Resumen

MORENO, J; CAICEDO, J  y  GONZALEZ, F. Una representación multi-características de imágenes basada en kernels para clasificación de imágenes de histopatología. Acta biol.Colomb. [online]. 2010, vol.15, n.3, pp.251-260. ISSN 0120-548X.

This paper presents a novel strategy for building a high-dimensional feature space to represent histopathology image contents. Histogram features, related to colors, textures and edges, are combined together in a unique image representation space using kernel functions. This feature space is further enhanced by the application of Latent Semantic Analysis, to model hidden relationships among visual patterns. All that information is included in the new image representation space. Then, Support Vector Machine classifiers are used to assign semantic labels to images. Processing and classification algorithms operate on top of kernel functions, so that, the structure of the feature space is completely controlled using similarity measures and a dual representation. The proposed approach has shown a successful performance in a classification task using a dataset with 1,502 real histopathology images in 18 different classes. The results show that our approach for histological image classification obtains an improved average performance of 20.6% when compared to a conventional classification approach based on SVM directly applied to the original kernel.

Palabras clave : Automatic image annotation.

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