Extraction of features from medical images using a modular neural network approach that relies on learning by sample

Djamel Brahmi; Camille Serruys; Nathalie Cassoux; Alain Giron; Raoul Triller; Phuc Lehoang; Bernard Fertil

doi:10.1117/12.387688

6 June 2000 Extraction of features from medical images using a modular neural network approach that relies on learning by sample

Djamel Brahmi, Camille Serruys, Nathalie Cassoux, Alain Giron, Raoul Triller, Phuc Lehoang, Bernard Fertil

Author Affiliations +

Proceedings Volume 3979, Medical Imaging 2000: Image Processing; (2000) https://doi.org/10.1117/12.387688
Event: Medical Imaging 2000, 2000, San Diego, CA, United States

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).

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Djamel Brahmi, Camille Serruys, Nathalie Cassoux, Alain Giron, Raoul Triller, Phuc Lehoang, and Bernard Fertil "Extraction of features from medical images using a modular neural network approach that relies on learning by sample", Proc. SPIE 3979, Medical Imaging 2000: Image Processing, (6 June 2000); https://doi.org/10.1117/12.387688

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