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Proceedings Paper

Extraction of features from medical images using a modular neural network approach that relies on learning by sample
Author(s): Djamel Brahmi; Camille Serruys; Nathalie Cassoux; Alain Giron; Raoul Triller; Phuc Lehoang; Bernard Fertil
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Paper Abstract

Medical images provide experienced physicians with meaningful visual stimuli but their features are frequently hard to decipher. The development of a computational model to mimic physicians' expertise is a demanding task, especially if a significant and sophisticated preprocessing of images is required. Learning from well-expertised images may be a more convenient approach, inasmuch a large and representative bunch of samples is available. A four-stage approach has been designed, which combines image sub-sampling with unsupervised image coding, supervised classification and image reconstruction in order to directly extract medical expertise from raw images. The system has been applied (1) to the detection of some features related to the diagnosis of black tumors of skin (a classification issue) and (2) to the detection of virus-infected and healthy areas in retina angiography in order to locate precisely the border between them and characterize the evolution of infection. For reasonably balanced training sets, we are able to obtained about 90% correct classification of features (black tumors). Boundaries generated by our system mimic reproducibility of hand-outlines drawn by experts (segmentation of virus-infected area).

Paper Details

Date Published: 6 June 2000
PDF: 8 pages
Proc. SPIE 3979, Medical Imaging 2000: Image Processing, (6 June 2000); doi: 10.1117/12.387688
Show Author Affiliations
Djamel Brahmi, INSERM (France)
Camille Serruys, INSERM (France)
Nathalie Cassoux, Hopital de la Pitie-Salpetiere (France)
Alain Giron, INSERM (France)
Raoul Triller, Ctr. International de Dermatologie (France)
Phuc Lehoang, Hopital de la Pitie-Salpetiere (France)
Bernard Fertil, INSERM (France)


Published in SPIE Proceedings Vol. 3979:
Medical Imaging 2000: Image Processing
Kenneth M. Hanson, Editor(s)

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