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Proceedings Paper

Automatic classification of urinary sediment images by using a hierarchical modular neural network
Author(s): Satoshi Mitsuyama; Jun Motoike; Hitoshi Matsuo
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Paper Abstract

We have developed an automated image-classification method for the examination of urinary sediment. Urine contains many kinds of particles of various colors and sizes. To classify these particles automatically, we developed a hierarchical modular neural network (HMNN) to enable accurate classification of urinary-sediment images. Simulations results showed that a neural network with a modular structure can classify artificially generated patterns more accurately than a single neural network (SNN). By using a HMNN, any kind of particle contained in urine can be automatically classified. We compared the classification accuracy when using the HMNN to that with a SNN and found that the classification accuracy for some classes of particles when using the HMNN was 25% to 30% higher than when using the SNN. With the HMNN, the examination accuracy was sufficient to allow automation of the examination process.

Paper Details

Date Published: 21 May 1999
PDF: 9 pages
Proc. SPIE 3661, Medical Imaging 1999: Image Processing, (21 May 1999); doi: 10.1117/12.348624
Show Author Affiliations
Satoshi Mitsuyama, Hitachi, Ltd. (Japan)
Jun Motoike, Hitachi, Ltd. (Japan)
Hitoshi Matsuo, Hitachi, Ltd. (Japan)


Published in SPIE Proceedings Vol. 3661:
Medical Imaging 1999: Image Processing
Kenneth M. Hanson, Editor(s)

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