117/12.152526

2 September 1993 Applying neural networks to ultrasonographic texture recognition

Jean-Francois Gallant, Jean Meunier, Robert Stampfler, Jocelyn Cloutier

Proceedings Volume 1965, Applications of Artificial Neural Networks IV; (1993) https://doi.org/10.1117/12.152526
Event: Optical Engineering and Photonics in Aerospace Sensing, 1993, Orlando, FL, United States

Abstract

A neural network was trained to classify ultrasound image samples of normal, adenomatous (benign tumor) and carcinomatous (malignant tumor) thyroid gland tissue. The samples themselves, as well as their Fourier spectrum, miscellaneous cooccurrence matrices and 'generalized' cooccurrence matrices, were successively submitted to the network, to determine if it could be trained to identify discriminating features of the texture of the image, and if not, which feature extractor would give the best results. Results indicate that the network could indeed extract some distinctive features from the textures, since it could accomplish a partial classification when trained with the samples themselves. But a significant improvement both in learning speed and performance was observed when it was trained with the generalized cooccurrence matrices of the samples.

Citation Download Citation

Jean-Francois Gallant, Jean Meunier, Robert Stampfler, and Jocelyn Cloutier "Applying neural networks to ultrasonographic texture recognition", Proc. SPIE 1965, Applications of Artificial Neural Networks IV, (2 September 1993); https://doi.org/10.1117/12.152526

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