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9 March 1999 Correlation between variability of hand-outlined segmentation drawn by experts and local features of underlying image: a neuronal approach
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Abstract
Detection of contours in biomedical imags is quite often an a priori step to quantification. Using computer facilities, it is now straightforward for a medical expert to draw boundaries around regions of interest. However, accuracy of drawing is an issue, which is rarely addressed although it may be a crucial point when for example one looks for local evolution of boundaries on a series of images. The aim of our study is to correlate the local accuracy of experts' outlines with local features of the underlying image to allow meaningful comparisons of boundaries. Local variability of experts' outlines has been characterized by deriving a set of distances between outlines repeatedly drawn on the same image. Local features of underlying images were extracted from 64 by 64 pixel windows. We have used a two-stage neural network approach in order to deal with complexity of data within windows and to correlate their features with local variability of outlines. Our method has been applied to the quantification of the progression of the Cytomegalovirus infection as observed from a series of retinal angiograms in patients with AIDS. Reconstruction of new windows from the set of primitives obtained from the GHA network shows that the method preserves desired features. Accuracy of the border of infection is properly predicted and allows to generate confidence envelope around every hand-outlined.
© (1999) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
Djamel Brahmi, Nathalie Cassoux, Camille Serruys, Alain Giron, Phuc Lehoang, and Bernard Fertil "Correlation between variability of hand-outlined segmentation drawn by experts and local features of underlying image: a neuronal approach", Proc. SPIE 3647, Applications of Artificial Neural Networks in Image Processing IV, (9 March 1999); https://doi.org/10.1117/12.341117
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